INFORMS Annual Meeting - San Diego

Sunday Oct 11 Delete 08:00 - 09:30 : Stochastic Control and Optimization Chair:  Yang Wang,Stanford University, Packard Electrical Engineering, 350 Serra Mall, Stanford CA 94305, United States of America, yw224@stanford.edu Title:  Singular Stochastic Control and Composite Markov Processes   Lead:  Xiren Cao,Professor, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Hong Kong - PRC, eecao@ust.edu.hk   Abstract:  We propose a composite model for Markov processes. The state space of a composite Markov process consists of two parts, J and J-. When the process is in J-, it evolves like a continuous-time Levy process; and once the process enters J, it makes a jump instantly like a discrete-time Markov chain. The composite Markov process provides a new model for singular stochastic control problem, and we show that this problem can be solved using a direct-comparison method.     Title:  Computationally Tractable Performance Bounds for Constrained Linear Stochastic Control   Lead:  Yang Wang,Stanford University, Packard Electrical Engineering, 350 Serra Mall, Stanford CA 94305, United States of America, yw224@stanford.edu   Abstract:  We present computational lower bounds on performance for constrained linear stochastic control. Our method involves solving a semidefinite program, a convex optimization problem which we can solve efficiently. Numerical experiments show that the lower bound obtained by our method is often close to the performance achieved by suboptimal control policies. As a by-product, our bound yields approximate value functions that can be used as control Lyapunov functions for suboptimal control methods.     Title:  Structural Properties of the Value of Information in Single-Stage Ranking and Selection   Lead:  Peter Frazier,Assistant Professor, Cornell University, Rhodes Hall, Ithaca NY 14853, United States of America, pf98@cornell.edu   Abstract:  Within the Bayesian ranking and selection problem with independent normal priors and independent normal sampling noise, we consider the expected value of information obtained from a non-sequential sampling allocation. This quantity is known to be a non-concave function of the sampling allocation, which makes finding the optimal allocation difficult. Nevertheless, this function possesses a number of structural properties. We discuss these properties and their application.     Title:  Optimal Learning On A Graph   Lead:  Ilya Ryzhov,Princeton University, Princeton NJ 08540, United States of America, iryzhov@princeton.edu   Co-Author:  Warren Powell,Professor, Princeton University, Sherrerd Hall 230, Princeton NJ 08544, United States of America, powell@princeton.edu   Abstract:  Consider a stochastic path-finding problem on a graph where the distributions of the arc costs are unknown. By sequentially measuring individual edges, we can refine Bayesian estimates of the arc costs. Our goal is to allocate measurements in order to efficiently learn about the optimal solution to the path-finding problem. We propose a knowledge gradient policy that is easy to compute, with certain desirable theoretical properties, and performs competitively against other learning strategies.     Delete 08:00 - 09:30 : Computational Optimization Methods and Applications Chair:  Jiawang Nie,UCSD, 9500 Gilman Drive, La Jolla\, United States of America, njw@math.ucsd.edu Title:  An Affine-scaling Interior-point Method for Continuous Knapsack Constraints   Lead:  Hongchao Zhang,Department of Mathematics, Louisiana State University, Baton Rouge LA, hozhang@math.lsu.edu   Abstract:  A gradient based affine-scaling algorithm for continuous knapsack constraints will be presented. This algorithm has the property that each iterates lie in the interior of the feasible set and is more suitable for large dimensional optimization problems where the Hessian of the objective function is a large, dense, and possibly ill-conditioned matrix. Global and local linear convergence of the algorithm will be discussed. Numerical results will be also reported.     Title:  Solving Graph Bisection Minimization Problems using Convex Quadratic Relaxations   Lead:  Dzung Phan,University of Florida, 358 Little Hall, P.O. Box 118105, Gainesville FL 32611-8105, United States of America, dphan@ufl.edu   Co-Author:  William Hager,University of Florida, 358 Little Hall, P.O. Box 118105, Gainesville FL 32611-8105, United States of America, hager@math.ufl.edu   Abstract:  We present an exact algorithm for solving the node and edge weighted graph partitioning problem. The algorithm is based on a continuous quadratic formulation of the problem. Necessary and sufficient optimality conditions for a local minimizer of the quadratic program are introduced. Lower bounds for the branch and bound algorithm are obtained by replacing the concave part of the objective function by the best affine underestimator. Numerical results show the effectiveness of our method.     Title:  A Convergence Theorem for Solving the SQP System in SSM   Lead:  Ning Guo,Student, University of Florida, Gainesville FL, guoning@ufl.edu   Co-Author:  William Hager,Professor, University of Florida, Gainesville FL, hager@ufl.edu   Abstract:  SSM(sequential subspace method) is an iterative method used to solve the trust region subproblem. A convergence theorem is developed concerning how precisely we should solve the SQP(sequential quadratic programming) system in each iteration in order to obtain an at least linear convergence for the non-degenerate case. We shall look at some numerical results.     Delete 08:00 - 09:30 : Interior-point Methods Chair:  Samir Elhedhli,University of Waterloo, 200 University Avenue West, Waterloo ON N2L 3G1, Canada, elhedhli@uwaterloo.ca Title:  Bi-parametric Convex Quadratic Optimization   Lead:  Tamas Terlaky,Professor, Chair, Lehigh University, Department ISE, H. Mohler Lab,, 200 West Packer Ave, Bethlehem PA 18015, United States of America, terlaky@lehigh.edu   Co-Author:  Alireza Ghafari Hadigheh,Professor, Department of Mathematics, Azarbaijan University of Tarbiat Moallem, Tabriz, Iran, hadigheha@azaruniv.edu   Oleksandr Romanko,PhD Candidate, McMaster University, 1280 Main Street West, Hamilton ON L8S4K1, Canada, romanko@mcmaster.ca   Abstract:  We consider the Convex Quadratic Optimization problem with simultaneous perturbation in the right-hand-side of the constraints and the linear term of the objective function with different parameters. The regions with invariant optimal partitions are investigated as well as the behavior of the optimal value function on the regions. Identifying these regions can be done in polynomial time in the output size. An algorithm for identifying all invariancy regions is presented.     Title:  Interior-Point Methods for Integer Programming: The ACCPM Approach   Lead:  Samir Elhedhli,University of Waterloo, 200 University Avenue West, Waterloo ON N2L 3G1, Canada, elhedhli@uwaterloo.ca   Abstract:  This talk highlights the use of interior point methods in solving integer programming through the Analytic Centre Cutting Plane Method (ACCPM). Heuristic, Branch-and-price, branch-and-cut, and Benders decomposition algorithms will be presented that compete with LP-based approaches.     Title:  An L1 Elastic Interior-Point Method for MPCCs   Lead:  Zoumana Coulibaly,Phd Student, Ecole Polytechnique de Montreal and GERAD, 2500, chemin de Polytechnique, Montreal Qc h2e2g5, Canada, zoumana.coulibaly@polymtl.ca   Abstract:  Our interior-point algorithm consist of an elastic formulation of the L1-penalty merit function for mathematical programs with complementarity constraints. The method naturally converges, at a sub-quadratic local convergence rate, to a strongly stationary point or delivers a certificate of degeneracy without recourse to second-order intermediate solutions. Numerical results on a standard test set illustrate the efficiency and robustness of the approach.     Title:  Solving Unconstrained Nonlinear Programs with ACCPM   Lead:  Ahad Dehghani,McGill University, 1001 Sherbrooke west, Montreal H3A 1G5, Canada, ahad.dehghani@mcgill.ca   Co-Author:  Jean-louis Goffin,McGill University, 1001 Sherbrooke west, Montreal H3A 1G5, Canada, jean-louis.goffin@mcgill.ca   Dominique Orban,Ecole Polytechnique de Montréa, Mathematics and Industrial Engineering D, Montreal Qc, Canada, dominique.orban@gerad.ca   Abstract:  ACCPM and proximal ACCPM are well known techniques for convex programming problems. We propose a sequential convex programming method based on ACCPM and convexification techniques to tackle unconstrained problems with a non-convex objective function.     Delete 11:00 - 12:30 : Gradient Algorithms for Large Scale Convex Optimization Chair:  Peter Richtarik,University of Edinburgh, James Clerk Maxwell Building, The King's Buildings, Edinburgh EH9 3JZ, United Kingdom, peter.richtarik@uclouvain.be Title:  Primal-Dual Gradient Methods for Structured Convex Problems   Lead:  Peter Richtarik,University of Edinburgh, James Clerk Maxwell Building, The King's Buildings, Edinburgh EH9 3JZ, United Kingdom, peter.richtarik@uclouvain.be   Co-Author:  Yurii Nesterov,Catholic University of Louvain, CORE/INMA, 34 Voie du Roman Pays, Louvain-la-Neuve 1348, Belgium, yurii.nesterov@uclouvain.be   Abstract:  In this work we develop and analyze an algorithm for minimizing a linear function over a simple convex compact set intersected with an affine subspace. We construct a primal-dual convex reformulation of the original problem with optimal value zero and analyze new gradient methods for minimizing convex functions with known optimal value. A special case of our algorithm is the subgradient method at one extreme and the level method at the other.     Title:  Approximate and Restarted Estimate Sequences Schemes   Lead:  Michel Baes,ETH, IFOR, Rämistrasse101, Zurich 8092, Switzerland, michel.baes@ifor.math.ethz.ch   Abstract:  Estimate sequences are provably the fastest gradient method for smooth convex problems. At every iteration, one must compute the objective gradient and solve two optimization problems. We determine how accurately those computations have to be done to avoid error propagations. Several variants of estimate sequence schemes will also be analyzed, including different restarting strategies.     Title:  AdaBoost - Nothing Else Than a Mirror Descent Algorithm?   Lead:  Michael Buergisser,ETH, IFOR, Rämistrasse101, Zurich 8092, Switzerland, michael.buergisser@ifor.math.ethz.ch   Co-Author:  Michel Baes,ETH, IFOR, Rämistrasse101, Zurich 8092, Switzerland, michel.baes@ifor.math.ethz.ch   Abstract:  Freund and Schapire introduced AdaBoost, a standard scheme in Machine Learning, in the late nineties. Mirror descent methods are due to Nemirovski and Yudin and belong to the class of subgradient schemes. We show that AdaBoost coincides with a well-chosen mirror descent algorithm. For this, we define an appropriate objective function and interpet AdaBoost's weak learner as an oracle providing approximate stochastic subgradients.     Delete 13:30 - 15:00 : Some Advances in Conic Optimization Chair:  Mohammad Oskoorouchi,College of Business Administration, California State University San Marcos, 333 S. Twin Oaks Valley Road, San Marcos CA 92096, United States of America, moskooro@csusm.edu Title:  An Interior Point Constraint Generation Method for Semi-infinite Linear Programming   Lead:  Mohammad Oskoorouchi,College of Business Administration, California State University San Marcos, 333 S. Twin Oaks Valley Road, San Marcos CA 92096, United States of America, moskooro@csusm.edu   Co-Author:  Hamid Ghaffari,University of Toronto, 5 King's Collage Rd., Toronto, Canada, h.ghaffari@utoronto.ca   Tamas Terlaky,Professor, Chair, Lehigh University, Department ISE, H. Mohler Lab,, 200 West Packer Ave, Bethlehem PA 18015, United States of America, terlaky@lehigh.edu   Abstract:  We present an interior point constraint generation algorithm for semi-infinite linear optimization and prove that the algorithm converges to an $\varepsilon$-solution after a finite number of constraints is generated. We implement our algorithm to solve second-order cone optimization (SOCO) problems and compare our numerical results with that of SeDuMi and show that our method outperforms classical primal-dual interior point methods on a class of large-scale SOCO problems.     Title:  Full-Newton Step Polynomial-time Methods for LO Based on Locally Self-concordant Barrier Functions   Lead:  Kees Roos,TU Delft, Mekelweg 4, Delft, Netherlands, C.Roos@tudelft.nl   Co-Author:  G. Lesaja,College of Science and Technology, Georgia Southern University, United States of America, Goran@GeorgiaSouthern.edu   H. Mansouri,Department of Mathematical Science, Shahrekord University, Iran, HMansory@yahoo.com   Abstract:  Recently, several interior-point methods were introduced by using barrier functions that are based on kernel functions. These barrier functions are not self-concordant. Near the central path, however, they behave as being self-concordant. Using this we prove that for full-Newton step variants of these methods the iteration bound is the currently best known bound for LO.     Title:  Relationships Among Some Geometric Measures of Convex Cones   Lead:  Levent Tuncel,Professor, University of Waterloo, Dept. Combinatorics and Optim., 200 Unversity Avenue, West, Waterloo ON N2L3G1, Canada, ltuncel@math.uwaterloo.ca   Co-Author:  Hugo Lara,Universidad CLA, Apdo 400., Barquisimeto 3001, Venezuela, hugol@ucla.edu.ve   Abstract:  We study geometric measures of convex cones and present various relationships among the width, the norm approximation coefficient, Caratheodory number and Goffin-Cheung-Cucker measure of convex cones.     Title:  An Active Set Algorithm for Semidefinite Programming   Lead:  Kartik Sivaramakrishnan,Senior Research Associate, Axioma, Inc., 8800 Roswell Road, Building B, Suite 295, Atlanta GA 30350, United States of America, kksivara@gmail.com   Co-Author:  John Mitchell,Professor, Rensselaer Polytechnic Institute, Department of Mathematical Sciences, 110 Eighth Street, Troy NY 12180, United States of America, mitchj@rpi.edu   Abstract:  We develop an active set framework for solving approximately solving large scale semidefinite programming problems (SDPs) as a sequence of smaller semidefinite programs in every iteration. We will highlight the features of our algorithm and also present some of our preliminary computational results.     Delete 13:30 - 15:00 : ICS Prize Winners Chair:  S. Raghavan,Associate Professor, University of Maryland, University of Maryland, College Park MD, United States of America, raghavan@umd.edu Title:  An Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming   Lead:  Andreas Wächter,PhD, IBM T.J. Watson Research Center, Department of Mathematical Sciences, P.O. Box 218, Yorktown Heights NY 10598, andreasw@watson.ibm.com   Co-Author:  Lorenz Biegler,Professor, Carnegie Mellon University, Department of Chemical Engineering, 5000 Forbes Avenue, Pittsburgh PA 15213, United States of America, lb01@andrew.cmu.edu   Abstract:  We present an algorithm for large-scale nonlinear continuous optimization, together with real-life applications, such as the tuning of transistors in digital circuits, modeling and design of chemical processes and optimal control of nonlinear dynamic systems. We will also present some recent developments of the algorithm, including parametric sensitivity of NLP solutions and the use of iterative linear solvers. An implementation of this algorithm ("Ipopt") is available as open source.     Title:  A Line Search Multigrid Method for Large-Scale Nonlinear Optimization (Winning Paper)   Lead:  Zaiwen Wen,NSF Math Institutes’ postdoc, IPAM, University of California, Los Angeles and Rice University, United States of America, zw2109@columbia.edu   Co-Author:  Donald Goldfarb,Professor, Columbia University, New York, New York, United States of America, goldfarb@columbia.edu   Abstract:  We present a line search multigrid method for solving discretized versions of general unconstrained infinite dimensional optimization problems. At each iteration on each level, the algorithm computes either a “direct search” direction on the current level or a “recursive search” direction from coarser level models. Introducing a new condition that must be satisfied by a backtracking line search procedure, the “recursive search” direction is guaranteed to be a descent direction. Global convergence is proved under fairly minimal requirements on the minimization method used at all grid levels. Using a limited memory BFGS quasi- Newton method to produce the “direct search” direction, preliminary numerical experiments show that our line search multigrid approach is promising.     Title:  The Knowledge-Gradient Policy for Correlated Normal Beliefs (Honorary Mention)   Lead:  Peter Frazier,Assistant Professor, Cornell University, Rhodes Hall, Ithaca NY 14853, United States of America, pf98@cornell.edu   Co-Author:  Savas Dayanik,Department of Operations Research and Financial Engineering, Princeton University, Princeton NJ 08544, sdayanik@princeton.edug   Warren Powell,Professor, Princeton University, Sherrerd Hall 230, Princeton NJ 08544, United States of America, powell@princeton.edu   Abstract:  We consider a Bayesian ranking and selection problem with independent normal rewards and a correlated multivariate normal belief on the mean values of these rewards. Because this formulation of the ranking and selection problem models dependence between alternatives' mean values, algorithms may utilize this dependence to perform effciently even when the number of alternatives is very large. We propose a fully sequential sampling policy called the knowledge-gradient policy, which is provably optimal in some special cases and has bounded suboptimality in all others. We then demonstrate how this policy may be applied to effciently maximize a continuous function on a continuous domain while constrained to a xed number of noisy measurements     Title:  Appointment Scheduling with Discrete Random Durations (Honorary Mention)   Lead:  Mehmet Begen,Richard Ivey School of Business, University of Western Ontario, 1151 Richmond St. N., London ON N6A 3K7, Canada, mbegen@ivey.uwo.ca   Co-Author:  Maurice Queyranne,Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver BC, Canada, maurice.queyranne@sauder.ubc.ca   Abstract:  We consider the problem of determining an optimal appointment schedule for a given sequence of jobs (e.g., medical procedures) on a single processor (e.g., operating room, exami- nation facility, physician), to minimize the expected total underage and overage costs when each job has a random processing duration given by a joint discrete probability distribution. Simple conditions on the cost rates imply that the objective function is submodular and L-convex. Then there exists an optimal appointment schedule which is integer and can be found in polynomial time. Our model can handle a given due date for the total processing (e.g., end of day for an operating room) after which overtime is incurred, and no-shows and some emergencies.     Title:  ICS Prize Session   Abstract:  The winners of the 2009 ICS Prize and the 2009 ICS Student Paper Award present their award-winning work.     Delete 13:30 - 15:00 : Convex Hull Approximations for Integer and Global Optimization Chair:  Suvrajeet Sen,Professor, The Ohio State University, 1971 Neil Ave, Columbus OH 43210, United States of America, sen.22@osu.edu Title:  On Polytopes Associated with Products of Discrete Variables   Lead:  Warren Adams,Professor, Clemson University, Martin Hall, Clemson SC 29634, United States of America, wadams@clemson.edu   Co-Author:  Stephen M Henry,Department of Mathematical Sciences, Clemson University, Clemson SC, smhenry@clemson.edu   Abstract:  Linearizations of quadratic binary terms have been well-studied. However, little is known for the discrete case. We characterize desirable extreme point traits of discrete polytopes, and establish fundamental results relative to dimension and facial structure. Convex hull representations are obtained for special instances, and partial such results in general.     Title:  Exploiting Multilinearity in Global Optimization Relaxations   Lead:  Mohit Tawarmalani,Associate Professor, Krannert Graduate School of Management of Purdue University, 4026, RAWLS Hall, 100, S. Grant, West lafayette IN 47907, United States of America, mtawarma@purdue.edu   Co-Author:  Xiaowei Bao,University of Illinois at Urbana Champaign, xbao2@uiuc.edu   Nick Sahinidis,John E. Swearingen Professor, Carnegie Mellon University, Department of Chemical Engineering, Pittsburgh, United States of America, sahinidis@cmu.edu   Abstract:  We use the convex extensions theory to derive a column-generation algorithm for generating facets of the envelopes of a multilinear function. We provide preliminary computational experience on a variety of global optimization problems. We compare the strength of various commonly used relaxations for the multilinear expressions, discuss generalizations to multilinear sets.     Title:  Finite Disjunctive Programming Characterizations for General Mixed-Integer Linear Programs   Lead:  Binyuan Chen,PhD Students, University of Arizona, SIE Department, Tucson AZ 85721, United States of America, bychen@email.arizona.edu   Co-Author:  Simge Kucukyavuz,Ohio State University, Columbus OH 43202, United States of America, kucukyavuz.2@osu.edu   Suvrajeet Sen,Professor, The Ohio State University, 1971 Neil Ave, Columbus OH 43210, United States of America, sen.22@osu.edu   Abstract:  We give a finite disjunctive programming procedure to obtain the convex hull of bounded general mixed-integer linear programs. We propose a sequential disjunctive cutting plane algorithm that converges to an integral optimal solution in finitely many iterations. We illustrate the proposed algorithm on three well-known examples in the literature.     Delete 16:30 - 18:00 : Complementarity, Conality, and SDP - Algorithms and Applications Chair:  Robert Vanderbei,Professor, Princeton University, Sherrerd Hall, Room 106, Princeton NJ 08544, United States of America, rvdb@princeton.EDU Title:  A Disjunctive Programming Approach to LPCC   Lead:  Bin Yu,Graduate student, Rensselaer Polytechnic Institute, CII5015 Rensselaer Polytechnic Institute, 110 8th St., Troy NY 12180, United States of America, yub@rpi.edu   Co-Author:  John Mitchell,Professor, Rensselaer Polytechnic Institute, Department of Mathematical Sciences, 110 Eighth Street, Troy NY 12180, United States of America, mitchj@rpi.edu   Abstract:  A linear program with complementarity constraints (LPCC) can be modeled as a disjunctive program. By imposing one pair of disjunctive constraints at a time, we are able to use a cut generating LP to generate a disjunctive cut for the LPCC problem. We present a branch and cut algorithm to globally solve the LPCC problem. The algorithm is able to characterize infeasible and unbounded LPCC problems as well as solve problems with finite optimal value.     Title:  Optimization over the Doubly Non-Negative Cone   Lead:  Akiko Yoshise,Professor, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Ibaraki, Japan, yoshise@sk.tsukuba.ac.jp   Co-Author:  Yasukaki Matsukawa,University of Tsukuba, Tsukuba, Ibaraki 305-8573, Ibaraki, Japan, matsuk50@sk.tsukuba.ac.jp   Abstract:  While many studies on semidefinite relaxation for combinatorial optimization problems force the solution matrix to be symmetric and positive semidefinite, we often see that it is meant to be non-negative. We call the set of such matrices the doubly non-negative cone. Aiming to develop new algorithms for solving optimization problems over the cone, we provide some basic properties of the doubly non-negative cone focusing on barrier functions on its interior and its duality.     Title:  Solving an Image Deconvolution Problem   Lead:  Robert Vanderbei,Professor, Princeton University, Sherrerd Hall, Room 106, Princeton NJ 08544, United States of America, rvdb@princeton.EDU   Abstract:  A recent paper by A. Leshem shows how high resolution imaging in radio astronomy can be formulated as a block 2x2 semidefinite programming problem. In this talk, I will describe the problem and show how it can be solved efficiently as a convex optimization problem.     Title:  Interior-Point Methods for MPECs   Lead:  Hande Benson,Assistant Professor, Drexel University, 3141 Chestnut St, Philadelphia PA 19104, United States of America, hvb22@drexel.edu   Co-Author:  Arun Sen,sen@post.harvard.edu   David Shanno,RUTCOR, Rutgers University, New Brunswick, United States of America, shannod@comcast.net   Abstract:  Penalty approaches for interior-point methods have been favored to handle the unbounded multipliers when solving mathematical programs with equilibrium constraints (MPECs). Even without this remedy, interior-point methods are quite successful at solving many MPECs in practice. In this talk, we will present a globally convergent interior-point method which uses a penalty approach and analyze its numerical performance on MPECs. Comparisons to non-penalty interior-point methods will be provided.     Delete 16:30 - 18:00 : Matrix Rank Minimization: Theory and Algorithms Chair:  Maryam Fazel,Assistant professor, University of Washington, Campus Box 352500, Univ of Washington, Seattle WA 98195, United States of America, mfazel@u.washington.edu Title:  Matrix Completion from A Few Entries   Lead:  Andrea Montanari,Professor, Standord University, Packard Bldg, Stanford CA 94025, United States of America, montanar@stanford.edu   Co-Author:  Raghunandan Keshavan,Stanford University, Stanford, United States of America, raghuram@stanford.edu   Sewoong Oh,Stanford University, Stanford, United States of America, swoh@stanford.edu   Abstract:  We consider the use of low-rank models in collaborative filtering (the `Netflix problem'). Given M, a n x n `incoherent' matrix of rank r, a random subset of its entries is observed. We describe an efficient algorithm that reconstructs M from O(rn) entries with arbitrarily small RMSE. If r = O(1), it reconstructs M exactly from O(n log n) entries. The algorithm is robust with respect to noise. In the case of gaussian noise, it appears to surpass approaches based on nuclear norm relaxation.     Title:  Exact Matrix Completion via Convex Optimization   Lead:  Emmanuel Candes,CalTech, emmanuel@acm.caltech.edu   Co-Author:  Terence Tao,University of California at Los Angeles, MC 1555, Los Angeles CA 90095, United States of America, tao@math.ucla.edu   Abstract:  We observe a few entries from a matrix and ask whether we can complete the matrix and recover the entries we have not seen. This is the famous Netflix problem. We show that surprisingly one can recover low-rank matrices exactly from what appear to be highly incomplete sets of entries. Further, perfect recovery is possible by solving a semidefinite program. Our methods are optimal and succeed as soon as recovery is possible by any method whatsoever.     Title:  Fixed Point and Bregman Iterative Methods for Matrix Rank Minimization   Lead:  Shiqian Ma,Columbia University, 500 W. 120TH ST, Mudd Building, RM 313, New York NY 10027, United States of America, sm2756@columbia.edu   Co-Author:  Donald Goldfarb,Professor, Columbia University, New York, New York, United States of America, goldfarb@columbia.edu   Abstract:  We consider in this talk the linearly constrained matrix rank minimization problem. We propose fixed point and Bregman iterative algorithms for solving the nuclear norm minimization problem, which is the tightest convex relaxation of this problem. Our algorithm can recover 1000 by 1000 matrices of rank 50 with a relative error of 1e-5 in about 3 minutes sampling only 20 percent of the elements. We know of no other method that achieves as good recoverability.     Title:  Results on Low-rank Matrix Recovery from Noisy Data   Lead:  Maryam Fazel,Assistant professor, University of Washington, Campus Box 352500, Univ of Washington, Seattle WA 98195, United States of America, mfazel@u.washington.edu   Abstract:  We consider the problem of recovering a low-rank matrix from various classes of limited and noisy observations. We show that minimizing the nuclear norm of the matrix (i.e., the sum of its singular values) subject to the observations provides not only a provably good approximation of the unknown matrix, but also a way to estimate the correct rank and the singular spaces of the matrix.     Monday Oct 12 Delete 08:00 - 09:30 : Advanced Portfolio Theory and Practice Chair:  John Mulvey,Professor, Princeton University, Sherrerd Hall, Princeton NJ 08540, United States of America, mulvey@princeton.edu Title:  Portfolio Theory versus Financial Engineering and Their Roles in Financial Crises   Lead:  Harry Markowitz,harryhmm@aol.com   Abstract:      Title:  Replicating Private Equity Returns with Exchange Traded Funds   Lead:  John Mulvey,Professor, Princeton University, Sherrerd Hall, Princeton NJ 08540, United States of America, mulvey@princeton.edu   Abstract:      Title:  Value-at-Risk vs. Conditional Value-at-Risk in Risk Management and Optimization   Lead:  Stan Uryasev,University of Florida, Dept. of Industrial and Systems Engineer, 303 Weil Hall, P.O. Box 116595, Gainesville FL 32611-6595, United States of America, uryasev@ufl.edu   Abstract:  From the mathematical perspective, risk management is a procedure for shaping a risk distribution. Two popular risk measures are Value at-Risk (VaR) and Conditional Value-at-Risk (CVaR). This paper presents our personal experience working with these key percentile risk measures. We explain strong and weak features of these risk measures. We discuss related case studies conducted with the Portfolio Safeguard package.     Title:  Managing a Fixed-income Portfolio   Lead:  Roger Wets,Professor, University of California, Davis, EpiRisk Research, CA, United States of America, rjbwets@epirisk.com   Abstract:  As stressed by Markowitz in the early 1950s and many others since, investors can't ignore risk. This has led to widely implemented mean-variance models. This lecture describes an alternative approach relying on i) a more comprehensive description of uncertainty that takes into account historical and market (term structures) information and, ii) on an approximate assessment of the manager's risk aversion whose parameters can be (easily)adjusted based on the(projected) distribution of returns.     Delete 08:00 - 09:30 : Optimization in Practice I - Algorithms Co-Chair:  Robert Vanderbei,Professor, Princeton University, Sherrerd Hall, Room 106, Princeton NJ 08544, United States of America, rvdb@princeton.EDU Chair:  Ernest Forman,Dr., Department of Decision Science, School of Business, George Washington University, Washington DC 20052, United States of America, forman@gwu.edu Bjarni Kristjansson,President, Maximal Software, Inc., 2111 Wilson Boulevard, Suite 700, Arlington VA 22201, United States of America, bjarni@maximalsoftware.com Title:  Solving Practical Problems with Hybrid Optimization Approaches   Lead:  Genetha Gray,gagray@sandia.gov   Co-Author:  Katie Fowler,Clarkson University, 8 Clarkson Avenue, Potsdam NY 13699, United States of America, kfowler@clarkson.edu   Joshua Griffin,SAS Institute Inc, 100 SAS Campus Drive, Cary NC 27513, United States of America, joshua.griffin@sas.com   Abstract:  In this talk, we will review the EAGLS (Evolutionary Algorithm Guiding Local Search) software which allows for the hybridization of one or more optimization methods. We will focus on the design characteristics of EAGLS which make it particularly applicable for solving mixed integer problems that arise in hydrological system design.     Title:  Determining an Optimal Portfolio of Projects: Project Portfolio Management   Lead:  Ernest Forman,Dr., Department of Decision Science, School of Business, George Washington University, Washington DC 20052, United States of America, forman@gwu.edu   Co-Author:  Raj Kanungo,Associate Professor, George Washington University, Department of Decision Sciences, Washington DC 20052, United States of America, kanungo@gwu.edu   William Taylor,Chief, Operations Analysis, US Bureau of Citizenship and Immigration Services, US Bureau of Citizenship and Immigration, Department of Homeland Security, Washington DC, United States of America, William.R.Taylor@dhs.gov   Abstract:  Determining an optimal portfolio of projects involves two phases: prioritizing -- developing ratio scale measures of anticipated benefits with respect to an organizations hierarchy of objectives, and determining a combination of projects that maximizes the anticipated benefits subject to dependencies and resource constraints. Three types of risk must also be addressed: business risk, project risk, and portfolio risk.     Title:  Combinatorial Optimization Algorithms for Fault Diagnosis in Complex Systems   Lead:  Krishna Pattipati,krishna@engr.uconn.edu   Abstract:  In this talk, we will first discuss a unified methodology for intelligent diagnostics that judiciously combines hybrid model-based/data-driven/knowledge-based techniques that seamlessly employs quantitative models, machine learning techniques and graph-based dependency models to address real-time diagnosis, test sequencing (NP-hard optimization problems), and remaining life predictions (solution of partial differential equations) as well as the associated maintenance optimization problems.     Delete 08:00 - 09:30 : Portfolio Optimization Chair:  Victor DeMiguel,Associate Professor, London Business School, Regents Park, London NW14SA, United Kingdom, avmiguel@london.edu Title:  A Pathwise Algorithm for Covariance Selection   Lead:  Alexandre d'Aspremont,Princeton University, Dept of Operations Res & Fin Eng, Princeton University, Princeton NJ 08544, United States of America, aspremon@princeton.edu   Co-Author:  VIjay Krishnamurthy,Princeton University, Sherrerd Hall, Princeton, United States of America, kvijay@nospamfrominforms.org   Abstract:  Covariance selection seeks to estimate a covariance matrix by maximum likelihood while restricting the number of nonzero inverse covariance matrix coefficients. A single penalty parameter usually controls the tradeoff between log likelihood and sparsity in the inverse matrix. We describe an efficient algorithm for computing a full regularization path of solutions to this problem.     Title:  Efficient Estimation of Large Dynamic Multivariate Volatility Models for Portfolio Optimization   Lead:  Francisco J. Nogales,Associate Professor, Universidad Carlos III de Madrid, Dept. of Statistics, Madrid, Spain, FcoJavier.Nogales@uc3m.es   Abstract:  In large portfolio selection problems, the estimation of dynamic multivariate volatility models (maximization of a nonlinear likelihood function subject to a constraint ensuring the positiveness of all the conditional covariance matrices) becomes unstable. This is mainly due to the need of inverting all the conditional matrices at each iteration. We propose new multivariate volatility specifications and compare the resulting portfolio performance with that of several recent proposals.     Title:  Exploiting Asset Return Predictability for Portfolio Selection   Lead:  Victor DeMiguel,Associate Professor, London Business School, Regents Park, London NW14SA, United Kingdom, avmiguel@london.edu   Co-Author:  Francisco J. Nogales,Associate Professor, Universidad Carlos III de Madrid, Dept. of Statistics, Madrid, Spain, FcoJavier.Nogales@uc3m.es   Abstract:  There is extensive evidence in the Finance literature about the predictability of asset returns. We explore the use of parametric and nonparametric statistical methods to exploit asset return predictability for portfolio selection, and show that substantial certainty equivalent gains can be achieved.     Delete 11:00 - 12:30 : Computational Stochastic Integer Programming Chair:  David Morton,Professor, The University of Texas at Austin, Graduate Program in Operations Research, 1 University Station C2200, Austin TX 78712, United States of America, morton@mail.utexas.edu Title:  Nuclear Medicine Patient and Resource Scheduling via Stochastic Integer Programming   Lead:  Eduardo Perez,Graduate Student, Texas A & M University, 3131 TAMU, College Station TX, United States of America, eduardopr@neo.tamu.edu   Co-Author:  Cesar O. Malave,Professor, Texas A & M University, 3131 TAMU, College Station TX 77843, United States of America, malave@tamu.edu   Lewis Ntaimo,Assistant Professor, Texas A&M University, 3131 TAMU, College Station TX 77843, United States of America, ntaimo@tamu.edu   Abstract:  Nuclear medicine (NM) procedures use radioisotopes for diagnosis and treatment of patients, involve multiple steps, and have to be performed under strict time constraints and uncertainty in patient/radioisotope arrivals. This makes the problem of NM patient service management very challenging. We present stochastic integer programming models for NM patient/resource scheduling within a stochastic online optimization framework. A computational study based on an actual NM clinic will be presented.     Title:  Parallelization of Progressive Hedging Revisited: Asynchronous Convergence and Scenario Bundling   Lead:  David Woodruff,Professor, University of California Davis, UC Davis, Davis, CA 95616, Davis CA, United States of America, dlwoodruff@ucdavis.edu   Co-Author:  Jean-Paul Watson,Principal Member of Technical Staff, Sandia National Laboratories, P.O. Box 5800, MS 1318, Albuquerque NM 87185-1318, United States of America, jpwatson@sandia.gov   Abstract:  Although Progressive Hedging is naturally parallelizable, a number of unexplored issues arise for efficient implementation. In particular, asynchronous sub-problem solves facilitate improved efficiency, but may adversely impact algorithm convergence. Similarly, intelligent bundling of scenarios may accelerate local convergence, but degrade global convergence. We explore the practical implementation of these techniques, and discuss computational results on various test problems.     Title:  Prioritization via Stochastic Optimization   Lead:  Ali Koc,The University of Texas at Austin, alikoc@mail.utexas.edu   Co-Author:  David Morton,Professor, The University of Texas at Austin, Graduate Program in Operations Research, 1 University Station C2200, Austin TX 78712, United States of America, morton@mail.utexas.edu   Elmira Popova,The University of Texas at Austin, 1 University Station, C2200, Austin TX 78712, United States of America, elmira@mail.utexas.edu   Abstract:  The operations research literature handles activity selection problems by forming an optimal portfolio of activities, as opposed to a common approach in industry which forms a prioritized list. We develop a novel prioritization approach incorporating both views. We illustrate our approach on stochastic k-median and capital budgeting models. We formulate two-stage and multi-stage stochastic integer programs and develop valid inequalities. We use parallel branch-cut-price to improve solution time.     Delete 13:30 - 15:00 : New Trends in Optimization Chair:  Angelia Nedich,Assistant Professor, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 South Mathews Avenue, Urbana IL 61801, United States of America, angelia@illinois.edu Title:  Testing the Nullspace Property using Semidefinite Programming   Lead:  Alexandre d'Aspremont,Princeton University, Dept of Operations Res & Fin Eng, Princeton University, Princeton NJ 08544, United States of America, aspremon@princeton.edu   Co-Author:  Laurent El Ghaoui,U.C. Berkeley, Somewhere, Berkeley, United States of America, elghaoui@nospamforhim.com   Abstract:  Recent results in compressed sensing show that, under certain conditions, the sparsest solution to an underdetermined set of linear equations can be recovered by solving a linear program. Given a matrix $A$, we use semidefinite relaxation techniques to test the nullspace property on $A$ and show on some numerical examples that these relaxation bounds can prove perfect recovery of sparse solutions with relatively high cardinality.     Title:  Sparse Solutions of Standard Quadratic Programmimng Problems with Random Matrices   Lead:  Jiming Peng,Assistant Professor, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Ave., Urbana IL 61801, United States of America, pengj@illinois.edu   Abstract:  In this talk, we investigate the properties of the solutions of the so-called standard quadratic programming problems (StQP), which has been proved to be NP-hard. Motivated by the recent results in compressed sensing, we focus on the scenarios where the associated matrix is random. We show that if all the elements of the underlying matrix follow i.i.d., then the corresponding StQP has a very sparse solution with a high probability.     Title:  Online Learning and Interior Point Algorithms for MDPs with Expected Budget Constraints   Lead:  Constantine Caramanis,The University of Texas at Austin, Austin TX, cmcaram@ece.utexas.edu   Co-Author:  Nedialko Dimitrov,The University of Texas at Austin, Department of Mechanical Engineering, 1 University Station C2200, Austin TX 78712, United States of America, ned.dimitrov@gmail.com   David Morton,Professor, The University of Texas at Austin, Graduate Program in Operations Research, 1 University Station C2200, Austin TX 78712, United States of America, morton@mail.utexas.edu   Abstract:  An MDP with n states can be solved using the value iteration algorithm in O(n^2) time, as opposed to the Omega(n^3) time required if one uses a linear program. Expected budget constraints on the MDP policy can be easily captured in a linear program formulation, but break the basic value iteration algorithm. We show two new algorithms for solving MDPs with k budget constraints giving the exact solution in O(poly(k) n^2) time or an approximately feasible solution in O(log(k) n^2) time.     Title:  Distributed Asynchronous Stochastic Optimization Method   Lead:  Angelia Nedich,Assistant Professor, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 South Mathews Avenue, Urbana IL 61801, United States of America, angelia@illinois.edu   Abstract:  We consider a distributed method over a network for optimizing a convex objective function, which is known to all agents in the network. At any given time, one agent updates and passes its estimate to one of its neighbors according to a Markov chain distribution. We consider a gradient-based scheme, and establish some convergence properties and error bounds on the performance.     Delete 13:30 - 15:00 : The Rise and Fall and Rise Again of Interior-Point Methods Chair:  Margaret Wright,Courant Institute of Mathematical Sciences, 251 Mercer Street, New York 10012, United States of America, mhw@cs.nyu.edu Title:  Then and Now: The Renaissance of Interior-Point Methods for Nonlinear Programming Part 1   Lead:  Margaret Wright,Courant Institute of Mathematical Sciences, 251 Mercer Street, New York 10012, United States of America, mhw@cs.nyu.edu   Abstract:  The 1984 "interior-point revolution" in optimization could equally well be called an "interior-point renaissance" because of its interconnected combination of old and new ideas. The two speakers will address several themes, including: the pre-1984 rise and fall of interior-point methods; old ideas that have been revived and extended; and genuinely new ideas related to computational complexity, ties with linear algebra, and approaches for large-scale problems.     Title:  Then and Now: The Renaissance of Interior-Point Methods for Nonlinear Programming (Part 2)   Lead:  Philip Gill,Professor, University of California, San Diego, 9500 Gilman Drive, # 0112, La Jolla CA 92093-0112, United States of America, pgill@ucsd.edu   Abstract:  The 1984 "interior-point revolution" in optimization could equally well be called an "interior-point renaissance" because of its interconnected combination of old and new ideas. The two speakers will address several themes, including: the pre-1984 rise and fall of interior-point methods; old ideas that have been revived and extended; and genuinely new ideas related to computational complexity, ties with linear algebra, and approaches for large-scale problems.     Title:  Interior Point Methods for Very Large Scale Optimization 25 Years Later   Lead:  Jacek Gondzio,Prof, Edinburgh University, School of Mathematics, Edinburgh EH9 2EA, United Kingdom, J.Gondzio@ed.ac.uk   Abstract:  This talk will focus on remaining challenges faced by interior point methods for very large scale optimization. In particular, the issues of iterative methods and a choice of suitable preconditioners to solve reduced Newton systems arising in optimization with interior point methods will be addressed.     Tuesday Oct 13 Delete 08:00 - 09:30 : Methods for Large-Scale Nonlinear Optimization Chair:  Philip Gill,Professor, University of California, San Diego, 9500 Gilman Drive, # 0112, La Jolla CA 92093-0112, United States of America, pgill@ucsd.edu Title:  Solution of a Stochastic Model for Allocation of On-line Advertising Inventory   Lead:  John Tomlin,Yahoo! Labs, 701 First Avenue, Sunnyvale CA 94089, United States of America, tomlin@yahoo-inc.com   Co-Author:  Vijay Bharadwaj,Yahoo! Inc., 701 First Avenue, Sunnyvale CA 94089, United States of America, vbharadw@yahoo-inc.com   MIchael Saunders,MSE Department, Stanford University, Stanford CA 94305, United States of America, saunders@stanford.edu   Abstract:  We describe the stochastic extension of a transportation model with separable convex objective for optimal allocation of online advertising inventory for guaranteed delivery. Using the approach pioneered by Beale and Dantzig, we arrive at a stochastic model that is also convex and separable. We apply a C++ version of the primal-dual interior solver PDCO.     Title:  An Interior-point Trust-funnel Algorithm for Large-scale Nonconvex Optimization   Lead:  Daniel Robinson,Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom, daniel.p.robinson@gmail.com   Co-Author:  Nick Gould,Professor, Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire OX11 0QX, United Kingdom, nick.gould@stfc.ac.uk   Philippe Toint,FUND-P University of Namur, 61 Rue de Bruxelles, B-5000, Namur, Belgium, philippe.toint@fundp.ac.be   Abstract:  We discuss an interior-point trust-funnel algorithm for large-scale nonconvex optimization. Global convergence of our method is ensured by trust-region methodology combined with the so-called ``funnel''. The prominent features of our algorithm are that each subproblem may be solved approximately and that inexact SQP steps may be computed when deemed advantageous during the course of solving each barrier subproblem.     Title:  SQP Methods for Nonlinear Optimization   Lead:  Philip Gill,Professor, University of California, San Diego, 9500 Gilman Drive, # 0112, La Jolla CA 92093-0112, United States of America, pgill@ucsd.edu   Co-Author:  Elizabeth Wong,University of California, San Diego, 9500 Gilman Drive, # 0112, La Jolla CA 92093-0112, United States of America, elwong@ucsd.edu   Abstract:  We consider the formulation and analysis of sequential quadratic programming (SQP) methods for large-scale nonlinearly constrained optimization that may be ``hot started'' from a good approximate solution. In this context we focus on SQP methods that are best able to use ``black-box'' linear algebra software. Such methods provide an effective way of exploiting recent advances in linear algebra software for multicore and GPU-based computer architectures     Delete 08:00 - 09:30 : IEEE Transactions on Automation Science & Engineering Sponsored Session Co-Chair:  Yu Ding,Associate Professor, Texas A&M University, 3131 MS, College Station TX 77843, United States of America, yuding@iemail.tamu.edu Chair:  N. Viswanadham,N_Viswanadham@isb.edu Title:  Port-of-Entry Inspection: Sensor Deployment Policy Optimization   Lead:  Elsayed Elsayed,Professor, Rutgers University, Industrial and Systems Engineering, Piscataway NJ 08854, United States of America, elsayed@rci.rutgers.edu   Co-Author:  Minge Xie,Associate professor, Rutgers University, Department of Statistics, Piscataway NJ 08854, United States of America, mxie@stat.rutgers.edu   Christina Young,PhD Candidate, Rutgers University, Industrial and Systems Engineering Dept, Piscataway NJ 08854, United States of America, cms168@rci.rutgers.edu   Hao Zhang,Rutgers University, Industrial and Systems Eng., Piscataway NJ 08854, United States of America, haoz88@gmail.com   Yada Zhu,Graduate Student, Rutgers University, Industrial and Systems Engineering, Piscataway NJ 08854, United States of America, yadazhu@eden.rutgers.edu   Abstract:  At the port-of-entry containers are inspected through a sequence of stations to detect illegal cargo. The inspection policy, which includes the sequence in which sensors are applied and the threshold levels used at the inspection stations, affects the probability of misclassifying a container as well as the cost and time spent in inspection. We will present our work in the optimization of such inspection systems, as well as recent investigation to reduce the effect of measurement error.     Title:  Sensor Selection in Arbitrary Dimensions   Lead:  Malik Magdon-Ismail,Associate Professor, Rensselaer Polytechnic Institute, magdon@cs.rpi.edu   Co-Author:  Volkan Isler,University of Minnesota, United States of America, isler@cs.umn.edu   Abstract:  We address the sensor selection problem which arises in tracking and localization applications. In sensor selection, the goal is to select a small number of sensors whose measurements provide a good estimate of a target’s state. We focus on the bounded uncertainty sensing model where the target is a point in the d dimensional Euclidean space, and show that, on the plane, four sensors are sufficient to obtain an estimate whose area is at most twice the area of the best possible estimate.     Title:  Identification of Influential Process Variables for Surface Quality Control in Hot Rolling   Lead:  Shiyu Zhou,University of Wisconsin-Madison, Department of Industrial and Systems Engineering, 1513 University Avenue, Madison WI 53706-1572, United States of America, szhou@engr.wisc.edu   Co-Author:  Tzyy-Shuh Chang,OG Technologies, 4300 Varsity Drive, Ann Arbor, United States of America, chang@ogtechnologies.com   Howard Huang,OG technologies, 4300 Varsity Drive, Ann Arbor, United States of America, huang@ogtechnologies.com   Nong Jin,University of Wisconsin - Madison, 1513 University Ave., Madison, United States of America, nong.jin@capitalone.com   Abstract:  Surface defects have been a long-term troubling issue in hot rolling processes. In this paper, functional data analysis and rigorous statistical testing are integrated to identify the process variables that significantly influence the surface quality of the finished products. The results can provide guidelines for root cause identification and surface quality improvement in hot rolling processes.     Delete 08:00 - 09:30 : Joint Session ICS/DM Optimization in Data Mining/Machine Learning Chair:  Paul Brooks,Virginia Commonwealth University, PO Box 843083, Richmond VA 23229, United States of America, jpbrooks@vcu.edu Title:  Solving the Sparse PCA SDP Relaxation   Lead:  David Phillips,Assistant Professor, College of William & Mary, Math Department, Williamsburg VA 23185, United States of America, djphil@wm.edu   Co-Author:  Garud Iyengar,Associate Professor, Columbia University, IEOR Department, New York NY 10027, United States of America, garud@ieor.columbia.edu   Cliff Stein,Professor, Columbia University, IEOR Department, New York NY 10027, United States of America, cliff@ieor.columbia.edu   Abstract:  In this presentation, we present an algorithm to solve the Sparse PCA SDP relaxation. Our solution algorithm solves a Lagrangian saddle point problem via a prox method of Nesterov's. Some key differences in our approach from previous algorithms is our use of a quadratic prox function and that we return feasible solutions to the SDP relaxation, where the former leads to a better run theoretical bound. Experimental results are presented.     Title:  Relaxing Support Vectors with Linear and Quadratic Programming Models   Lead:  Onur Seref,Assistant Professor, Virginia Tech, 1007 Pamplin Hall (0235), Blacksburg VA 24061, United States of America, seref@vt.edu   Abstract:  In this talk, we introduce linear and quadratic programming models to relax vectors that are usually misclassified by maximal margin classifiers using a restricted amount of free (unpenalized) total slack. We introduce kernelized versions and emphasize important properties of these models. We also introduce a simple 2-phase method based on these models for multiple instance classification and present competitive computational results on public benchmark datasets and neurological data.     Title:  From Pictures to 3D: A Global Optimization Approach   Lead:  Manmohan Chandraker,University of California, San Diego, La Jolla, United States of America, mkchandraker@cs.ucsd.edu   Co-Author:  Sameer Agarwal,University of Washington, Seattle, Seattle, United States of America, sagarwal@cs.washington.edu   Serge Belongie,University of California, San Diego, La Jolla, United States of America, sjb@cs.ucsd.edu   David Kriegman,University of California, San Diego, La Jolla 92093, United States of America, kriegman@cs.ucsd.edu   Abstract:  We present practical algorithms for achieving globally optimal solutions for 3D scene reconstruction from 2D images. We pose the non-convex problem as a fractional program and construct efficient convex envelopes to perform minimization in a branch and bound paradigm. A unified framework is developed for minimizing the standard L2-norm as well as the robust L1-norm. We demonstrate that exploiting domain knowledge can alleviate branch and bound's worst case exponential complexity in practice.     Title:  Locally Linear Support Vector Machines   Lead:  Paul Brooks,Virginia Commonwealth University, PO Box 843083, Richmond VA 23229, United States of America, jpbrooks@vcu.edu   Co-Author:  Vojislav Kecman,VCU, PO Box 843019, Richmond VA 23284, United States of America, vkecman@vcu.edu   Abstract:  In this talk we present a new method for combining the k-nearest neighbors (KNN) and support vector machines (SVM) to produce a classifier that reflects local patterns in data. The Locally Linear SVM (LLSVM) is able to maximize margin and minimize error in the input feature space for points in the neighborhood of a query and derive globally complex separating surfaces. Theoretical evidence of the superior stability of LLSVM over global (traditional) SVM is given.     Delete 11:00 - 12:30 : Optimization in Practice IV - Software Co-Chair:  Bjarni Kristjansson,President, Maximal Software, Inc., 2111 Wilson Boulevard, Suite 700, Arlington VA 22201, United States of America, bjarni@maximalsoftware.com Chair:  Rob Suggs,CEO, Vanguard Software Corporation, 1100 Crescent Green, Cary NC 27518, United States of America, rob.suggs@vanguardsw.com Title:  Optimization in Strategic Business Planning   Lead:  Rob Suggs,CEO, Vanguard Software Corporation, 1100 Crescent Green, Cary NC 27518, United States of America, rob.suggs@vanguardsw.com   Abstract:  Applying optimization techniques to strategic business planning can provide benefits that are potentially far greater than those realized by optimizing routine business operations. Quite simply, doing the right thing is far more important than doing the wrong thing well. We discuss the lessons learned in Vanguard's development of a forecasting and stochastic optimization solution that Novartis Vaccines & Diagnostics uses to optimize its drug investment activities.     Title:  Efficient Optimization with Portfolio Safeguard Software   Lead:  Stan Uryasev,University of Florida, Dept. of Industrial and Systems Engineer, 303 Weil Hall, P.O. Box 116595, Gainesville FL 32611-6595, United States of America, uryasev@ufl.edu   Abstract:  We will present several case studies mostly in financial optimization area conducted using Portfolio Safeguard optimization software. Case studies will cover the following topics: portfolio optimization and portfolio hedging with downside risk measures such as VaR, CVaR, Drawdown, and others.     Title:  Valid Inequalities for an Integer Programming Model for Software Projects with Soft Deadlines   Lead:  Ishwar Murthy,Professor, Indian Institute of Management, Bannerghatta Road, Bangalore 560076, India, ishwar@IIMB.ERNET.IN   Co-Author:  Kaushik Dutta,Associate Professor, Florida International University, Decision Sciences & Information Systems, College of Business Administration, Miami FL 33199, United States of America, Kaushik.Dutta@fiu.edu   Sridhar Narasimhan,Professor, Georgia Institute of Technology, College of Management, 800 West Peachtree St, NW, Atlanta GA 30308, United States of America, Sri.Narasimhan@mgt.gatech.edu   Abstract:  We develop an integer programming model that enables software development companies manage software engineers (in-house and vendors) at their disposal to complete their projects with minimal violation of the project deadlines. The manpower requirement as well as the skill sets required for each project in each time period is known. The above NP-Hard problem is modeled as a multi-commodity path problem on an acyclic graph, with side constraints.     Title:  OptForce™ Strategic Workforce Planning Technology: Combining Simulation and Optimization   Lead:  Jay April,Chief Development Officer, OptTek Systems, Inc., 1919 Seventh Street, Boulder CO 80302, United States of America, april@opttek.com   Co-Author:  Marco Better,Director of Custom Solutions, OptTek Systems, 1919 Seventh Street, Boulder CO 80302, United States of America, better@opttek.com   Fred Glover,CTO, OptTek Systems, 1919 Seventh Street, Boulder CO 80302, United States of America, glover@opttek.com   James Kelly,CEO, OptTek Systems, 1919 Seventh Street, Boulder CO 80302, United States of America, kelly@opttek.com   Abstract:  This presentation describes simulation optimization software technology being developed under a National Science Foundation SBIR contract to be used to enhance strategic workforce planning and management. The software combines two innovative methods drawn from the field of operations research: agent-based simulation and metaheuristic optimization. The software, which is called OptForce™, is used to evaluate decisions for creating, modifying, and eliminating workforce policies and programs.     Delete 11:00 - 12:30 : Modeling and Optimization in Health Care Chair:  Todd Easton,Associate Professor, Kansas State University, 2037 Durland Hall, Manhattan KS 66506, United States of America, teaston@ksu.edu Title:  A System Approach for Uncertainty Modeling in Clinical Laboratory Measurements   Lead:  Varun Ramamohan,Graduate Student (PhD), Department of Industrial Engineering, Purdue University, 315 North Grant Street, West Lafayette IN 47907, United States of America, vramamoh@purdue.edu   Co-Author:  Jim Abbott,Manager, Clinical Support Group, Roche Diagnsotics Corporation, 9115 Hague Road, PO Box 50457, Indianapolis IN 46250-0457, United States of America, jim.abbott@roche.com   Vishal Chandrasekar,Graduate Student (MS), Department of Industrial Engineering, Purdue University, 315 North Grant Street, West Lafayette IN 47907, United States of America, vchandra@purdue.edu   George Klee,Professor of Laboratory Medicine & Pathology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester MN 55905, United States of America, klee.george@mayo.edu   Yuehwern Yih,Professor, Purdue University, 315 N. Grant Street, West Lafayette, United States of America, yih@purdue.edu   Abstract:  Systematic errors in laboratory measurements have significant impact on clinical decisions, patient safety and medical costs. A system approach towards quality control of clinical laboratory measurements is described in this paper. We present a calibration model that identifies the sources of systematic error and then systematically analyzes the propagation of these errors throughout the process. Monte Carlo simulation is used to derive the general uncertainty measure for this bias.     Title:  Modeling the Treatment-prevention Tradeoff   Lead:  George Miller,Institute Fellow, Altarum Institute, 3520 Green Court, Suite 300, Ann Arbor MI 48105, United States of America, george.miller@altarum.org   Co-Author:  Matt Daly,Senior Analyst, Altarum Institute, 3520 Green Court, Suite 300, Ann Arbor MI 48105, United States of America, matt.daly@altarum.org   Abstract:  It is frequently claimed "with little quantitative evidence" that current health care expenditures inappropriately emphasize treatment over prevention. We are investigating this assertion using a dynamic model of the impacts of alternative investments in treatment and prevention, and in research to develop new treatment and prevention interventions, on effectiveness (measured in quality adjusted life years). We describe the structure of the model and emerging results from its application.     Title:  Radiation Quality Assurance with DICOM Information   Lead:  Shanshan Wang,PhD Student, Arizona State University, School of CIDSE, Tempe AZ 85281, wshanshan@asu.edu   Co-Author:  Steve Langer,Associate Professor of Radiologic Physics, Radiology, Mayo Clinic, Arizona, AZ,   Richard L. Morin,Brooks-Hollern Professor, Professor of Radiologic Physics, Radiology, Mayo Clinic, Rochester, AZ,   William Pavlicek,Associate Professor of Radiologic Physics, Mayo Clinic, Radiology, AZ,   Mary B. Peter,Physicist - Diagnostic Imaging, Radiology, Mayo Clinic, Rochester, AZ,   Catherine C. Roberts,Associate Professor of Radiology, Radiology, Mayo Clinic, Arizona, AZ,   Beth A. Schueler,Associate Professor of Radioligy, Radiology, Mayo Clinic, Rochester, AZ,   Teresa Wu,Associate Professor, Arizona State University, Tempe AZ 85281, United States of America, teresa.wu@asu.edu   Muhong Zhang,Asst Professor, Arizona State University, Arizona State University, Department of Industrial Engineering, Tempe 85287, United States of America, Muhong.Zhang@asu.edu   Abstract:  The oversight and monitoring of radiation dose use are required in federal and state statutes. Digital Imaging and Communications in Medicine (DICOM) standards is a widely adopted standard in radiation imaging processing. In this study, we build an information system to capture the information in DICOM compliant radiation images and explore their use in routine quality assurance activities. The system consists of a knowledge base of known devices (i.e. modalities), a patient episode dose tracking database and a set of web-based reporting tools. The implementation potentially provides quantitative data hints to reduce radiation exposure and automates the radiation regulatory compliance process.     Title:  The When Diet : Mathematically Optimizing Eating and Exercise for Weight Loss   Lead:  Todd Easton,Associate Professor, Kansas State University, 2037 Durland Hall, Manhattan KS 66506, United States of America, teaston@ksu.edu   Abstract:  Over the past five years I have studied the dieting problem and recently published The When Diet: Mathematically Optimizing Eating and Exercise for Weight Loss. This talk presents the major developments contained in this book, including a mathematical proof of The When Diet, the stochastic properties of hunger, lean manufacturing and how to minimize the misery incurred by being on a diet while maximizing the amount of weight lost.     Delete 11:00 - 12:30 : Stochastic Optimization III Chair:  Rahul Nair,Grad Research Assistant, University of Maryland, 1173 Glenn Martin Hall, College Park MD 20742, United States of America, rahul@umd.edu Title:  Definition of Maintenance Policies Applying Stochastic Optimization   Lead:  Carlos Osorio-Ramirez,Politecnico Grancolombiano, Calle 57 3 00 Este Fac. Ingenieria, Bogota, Colombia, caosorio@poligran.edu.co   Co-Author:  Javier Nieto,DecisionWare, Cra 7 88 16 Of 202, Bogota, Colombia, javier.nieto@decisionware-ltd.com   Jesus Velasquez,DecisionWare, Cra 7 88 16 Of 202, Bogota, Colombia, jesus.velasquez@decisionware-ltd.com   Abstract:  This paper presents a non-anticipative stochastic optimization model in order to determine the most common maintenance decisions, according to a set of possible scenarios with some feasible sequences of failures.     Title:  Computational Experience of Solving Two-stage Stochastic Linear Programming Problems   Lead:  Viktar Zviarovich,PhD Student, CARISMA, Brunel University, Kingston Lane, Uxbridge UB8 3PH, United Kingdom, viktar.zviarovich@brunel.ac.uk   Co-Author:  Francis Ellison,CARISMA, Brunel University, Kingston Lane, Uxbridge UB8 3PH, United Kingdom, francis.ellison@brunel.ac.uk   Csaba Fabian,Institute of Informatics, Kecskemet College, 10 Izsaki ut, Kecskemet, Hungary, fabian.csaba@gamf.kefo.hu   Gautam Mitra,CARISMA, Brunel University, West London, United Kingdom, gautam.mitra@brunel.ac.uk   Abstract:  We present a computational study of two-stage SP solution algorithms for a range of benchmark problems. We consider application of (1) Simplex method and (2) IPM to solve deterministic equivalent problems, (3) Benders decomposition, (4) stochastic decomposition and (5) three regularisation methods. The first method is an experimental heuristic regularisation. The last two are based on the level decomposition by Fabian and Szoke. The scale-up properties and the performance profiles are presented.     Title:  A Stochastic Inventory Model for Critical Spare Parts used for Maintenance of Construction Machines   Lead:  Nihat Kasap,Assistant Professor, Sabanci University, Faculty of Management, Istanbul 34956, Turkey, nihatk@sabanciuniv.edu   Co-Author:  Ilker Bicer,Commandership of Gendarmerie Military Engineering Corps, J. Istihkam Insaat Grup Kom. Guvercinlik, Ankara, Turkey, ibicer73@gmail.com   Abstract:  We develop a stochastic inventory model for critical spare parts used for the maintenance of heavy construction machines. Proposed model is a nonlinear integer optimization problem with average service level and replenishment frequency constraints. The aim of the model is to find out reorder points and stocking levels of critical spare parts that minimize the inventory costs. We propose heuristic that contains modified ABC Analysis and can be implemented with the spreadsheet applications easily.     Title:  Progressive Hedging Applied to Stochastic Forestry Planning Problem   Lead:  Fernando Badilla Veliz,Master in OR thesist, Universidad de Chile, Beauchef 850, Santiago, Manuel de Salas 35, depto. 151, Ñuñoa, Santiago, Chile, fbadilla@ing.uchile.cl   Co-Author:  Andres Weintraub,Professor, University of Chile, Beauchef 850, Santiago, Santiago, Chile, aweintra@dii.uchile.cl   Roger Wets,Professor, University of California Davis, One Shields Avenue, Davis CA 95616, United States of America, rjbwets@ucdavis.edu   David Woodruff,Professor, University of California Davis, UC Davis, Davis, CA 95616, Davis CA, United States of America, dlwoodruff@ucdavis.edu   Abstract:  The problem consist in planning the optimal harvest, deciding when and which lots to harvest, roads to build (binary decisions) and the wood flow through these roads; under different probabilistic price, demand and yield scenarios. We present an approach to solve this integer stochastic problem which handles the non anticipativity constraints based on the heuristic progressive hedging, decomposing the problem so that it's not limited by the number of scenarios considered.     Title:  A Recursive Decomposition Algorithm for Generating p-Efficient Points   Lead:  Rahul Nair,Grad Research Assistant, University of Maryland, 1173 Glenn Martin Hall, College Park MD 20742, United States of America, rahul@umd.edu   Co-Author:  Elise Miller-Hooks,Associate Professor, University of Maryland, 1173 Glenn Martin Hall, College Park MD 20742, United States of America, elisemh@umd.edu   Abstract:  p-efficient points are employed in solving programs with joint chance constraints where the random vector is discrete. Generating these points is problematic when the number of dimensions or domain of the random vector is large. A new recursive decomposition algorithm is proposed that restricts the search to sub-domains that contain the p-frontier thereby reducing the computation and memory requirements.     Delete 11:00 - 12:30 : Applications of SDP to EDM and other Polynomial Optimization Problems Chair:  Henry Wolkowicz,University of Waterloo, Dept of Comb and Opt, Waterloo ON N2L 3G1, Canada, hwolkowi@uwaterloo.ca Title:  Universal Rigidity: Towards Accurate and Efficient Localization of Wireless Networks   Lead:  Yinyu Ye,Professor, Stanford University, 316 Terman Engineering Building, Stanford CA 94305, United States of America, yinyu-ye@stanford.edu   Co-Author:  Anthony Man-Cho So,The Chinese Univ of Hong Kong, Department of SE&EM, Shatin NT, Hong Kong - PRC, manchoso@se.cuhk.edu.hk   Zhisu Zhu,Stanford University, 102 Hoskins Ct, Apt 419, Stanford CA 94305, United States of America, zhuzhisu@stanford.edu   Abstract:  We propose a notion of universal rigidity that captures a large class of wireless networks and is much more relevant to the efficient solvability of network localization problems. We give various constructions of universally rigid instances. We also apply our results to design a novel edge sparsification scheme that can reduce the size of the input network while provably preserving its original localization properties.     Title:  Exploiting Sparsity in SDP Relaxation for Sensor Network Localization   Lead:  Hayato Waki,Dr., The University of Electro-Communications, Chofugaoka, Chofu-Shi, Tokyo 182-8585, Japan, hayato.waki@jsb.cs.uec.ac.jp   Co-Author:  Sunyoung Kim,Professor, Ewha W. University, 11-1 DaHyun-Dong,, SuhDaeMoon-Gu, Seoul 120-750, Korea, Republic of, skim@ewha.ac.kr   Masakazu Kojima,Professor, Tokyo Institute of Technology, 2-12-1-W8-29, Oh-Okayama, Meguro-ku, Tokyo 152-8552, Japan, kojima@is.titech.ac.jp   Abstract:  We present a sparse variant of the Biswas-Ye SDP relaxation for sensor network localization problems. We compare numerically the Biswas-Ye SDP relaxation, its sparse variant, and the edge-based SDP relaxation by Wang et al. to confirm the effectiveness of the proposed techniques for exploiting sparsity in SDP relaxation for sensor network localization problems. The sparse variant of the Biswas-Ye SDP relaxation outperforms all other SDP relaxations in speed.     Title:  On SDP and ESDP Relaxations for Sensor Network Localization   Lead:  Paul Tseng,Dept of Mathematics, University of Washington, Seattle, United States of America, tseng@math.washington.edu   Co-Author:  Ting Kei Pong,Ph.D. student, University of Washington, Box 354350, Math, Univ. Washington, Seattle WA 98195-4350, United States of America, tkpong@math.washington.edu   Abstract:  We study SDP and ESDP relaxations for ad hoc wireless sensor network localization. In particular, we propose a noise-aware version whose solution set is more robust in the presence of noisy distance measurements. We also propose an efficient distributed method to find an interior solution.     Title:  Explicit Sensor Network Localization Using Semidefinite Programming and Clique Reductions   Lead:  Henry Wolkowicz,University of Waterloo, Dept of Comb and Opt, Waterloo ON N2L 3G1, Canada, hwolkowi@uwaterloo.ca   Co-Author:  Nathan Krislock,University of Waterloo, Dept. Comb. & Opt., Faculty of Mathematics, Waterloo ON N2L3G1, Canada, ngbkrisl@uwaterloo.ca   Abstract:  The sensor network localization, SNL, problem consists of locating the positions of sensors, given only the distances between sensors that are within radio range and the positions of some fixed sensors (called anchors). By finding explicit representations of the faces of the SDP cone corresponding to unions of cliques of the SNL problem, we derive a preprocessing technique that solves the SNL problem, with exact data, by explicitly solving the corresponding SDP problem.     Delete 13:30 - 15:00 : Algorithms and Applications of Polynomial Optimization and Robust Optimization Chair:  X. Andy Sun,Doctoral Candidate, Operations Research Center, MIT, 77 Massachusetts Ave., Cambridge MA 02139, United States of America, sunx@MIT.EDU Title:  Efficient First-Order Methods for Polynomial Optimization   Lead:  X. Andy Sun,Doctoral Candidate, Operations Research Center, MIT, 77 Massachusetts Ave., Cambridge MA 02139, United States of America, sunx@MIT.EDU   Co-Author:  Dimitris Bertsimas,Boeing Professor of Operations Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Bldg. E40-149, Cambridge MA 02139, United States of America, dbertsim@mit.edu   Robert Freund,Professor, MIT, Sloan School of Mgmt., E52-476, 50 Memorial Drive, Cambridge MA 02142, United States of America, rfreund@mit.edu   Abstract:  We apply efficient first-order methods to polynomial optimization problems (POPs), which are generally difficult to solve. By exploiting special structure of the SDP representation of the POP, we demonstrate that first-order methods have the promise to improve the computational solvability of general POPs. We also explore techniques for solving sparse problems that greatly enhance computational efficiency.     Title:  Semidefinite Relaxations for Multistage Robust Optimization and Stochastic Programming   Lead:  Dan Iancu,Doctoral Candidate, Operations Research Center, MIT, 77 Massachusetts Ave., Cambridge MA 02139, United States of America, daniancu@mit.edu   Co-Author:  Dimitris Bertsimas,Boeing Professor of Operations Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Bldg. E40-149, Cambridge MA 02139, United States of America, dbertsim@mit.edu   Pablo A. Parrilo,Professor, MIT, Stata Center, MIT, Cambridge, United States of America, parrilo@mit.edu   Abstract:  In this work, we introduce a new framework for computing near-optimal policies for multi-stage robust optimization and stochastic programming problems, based on solving a single semi-definite programming relaxation. We demonstrate the performance of the resulting policies numerically, in the context of two classical inventory management applications.     Title:  Models for Minimax Stochastic Linear Optimization Problems with Risk   Lead:  Xuan Vinh Doan,Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge MA 02139, United States of America, vanxuan@mit.edu   Co-Author:  Dimitris Bertsimas,Boeing Professor of Operations Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Bldg. E40-149, Cambridge MA 02139, United States of America, dbertsim@mit.edu   Karthik Natarajan,National University of Singapore, Department of Mathematics, Singapore 117543, Singapore, matkbn@nus.edu.sg   Chung-Piaw Teo,National University of Singapore, Department of Decision Sciences, Business School, Singapore 117591, Singapore, bizteocp@ nus.edu.sg   Abstract:  We study the minimax stochastic optimization problem with a moment-based distribution class. We show that the model is tractable for problems with random objective and some special ones with random right-hand side. We provide explicit worst-case distributions and compare minimax solutions with data-driven solutions under contaminated distributions. Computational results show minimax solutions hedge against worst-case distributions and provide lower variability in cost than data-driven ones.     Delete 13:30 - 15:00 : Risk in Portfolio Decision Analysis Chair:  Jeff Keisler,Associate Professor, UMass Boston, 100 Morrissey Blvd, Boston MA 02125, United States of America, Jeff.Keisler@umb.edu Title:  Managing a Portfolio of Risks   Lead:  Jeff Keisler,Associate Professor, UMass Boston, 100 Morrissey Blvd, Boston MA 02125, United States of America, Jeff.Keisler@umb.edu   Co-Author:  Igor Linkov,Risk and Decision Science Focus Area Lead, US Army ENgineer Research and Development Center, 83 Winchester Str, Suite 1, Brookline MA 02446, United States of America, Igor.Linkov@usace.army.mil   Abstract:  We describe several key steps in applying DA to a portfolio of risks. First, we frame the portfolio and map it to a portfolio of decisions. Alternatives include allocation of funds to reduction activities for each risk, but may be more specific, e.g., mitigation, amelioration, diversification, and sharing. Risk interactions such as correlation and causal relationships must be efficiently characterized. Value functions are designed to consider significant losses across multiple dimensions.     Title:  Using Portfolio Objectives to Define and Manage Portfolio Risk   Lead:  Jack Kloeber,Kromite LLC, 12 Penns Trail, Newtown PA 18940, United States of America, jkloeber@kromite.com   Abstract:  In R&D organizations, budget, talent, time, constrain the number and size of R&D projects which can be resourced. Organizational objectives drive us to choose which project strategies should be funded. Since R&D is inherently risky, management needs a process for taking portfolio risk, not project risk, into account when making such decisions. We will discuss methods for accomplishing this, as well give examples of lost value when portfolio risk is ignored.     Title:  A Simple Mean-risk Model with Behavioral and Prescriptive Flexibility   Lead:  Alessandra Cillo,Assistant Professor, IESE, Avenida Pearson 21, Barcelona 08034, Spain, acillo@iese.edu   Co-Author:  Philippe Delquie,INSEAD, Boulevard de Constance, Fontainebleau, France, philippe.delquie@insead.edu   Abstract:  We propose a behavioral Mean-Risk model that fulfills first and second order stochastic dominance generalizing results on Mean-Gini, and the axioms of Convex Risk Measures. It produces behavior intermediary between Expected Utility and Rank-Dependent Utility. Model's predictions in asset trading and allocation are provided.     Title:  Portfolio Optimization of Natural Resources with an Application to the Management of Forest Stands   Lead:  Janne Kettunen,jkettunen@london.edu   Co-Author:  Ahti Salo,Professor, Helsinki University of Technology, Systems Analysis Laboratory, P.O. Box 1100, Espoo 02015, Finland, ahtisalo@cc.hut.fi   Abstract:  We develop a stochastic optimization model for a forest owner who needs to determine medium-term harvesting strategies under uncertainty about timber prices. Risks are managed by applying conditional value at risk and satisfying risk measures. The results suggest that extreme risks can be significantly reduced without appreciable reductions in the expected proceeds from timber sales. The results also suggest that risk averse owners tend to harvest their forests earlier than risk neutral owners.     Delete 13:30 - 15:00 : Computational Linear and Integer Programming Chair:  Yan Xu,Analytical Solutions Manager, SAS Institute Inc., SAS Campus Drive, Cary, United States of America, Yan.Xu@sas.com Title:  Microsoft Solver Foundation   Lead:  John Oberon,Director of Program Management, One Microsoft Way, 4/2194, Redmond WA 98052-6399, United States of America, John.Oberon@Microsoft.com   Abstract:  Solver Foundation is a pure, managed code runtime for mathematical programming, modeling, and optimization. This .NET/CLR based framework provides a rich set of tools, services, and engines. We will focus on our computational integer programming effort - a branch and cut solver based on Dual Simplex, and is designed to enable 64-bit and multi-core computation environments. We will discuss advanced presolve, Gomory mixed integer cuts, mixed integer rounding cuts, and local search heuristics.     Title:  Solving Linear Optimization Problems with MOSEK   Lead:  Bo Jensen,MOSEK ApS, C/O Symbion Science park Fruebjergevj 3, Copenhagen, Denmark, bo.jensen@mosek.com   Abstract:  The software package MOSEK is capable of solving large-scale sparse linear optimization problems using either an interior-point, a primal simplex or a dual simplex algorithm. The aim of this talk is to present the linear optimizers and the recent advances in their implementation. Moreover, we will present numerical results demonstrating the optimizers performance.     Title:  Cutting Planes in SAS MILP Solver   Lead:  Amar Narisetty,SAS Institute, SAS Campus Drive, Cary, United States of America, Amar.Narisetty@sas.com   Co-Author:  Yan Xu,Analytical Solutions Manager, SAS Institute Inc., SAS Campus Drive, Cary, United States of America, Yan.Xu@sas.com   Abstract:  The SAS MILP solver implements a branch-and-cut algorithm for solving large scale mixed integer linear programs. In this talk, we give an overview of the families of cutting planes currently incorporated in the SAS MILP solver. These include, GMIC, MIR, Covers, Cliques, etc. We present computation results to demonstrate the effectiveness and contribution of each family of cutting planes in solving some well known instances.     Title:  IBM ILOG CPLEX Technology Update   Lead:  John Gregory,ILOG CPLEX Product Manager, IBM ILOG Optimization, 889 Alder, Suite 200, Incline Village NV 89451, United States of America, jgregor@us.ibm.com   Abstract:  This talk will highlight the recent major enhancements in IBM ILOG CPLEX, covering both solution speed and usability features. Details on MIP benchmarking results will be provided. The latest information about the integration of ILOG and its products into the greater IBM optimization community will also be shared.     Delete 16:30 - 18:00 : Multivariate Polynomial Optimization: Theory and Algorithms Chair:  Jiawang Nie,UCSD, 9500 Gilman Drive, La Jolla\, United States of America, njw@math.ucsd.edu Title:  Which Convex Sets are Expressible by Semidefinite Programming ?   Lead:  J. William Helton,Professor, UCSD, 9500 Gilman Drive, La Jolla, United States of America, Helton@math.ucsd.edu   Co-Author:  Jiawang Nie,UCSD, 9500 Gilman Drive, La Jolla\, United States of America, njw@math.ucsd.edu   Abstract:  There are three natural directions this problem can take. The first one is what convex sets can be represented by linear matrix inequality (LMI). The second direction is whether this can be done when the variables are matrices of arbitrary dimensions. The last one is what convex sets are LMI represented by adding auxiliary variables, which is also called semidefinite programming (SDP) representable.     Title:  A Convex Polynomial that Is not SOS-convex   Lead:  Amir Ali Ahmadi,MIT, 195 Binney st., Apt. #4401, Cambridge MA 02142, United States of America, a_a_a@mit.edu   Co-Author:  Pablo A. Parrilo,Professor, MIT, Stata Center, MIT, Cambridge, United States of America, parrilo@mit.edu   Abstract:  The algebraic notion of sos-convexity has recently been proposed as a sufficient condition for convexity of polynomials based on semidefinite programming. Motivated by its computational tractability, it has been speculated whether sos-convexity is also a necessary condition for convexity of polynomials. In this paper, we give a negative answer to this question by presenting an explicit example of a trivariate homogeneous polynomial of degree eight that is convex but not sos-convex.     Title:  A New SDP Approach to the Max-cut Problem   Lead:  Joao Gouveia,University of Washington, Department of Mathematics Box 354350, Seattle WA 98105, United States of America, jgouveia@u.washington.edu   Co-Author:  Monique Laurent,CWI, Kruislaan 413, 1098 SJ, Amsterdam, Netherlands, monique@cwi.nl   Pablo A. Parrilo,Professor, MIT, Stata Center, MIT, Cambridge, United States of America, parrilo@mit.edu   Rekha R. Thomas,University of Washington, Department of Mathematics Box 354350, Seattle WA 98105, United States of America, rrthomas@u.washington.edu   Abstract:  Using sums-of-squares techniques we develop a new hierarchy of SDP relaxations for the Max-Cut problem, solving an open question by Laszlo Lovasz.     Title:  SDP Representation of Rational and Singular Convex Sets   Lead:  Jiawang Nie,UCSD, 9500 Gilman Drive, La Jolla\, United States of America, njw@math.ucsd.edu   Co-Author:  J. William Helton,Professor, UCSD, 9500 Gilman Drive, La Jolla, United States of America, Helton@math.ucsd.edu   Abstract:  A set is called SDP representable if it is expressible by some linear matrix inequality via lifting variables. First, we will present a general result: A set S defined by polynomial inequalities is SDP representable if its boundary pieces are nonsingular and positively curved. Second, we will present conditions for SDP representability when S is defined by multivariate rational polynomial functions or its boundary pieces have singularities. Specific examples will also be shown.     Wednesday Oct 14 Delete 08:00 - 09:30 : Wireless Networks 2 Chair:  Ivan Guardiola,Assistant Professor, Missouri S&T, 600 W. 14th St, Rolla MO 65409, United States of America, guardiolai@mst.edu Title:  Control Overhead Reduction and Through Movement Awareness using GPS Information.   Lead:  Ivan Guardiola,Assistant Professor, Missouri S&T, 600 W. 14th St, Rolla MO 65409, United States of America, guardiolai@mst.edu   Abstract:  This presentation highlights a new approach of mitigating the correlated effects of movement and multi-path fading on wireless ad hoc network communication. The use of GPS and velocity estimation methods during the route discovery phase result in more stable communication route within specific time interval. This leads to an increase of scalability as the control overhead is reduced. We present a new fundamental approach to movement based route selection.     Title:  GPS Link Exclusion Comparison In TCP and UDP Based MANETS   Lead:  aaron phillips,Texas Tech University, 2320 A 18th Street, Lubbock TX 79401, United States of America, aaron.phillips@ttu.edu   Co-Author:  Timothy Matis,Texas Tech University, Dept of Industrial Engineering, Box 43061, Lubbock TX 79409, United States of America, timothy.matis@ttu.edu   Abstract:  Work done using GPS location information to exclude possibly weak links in a route sending data packets has covered both UDP and TCP transport protocols. The question raised is whether such a mechanism might be transport-protocol specific. This research seeks to determine if the GPS link exclusion method benefits one transport protocol more than the other, or if this mechanism benefits the network independent of the transport protocol being used.     Title:  Optimization Approaches to Wireless Sensor Network Design Problem   Lead:  Hui Lin,Texas A&M University, 201 Zachary, College Station, United States of America, linhui@neo.tamu.edu   Co-Author:  Halit Uster,Associate Professor, Texas A&M University, 3131TAMU, college station TX 77843, United States of America, uster@neo.tamu.edu   Abstract:  We present an integrated analytical modeling framework for wireless sensor networks design incorporating topology control and routing decisions. The optimization model attempts to promote energy efficiency with the purpose of prolonging network lifetime. We develop efficient solution procedures based on energy distribution characteristics and decomposition approaches. We also present computational results on the performance of the solution methods for test problem instances.     Title:  A Lagrangian Algorithm for the Capacitated Relay Network Design in Telecommunications   Lead:  Panitan Kewcharoenwong,Texas A&M University, Industrial and System Engineering, College Station TX, United States of America, panitan@neo.tamu.edu   Co-Author:  Halit Uster,Associate Professor, Texas A&M University, Industrial and Systems Engineering, College Station TX, United States of America, uster@tamu.edu   Abstract:  We consider a multi-commodity flow network where the commodities are relayed from origins to destinations through relay points. We provide a mathematical model to determine the relay point locations, arc selections, and data routing. We present a Lagrangian relaxation based algorithm for its solution and computational results illustrating the efficiency of the approach.     Delete 08:00 - 09:30 : Freight Planning & Policy Chair:  Rajeev Namboothiri,CIRRELT - CRT, C.P. 6128, succ. Centre-ville, MONTREAL, Canada, rajeev@crt.umontreal.ca Title:  Using Operations Research to Support > California Low-Carbon Emission Policy formation   Lead:  Changzheng Liu,University of California at Davis, Dep. of Civil & Environmental Eng., Davis CA 95616, United States of America, czliu@ucdavis.edu   Co-Author:  Yueyue Fan,Associate Professor, UC Davis, Department of Civil Engineering, One Shields Avenue, Davis CA 95616, United States of America, yyfan@ucdavis.edu   David Greene,Corporate Fellow, Oak Ridge National Laboratory, OAK RIDGE NATIONAL LABORATORY, Oak Ridge, United States of America, dlgreene@ornl.gov   Abstract:  This research studies the design and implementation of a feebate program in California for Green House Gas (GHG) emission reduction in Transportation sector by stimulating advanced vehicle technologies. Feebate is a market policy which charges fees on relatively high-emitting vehicles and gives rebates on low-emitting vehicles. A nonlinear program is established to identify an optimal feebate policy that balances the cost of technology and the requirement of related regulations such as CAFE     Title:  Insights into Robust Optimization: Lessons from Different Applications and Methods   Lead:  Lavanya Marla,lavanya@MIT.EDU   Co-Author:  Cynthia Barnhart,Associate Dean for Academic Affairs, MIT, School of Engineering, Cambridge MA 02139, United States of America, cbarnhart@MIT.EDU   Eleni Pratsini,IBM Research Zurich Research Lab, Ruschlikon, Switzerland, pra@zurich.ibm.com   Alexander Rikun,MIT, 1 Amherst St. E40-149, Cambridge MA 02139, United States of America, arikun@mit.edu   Gautier Staffer,IBM Research Zurich Research Lab, Ruschlikon, Switzerland, sta@zurich.ibm.com   Abstract:  In this talk, we will share insights from the application of different robust optimization approaches to the class of resource allocation problems. Instances of resource allocation problems are extracted from the finance, pharmaceutical and airline industries. We study the impact of building robust solutions using different methods for these applications. Through these instances, we improve upon our understanding of different robust approaches, and their relationship with data and recovery.     Title:  Measuring Logistics Security for Rationalizing Counter-Risk Priority: An AHP-Based Case Study   Lead:  Jung Ung Min,Assistant Professor, Inha University, Yonghyun 253, Incheon, Korea, Republic of, jumin@inha.ac.kr   Co-Author:  Minyoung Park,Assistant Professor, Inha University, Yonghyun 253, Incheon, Korea, Republic of, mypark@inha.ac.kr   Abstract:  To improve the efficiency of logistics and ensure safe freight flow, a comprehensive set of supply chain security initiatives have been practiced throughout the world. Therefore, it is necessary to respond immediately to security requirements. In this context, this study visited security experts in Korea in order to assess the security level of Incheon Port. Using AHP, this research derived the security items with respect to the importance level and their priorities.     Title:  A Comprehensive Evaluation of Regional Freight Transportation Systems Using an Integrated Platform   Lead:  Rajeev Namboothiri,CIRRELT - CRT, C.P. 6128, succ. Centre-ville, MONTREAL, Canada, rajeev@crt.umontreal.ca   Co-Author:  Teodor Crainic,CIRRELT-UQAM, C.P. 6128, succursale Centre-ville, Montreal QC H3C 3J7, Canada, theo@crt.umontreal.ca   Jean Damay,SNCF, SNCF, Paris, France, damay@crt.umontreal.ca   Michel Gendreau,Professor, CIRRELT/Université de Montréal, C.P. 6128, succ. Centre-ville, Montréal QC H3C 3J7, Canada, michel.gendreau@cirrelt.ca   Abstract:  In this talk, we analyse the strategic national/regional planning of multi-commodity multi-modal freight transportation systems using an integrated evaluation platform, and present comprehensive results examining the impact of planned and forecasted system modifications and regulatory policies     Delete 11:00 - 12:30 : Green Energy III Chair:  Panos Pardalos,University of Florida, 303 Weil Hall, P.O. Box 116595, Gainesville FL, United States of America, pardalos@ufl.edu Co-Chair:  Niko Iliadis,EnerCoRD, Plastira Street 4, Nea Smyrni, Athens 171 21, Greece, niko.iliadis@enercord.com Steffen Rebennack,PhD Candidate, University of Florida, 303 Weil Hall, P.O. Box 116595, Gainesville FL 32611, United States of America, steffen@ufl.edu Title:  Including Wind in Power-System Siting and Capacity Expansion Models   Lead:  Richard Chen,University of Michigan, IOE, Ann Arbor, United States of America, richchen@umich.edu   Co-Author:  Duncan Callaway,University of Michigan, SNRE, Ann Arbor, United States of America, dcall@umich.edu   Amy Cohn,University of Michigan, 1205 Beal Avenue, Ann Arbor MI 48109, United States of America, amycohn@umich.edu   Abstract:  When including wind in power-system design models, it is important to take into account transmission losses, because wind is often cited far from the load centers, unlike conventional power sources such as coal or natural gas based generation. We present new models for siting and capacity expansion planning that incorporate wind and the associated transmission losses. We demonstrate how these models can be solved using a variation of Benders Decomposition.     Title:  Test-and-Prune: Designing Wind Farms with Probabilistic Constraints   Lead:  Amy Cohn,University of Michigan, 1205 Beal Avenue, Ann Arbor MI 48109, United States of America, amycohn@umich.edu   Co-Author:  Duncan Callaway,University of Michigan, SNRE, Ann Arbor, United States of America, dcall@umich.edu   Richard Chen,University of Michigan, IOE, Ann Arbor, United States of America, richchen@umich.edu   Abstract:  We present models for designing networks of wind farms in which probabilistic constraints on loss-of-load must be enforced. We demonstrate the challenges of solving these models with traditional IP techniques and introduce a new algorithm, which we call Test-and-Prune, to overcome these challenges.     Title:  Incorporating Renewables and Demand Response into Unit Committment Problems   Lead:  Jayant Kalagnanam,Senior Manager, IBM T.J. Watson Research Center, Yorktown Hts NY 10598, United States of America, jayant@us.ibm.com   Abstract:  Many utilities have begin to incorporate wind and solar energy as part of their generation portfolio. Due to uncertainty from weather this leads to uncertainty in generation sources which needs to be explitly modeled in the unit committment problem. We study the stochastic unit committment problem to solve for generation uncertainty.     Title:  Optimization of Wind Farm Portfolios   Lead:  Warren Powell,Professor, Princeton University, Sherrerd Hall 230, Princeton NJ 08544, United States of America, powell@princeton.edu   Co-Author:  Yintao (Alex) Sun,Princeton University, Dept of Operations Research and Fin Engg, Princeton NJ 08544, United States of America, yintaos@Princeton.EDU   Abstract:  The electricity from multiple wind farms can be combined on a single electric power grid, making it possible to balance correlations in output to produce a portfolio with lower overall volatility. We use both a Markowitz model and a stochastic gradient algorithm to design wind portfolios which mitigate the intermittent nature of wind by using actual wind profiles which reflect spatial and temporal correlations. Numerical experiments demonstrate the effectiveness of the strategy.     Delete 11:00 - 12:30 : Optimization Techniques Chair:  David Papp,Rutgers University, 640 Bartholomew Rd, Center for Operations Research, Piscataway 08854, United States of America, dpapp@rutcor.rutgers.edu Title:  Discrete Risk Optimization Models with p-Order Conic Constraints   Lead:  Policarpio Soberanis,University of Iowa, 3131 Seamans Center, Iowa City IA 52242, United States of America, policarpio-soberanis@uiowa.edu   Co-Author:  Pavlo Krokhmal,Assistant Professor, University of Iowa, 3131 Seamans Center, Iowa City IA 52242, United States of America, krokhmal@engineering.uiowa.edu   Abstract:  We present a class of risk-reward optimization models that reduce to integer programming problems with p-order conic constraints. As an illustration, a portfolio optimization application is considered. The proposed solution approach relies on the developed polyhedral approximations for p-order cones. Computational results of portfolio optimization case studies are discussed.     Title:  Robust Regression using Semi-definite Programming   Lead:  Dzung Nguyen,UIUC, 205 North Mathews Ave., Urbana IL 61801, United States of America, ngtridung@yahoo.com   Abstract:  The robust regression problem is often formulated as the least trimmed squares (LTS) problem where we want to find the best subset of observations with the smallest sum of squared residuals. We show the LTS problem is equivalent to a concave minimization problem, which is very hard to solve. We resolve this difficulty by introducing the maximum trimmed squares problem that finds the worst subset of observations. This problem can be transformed into an SDP problem that can be solved efficiently.     Title:  Shape-constrained Estimation of Multivariate Functions via Semidefinite Programming   Lead:  David Papp,Rutgers University, 640 Bartholomew Rd, Center for Operations Research, Piscataway 08854, United States of America, dpapp@rutcor.rutgers.edu   Co-Author:  Farid Alizadeh,Professor, Rutgers University, 640 Bartholomew Road, Piscataway NJ 08854, United States of America, alizadeh@rutcor.rutgers.edu   Abstract:  Function estimation problems often involve functions that must satisfy certain shape constraints, which usually reduce to the nonnegativity of linear functionals of the estimated function. If the estimator is a multivariate spline, then these constraints are intractable; hence we consider tractable restrictions involving weighted-sum-of-squares cones. We present theoretical justifications of the proposed method, applications, and computational results.     Delete 11:00 - 12:30 : Joint Session Optimization/Minority Issues: Branchwidth, Treewidth, and Integer Programming! Oh, my!! Chair:  Illya Hicks,Associate Professor, Rice University, 6100 Main St. - MS 134, Houston TX 77005-1892, United States of America, ivhicks@rice.edu Title:  A Branch-and-price-and-cut Approach for Determining the Optimal Bramble Number   Lead:  Sibel Sonuc,University of Florida, PO Box 116595, Gainesville, United States of America, sibel.bilge@ufl.edu   Co-Author:  Cole Smith,Associate Professor, University of Florida, PO Box 116595, Gainesville FL 32611, United States of America, j.cole.smith@gmail.com   Abstract:  In this study, we investigate a methodology to find the bramble number of a given graph. We investigate a branch-and-price-and-cut algorithm to solve this problem. However, both columns and rows must be added to the master problem at each iteration. We model the not-yet-generated rows as hidden rows with unknown associated dual values. A dual value of zero is valid for these rows, but the generation of degenerate (and useless) columns can be avoided by examining a space of optimal dual values.     Title:  Near Optimal Branch Decompositions for Linear Matroids   Lead:  Illya Hicks,Associate Professor, Rice University, 6100 Main St. - MS 134, Houston TX 77005-1892, United States of America, ivhicks@rice.edu   Co-Author:  Jing Ma,graduate student, Stanford University, jm11.rice@gmail.com   Abstract:  The notions of branch decompositions and branchwidth were introduced by Robertson and Seymour in their proof of the Graph Minors Theorem. Later, Dharmatilake extended these notions to matroids. In this vein, we present a polynomial-time heuristic to compute near optimal branch decompositions of linear matroids. The heuristic utilizes diffusion regularization and techniques of partially-labeled classification. We offer some theoretical and computational results.     Title:  Computational Results on the Cunningham-Geelen Algorithm for solving Integer Programs   Lead:  Susan Margulies,Post-doc, Rice University, 6100 Main Street, Houston TX 77005, United States of America, susan.margulies@rice.edu   Co-Author:  Illya Hicks,Associate Professor, Rice University, 6100 Main St. - MS 134, Houston TX 77005-1892, United States of America, ivhicks@rice.edu   Jing Ma,Graduate Student, Rice University, 6100 Main St. - MS 134, Houston TX 77005, United States of America, Jing.Ma@rice.edu   Abstract:  Consider the integer program max(c^Tx : Ax = b, x >= 0,) where A is non-negative and the column-matroid of A (denoted by M(A)) has constant branch width. Cunningham and Geelen introduced a pseudo-polynomial time algorithm for solving this integer program that takes a branch decomposition T of M(A) as input. We report on computation results of a C++ implementation of this algorithm.     Delete 12:45 - 14:15 : Stochastic Optimization I Chair:  Medha Dhurandhar,Head & Program Coordinator, Centre for Development of Advanced Computing, Pune University Campus, Ganeshkhind Rd., Pune 411007, India, mdhurandhar@gmail.com Title:  Pattern Discrete and Mixed Hit-and-run for Global Optimization and Mixed Integer Programming   Lead:  Huseyin Mete,PhD Candidate / Research Assistant, University of Washington, Industrial and Systems Engineering, Box 352650, Seattle WA 98195-2650, United States of America, mete@u.washington.edu   Co-Author:  Zelda Zabinsky,Professor, University of Washington, Industrial and Systems Engineering, Box 352650, Seattle WA 98195-2650, United States of America, zelda@u.washington.edu   Abstract:  We develop Markov chain Monte Carlo samplers for neighborhood generation in global optimization based on Hit-and-Run. We define Sphere and Box Biwalks that are pattern based and easily implemented for discrete and mixed domains bounded by linear constraints. The Markov chains converge to a target distribution over sets in a polytope. We derive performance bounds on mixing time for special cases that are polynomial in dimension. We discuss impact to Improving Hit-and-Run for special cases of MIP.     Title:  Convex Bernstein Polynomial Approximation on the Stochastic Resource Allocation Problem   Lead:  Lijian Chen,Assistant Professor, University of Louisville, 5241 Craigs Creek Dr, Louisville KY 40241, United States of America, lijian.chen@louisville.edu   Abstract:  We propose to solve large scale network resource allocation problem with stochastic demands by fitting the expected objective by a sequence of convex polynomials with non-negative coefficients and thereafter apply Newton's method on it. We apply a Weierstrass-like theorem and showed that if we can evaluate the expected appraisal function, we will reach the arbitrary optimal by solving the new problem. When experimental errors exist, we have the probabilistic convergence instead.     Title:  Semidefinite Programming Under Uncertainty   Lead:  Yuntao Zhu,Arizona State University, PO Box 37100, Phoenix AZ 85069-7100, United States of America, yuntao.zhu@asu.edu   Co-Author:  K. A. Ariyawansa,Washington State University, Department of Mathematics, Pullman WA 99164, United States of America, ari@wsu.edu   Abstract:  In this talk we introduce a new optimization paradigm termed Stochastic Semidefinite Programming (SSDP) for dealing with uncertain data in applications leading to semidefinite programs. The formulation of SSDP is stressed as well as applications and solving algorithms.     Title:  The Distribution Problem for Stochastic Linear Programming   Lead:  Sara Nourazari,PhD Student, University of Oklahoma, 202 W. Boyd, Rm 124, Norman OK 73019, United States of America, sara.n@ou.edu   Co-Author:  Hillel Kumin,University of Oklahoma, 202 W. Boyd, Rm 124, Norman OK 73019, United States of America, hkumin@ou.edu   Abstract:  Numerical results for the distribution problem for stochastic linear programming problem are very difficult to obtain. Using a Jacobian transformation we obtain a new closed form expression for the cumulative distribution function (CDF) of the maximum value of the objective function in a stochastic linear programming problem in the case where either the objective function coefficients or the right-hand side coefficients are given by known probability distributions.     Title:  Efficient Algorithm Constructing Minimum Path Factors of Digraphs Minimizing Operational Cost   Lead:  Medha Dhurandhar,Head & Program Coordinator, Centre for Development of Advanced Computing, Pune University Campus, Ganeshkhind Rd., Pune 411007, India, mdhurandhar@gmail.com   Abstract:  We present an efficient algorithm constructing all minimum path factors of acyclic digraphs modeling real world problems. This involves generation of pseudo-bipartite subdigraphs & edge-contraction along maximum matching in the subdigraphs. This methodology gives flexible, pragmatic solutions under realistic constraints for a wide variety of Industrial applications like optimal resource scheduling for Project Management, Logistics/Transport industry, Assembly line processing in Manufacturing     Delete 14:45 - 16:15 : Simulation and Optimization Chair:  Jong-hyun Ryu,Purdue University, 315 N. Grant St., West Lafayette IN 47906, United States of America, ryuj@purdue.edu Title:  Large Scale Production Optimization using a MINLP Approach   Lead:  Kashif Rashid,Research Scientist, Schlumberger-Doll Research, One Hampshire Street, Cambridge MA 02319, United States of America, krashid@slb.com   Co-Author:  Benoit Couet,Scientific Advisor/Program Manager, Schlumberger-Doll Research, One Hampshire Street, Cambridge MA 02139, United States of America, bcouet@slb.com   Suleyman Demirel,PhD Candidate, University of Michigan, 701 Tappan Ave, Ann Arbor MI 48104, United States of America, sdemirel@umich.edu   Abstract:  A methodology to treat large-scale surface network models for hydrocarbon revenue maximization in the presence of various operating constraints is presented. The control variables include continuous gas-lift injection and discrete or continuous sub-surface chokes. As each simulation is costly and derivatives are unavailable, an efficient technique for handling such MINLP problems is proposed.     Title:  Kriging Metamodeling in Simulation Optimization   Lead:  Mehdi Zakerifar,PhD Student, University of Louisville, Dept. of Industrial Engineering, University of Louisville, Louisville KY 40292, United States of America, m0zake01@louisville.edu   Co-Author:  Gerald Evans,Professor, University of Louisville, Dept. of Industrial Engineering, University of Louisville, Louisville KY 40292, United States of America, gwevan01@louisville.edu   Abstract:  Kriging is a geostatistical interpolation method that predicts unknown values of a random function based on a weighted linear combination of all values already observed. This technique has been widely used in areas as diverse as geology, logistics, and manufacturing. In our approach, we will define the Kriging metamodeling as a tool for simulation optimization; and then we will compare Kriging methodology with the other standard interpolation methods within an experiment.     Title:  A Screening Model to Explore Planning Decisions in Manufacturing Systems under Demand Uncertainty   Lead:  Yingxia Yang,Massachusetts Institute of Technology, 550 Memorial Dr, Apt.11b3, Cambridge MA 02139, United States of America, yingxia@mit.edu   Co-Author:  Randolph Kirchain,Associate Professor, MIT, 77 Massachusetts Ave, E38-432, Cambridge MA 02139, United States of America, kirchain@mit.edu   Richard Roth,Director, Materials Systems Lab, MIT, 77 Massachusetts Ave, E38-435, Cambridge MA 02139, United States of America, rroth@mit.edu   Abstract:  This research develops a method to design manufacturing systems for uncertainty. It couples a screening model to identify designs with an evaluation model to evaluate identified solutions. By using the adaptive One Factor At a Time method with a Response Surface method and simulation-based linear optimization, the screening model efficiently explores a large decision space that is computationally intractable for conventional optimization, which is demonstrated by a case study.     Title:  Multiobjective Simulation Optimization Using Adaptive Weighted Sum Method   Lead:  Jong-hyun Ryu,Purdue University, 315 N. Grant St., West Lafayette IN 47906, United States of America, ryuj@purdue.edu   Co-Author:  Sujin Kim,Assistant Professor, National University of Singapore, 117576, Singapore, Singapore, iseks@nus.edu   Hong Wan,Assistant Professor, Purdue University, 315 N. Grant Street, West Lafayette IN 47907, United States of America, hwan@purdue.edu   Abstract:  This work proposes a new method for approximating Pareto front of multi-objective simulation optimization problems where the explicit forms of the objective functions are not available. The new scheme iteratively approximates the objective functions using metamodels and searches for the points on the Pareto front using weighted sum method. The numerical results show that the proposed algorithm efficiently generates evenly distributed points for various types of Pareto fronts.     Delete 16:30 - 18:00 : Threat Detection for Homeland Security Chair:  Ron Billings,CEO, FABQ.com, 280 NE Cambridge Circle, Corvallis OR 97330, United States of America, profbillings@gmail.com Title:  Sensor Network Optimization for Threat Detection   Lead:  Anton Molyboha,Postdoctoral Associate, Stevens Institute of Technology, Castle Point on Hudson, Hoboken NJ 07030, United States of America, amolyboh@stevens.edu   Co-Author:  Michael Zabarankin,Assistant Professor, Stevens Institute of Technology, Castle Point on Hudson, Department of Mathematical Sciences, Hoboken NJ 07030, United States of America, mzabaran@stevens.edu   Abstract:  A Markov chain approach to optimal coverage problem for threat detection by a sensor network in a noisy environment has been developed. The approach has been illustrated in application to diver detection in an urban harbor.     Title:  Identifying Vulnerabilities Using Sensors Information   Lead:  Monica M. De Marchi,Dra, Institute for Advanced Studies/General Command for Aerospace Technology, Rod dos Tamoios, km 5,5, Sao Jose dos Campos SP 12.228-001, Brazil, monica@ieav.cta.br   Co-Author:  Maria Jose P. Lamosa,Dra, Institute for Advanced Studies/General Command for Aerospace Technology, Rod dos Tamoios, km 5,5, Sao Jose dos Campos SP 12.228-001, Brazil, maju@ieav.cta.br   Carmen Lucia R. Santos,Dra, Institute for Advanced Studies/General Command for Aerospace Technology, Rod dos Tamoios, km 5,5, Sao Jose dos Campos SP 12.228-001, Brazil, carmenl@ieav.cta.br   Abstract:  In this work methods of optimization are used to support the decision making process by giving information about the coverage area by sensors such as radars and cellular antennas. The aim is to provide indicators to help the decision makers to identify vulnerabilities in a given area. Some examples of scenarios where the research could be applied and the methodology used until now are presented.     Title:  Discovering Radicalization Using Social Movement Theory, Argumentation Theory and Systems Dynamics   Lead:  Dennis Engi,Professor, New Mexico State University, Box 30001/MSC 4230, Las Cruces NM, United States of America, engi@nmsu.edu   Abstract:  The research described in this paper enabled analysts to develop a conceptual framework for understanding, recognizing, and anticipating the origins, dynamic mechanisms, perceptions, and social structures of social reform movements in the homeland and in Diaspora communities. The authors propose that Social Movement Theory combined with Systems Dynamics and Argumentation Theory can provide an extensible foundation for helping anticipate and potentially prevent terrorist actions.     Title:  Maritime Simulation Analysis of Radiological and Nuclear Threats   Lead:  Daniel Faissol,Postdoctoral Researcher, Lawrence Livermore National Laboratory, 7000 East Ave., L-227, Livermore CA 94550, United States of America, dfaissol@llnl.gov   Co-Author:  Matthew Dombroski,Lawrence Livermore National Laboratory, 7000 East Ave., L-229, Livermore, United States of America, dombroski2@llnl.gov   Thomas Edmunds,Lawrence Livermore National Laboratory, 7000 East Av., L-184, Livermore, United States of America, edmunds2@llnl.gov   Yiming Yao,Lawrence Livermore National Laboratory, 7000 East Ave., L-390, Livermore, United States of America, yao3@llnl.gov   Abstract:  We summarize simulation analysis of radiological and nuclear threats from small vessels that are directed towards, or transit, U.S. ports. Due to the complex dynamics of search patrols in the harbor, benign vessels that must be screened, radiation transport phenomena, detection algorithms and concepts of operations (CONOPS), a simulation model is required to evaluate the efficacy of a given countermeasure architecture and enumerate design trade-offs.     Title:  The Telegraph Patrol: Optimal Strategies for the Use of Randomness in Patrolling and Scanning   Lead:  Ron Billings,CEO, FABQ.com, 280 NE Cambridge Circle, Corvallis OR 97330, United States of America, profbillings@gmail.com   Abstract:  Often a fixed path needs to be patrolled (by a ship, aircraft, ground vehicle, or video camera), but the movement along the path needs to be difficult for a potential infiltrator to predict. If the patroller reverses direction according to a Poisson process, this gives the least information to an observer. We analyze infinite lines, closed loops, or open line segments (and discuss more complicated scenarios); and derive optimal values for reversal rates.     Delete 16:30 - 18:00 : Robust Optimization Chair:  Apostolos Fertis,PhD Candidate, MIT, 70 Pacific Str, Apt # 927, Cambridge MA 02139, United States of America, afertis@mit.edu Title:  Using Robust Optimization in Banking Industries   Lead:  Kevin Fleming,Scientist I, FICO, 200 Smith Ranch Road, San Rafael CA 94903, United States of America, kevinfleming@fico.com   Co-Author:  Brendan Del Favero,Senior Scientist, FICO, 200 Smith Ranch Road, San Rafael CA 94903, United States of America, BrendanDelFavero@fico.com   Frank Elliott,Principal Scientist, FICO, 3661 Valley Centre Drive, San Diego CA 92130, United States of America, FrankElliott@fico.com   Alkis Vazacopoulos,Vice President, FICO, 202 Parkway, Harrington Park NJ 07640, United States of America, alkisvazacopoulos@fico.com   Abstract:  Robust optimization refers to optimization with non-probabilistic uncertainty in the coefficients of the objective function and global constraints. These problems arise in new or changing markets because of the lack of historical data necessary to characterize the uncertainty as a probability. We present an implementation of robust linear programming that improves on previous techniques in both running time and optimality, and can be used to solve real, current problems in banking industries.     Title:  Robust Optimization in AIMMS   Lead:  Marcel Hunting,Paragon Decision Technology, Schipholweg 1, Haarlem 2034 LS, Netherlands, Marcel.Hunting@aimms.com   Abstract:  The AIMMS modeling language will be extended such that a robust optimization model can be generated from any given deterministic model, without the need to reformulate the deterministic model. By only supplying additional attributes for selected parameters, variables and constraints, AIMMS can generate both a deterministic and robust optimization model from the same formulation.     Title:  A New, Intuitive and Industrial Scale Approach Towards Uncertainty in Supply Chains   Lead:  Abhilasha Aswal,Associate Technical Evangelist, Infosys Technologies Limited, 26/C Electronics City, Hosur Road, Bangalore KA 560100, India, abhilasha_aswal@infosys.com   Co-Author:  G. N. Srinivasa Prasanna,International Institute of Information Technology Bangalore, 26/C Electronics City, Hosur Road, Bangalore KA 560100, India, gnsprasanna@iiitb.ac.in   Abstract:  This unique & industrial-scale robust-optimization approach bounds parameters in polytopes built from economically meaningful linear constraints. A set of parameter assumptions (SA) is a polytope. Polytope relational algebra determines if different SAs are the same, totally different, or have common assumptions. SA information content is polytope volume, enabling comparison/equivalencing of SA's based on information-content, comparison of information content of output w.r.t input, etc.     Title:  An Operational Learning Approach to Portfolio Optimization   Lead:  Ankit Jain,U.C. Berkeley, Berkeley CA 94709, ankit@ieor.berkeley.edu   Co-Author:  Andrew Lim,Associate Professor, University of California (Berkeley), IEOR Department, 4177 Etcheverry Hall, University of California, Berkeley CA 94720-1777, United States of America, lim@ieor.berkeley.edu   J. George Shanthikumar,UC Berkeley, Berkeley, United States of America, jgshant@ieor.berkeley.edu   Abstract:  We consider mean variance portfolio optimization problem. Classical approaches are known to perform poorly out of sample in the presence of estimation error. We introduce a new approach that guarantees a better out of sample performance than classical one. We explore the link between our approach and minimum norm portfolio. We reformulate the problem as a semidefinite program and show numerical results.     Title:  Robust Maximum Likelihood   Lead:  Apostolos Fertis,PhD Candidate, MIT, 70 Pacific Str, Apt # 927, Cambridge MA 02139, United States of America, afertis@mit.edu   Co-Author:  Dimitris Bertsimas,Boeing Professor of Operations Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Bldg. E40-149, Cambridge MA 02139, United States of America, dbertsim@mit.edu   Omid Nohadani,MIT, 77 Massachusetts Ave, E40, Cambridge MA 02139, United States of America, nohadani@mit.edu   Abstract:  We use robust optimization to define robust maximum likelihood estimators that deal with uncertainty. We model errors both as deterministic and stochastic variables. In case of stochastic variables, we prove that the resulting optimization over measures problem has the same solution as an optimization over deterministic decision variables problem. We develop efficient algorithms to compute the robust estimators for the multivariate normal distribution and show their effectiveness numerically.     Delete 16:30 - 18:00 : Algorithmic Developments in Stochastic Optimization II Chair:  Nan Kong,Assistant Professor, Purdue University, West Lafayette IN 47907, United States of America, nkong@purdue.edu Co-Chair:  Lewis Ntaimo,Assistant Professor, Texas A&M University, 3131 TAMU, College Station TX 77843, United States of America, ntaimo@tamu.edu Title:  Fenchel Decomposition for Stochastic Integer Programming   Lead:  Lewis Ntaimo,Assistant Professor, Texas A&M University, 3131 TAMU, College Station TX 77843, United States of America, ntaimo@tamu.edu   Abstract:  In this talk, we present a new cutting plane method for two-stage stochastic integer programs called Fenchel decomposition (FD). FD is based on a new class of valid inequalities termed, FD cuts, which are based on Fenchel cutting planes in integer programming. We consider FD cuts based on both the first- and second-stage variables, and based only on the second-stage variables. Preliminary computational results with instances from the literature will be presented.     Title:  Risk Classification Approach Through Convex Optimization   Lead:  Vladimir Bugera,Kammerdiner Consulting, 16031 N 31st AVE, Phoenix AZ 85053, United States of America, vladimir@bugera.com   Co-Author:  Stan Uryasev,University of Florida, Dept. of Industrial and Systems Engineer, 303 Weil Hall, P.O. Box 116595, Gainesville FL 32611-6595, United States of America, uryasev@ufl.edu   Abstract:  We consider an optimization approach for multi-class classification. The optimization problem is formulated as minimization of a penalty function built with quadratic separating functions. It is reduced to linear programming problem for finding parameters of optimal classification. We apply this approach to evaluate risks in several financial applications, and compare our methodology with conventional techniques.     Title:  Algorithmic Development on a Class of Stochastic Quadratic 0-1 Programs   Lead:  Nan Kong,Assistant Professor, Purdue University, West Lafayette IN 47907, United States of America, nkong@purdue.edu   Co-Author:  Zhen Zhu,Purdue University, West Lafayette IN 47907, zzhu@purdue.edu   Abstract:  We, in this paper, study a class of two-stage stochastic quadratic programming problems with 0-1 variables on both stages. We apply a branch-and-fix framework and adapt various decomposition methods, together with a number of linearization techniques within this framework. Analytical and computational results will be presented for the comparison of different methods.