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documentclass[12pt]article
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titleNumerical Methods for Optimization
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author
hrefhttp://orion.math.uwaterloo.ca/ hwolkowi/Henry WolkowiczthanksResearch supported by The Natural Sciences and Engineering
Research Council of Canada.
Email hwolkowicz@uwaterloo.ca
datetoday
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newedenvironmentnotebeginquotesmallsf NJH endquote
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newedenvironmentnoteMbeginquotesmallsf MFA endquote
newedenvironmentnoteHbeginquotesmallsf HW endquote
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newedtheoremnmbrsNumbering[section]
newedtheoremdefi[nmbrs]Definition
newedtheoremexample[nmbrs]Example
newedtheoremprop[nmbrs]Proposition
newedtheoremprob[nmbrs]Problem
newedtheoremlem[nmbrs]Lemma
newedtheoremthm[nmbrs]Theorem
newedtheoremcor[nmbrs]Corollary
newedtheoremrem[nmbrs]Remark
newedtheoremremark[nmbrs]Remark
newedtheoremconj[nmbrs]Conjecture
newedtheoremalg[nmbrs]Algorithm
newedtheoremex[nmbrs]Exercise
newedcountercount
newedcommandZSmathcal Z_S
newedcommandXSmathcal X
newedcommandXSumathcal X_u
newedcommandXSdmathcal X_d
newedcommandXXmathcal X
newedcommandSOmathcal S
newedcommandSOumathcal S
newedcommandSsmathcal S
newedcommandEmathcal E
newedcommandKKmathcal K
newedcommandEEmathcal E
newedcommandFFmathcal F
newedcommandDDmathcal D
newedcommandBBmathcal B
newedcommandGGmathcal G
newedcommandPPmathcal P
newedcommandTTmathcal T
newedcommandScmathcal S_C
newedcommandShmathcal S_H
newedcommandsnnmathcal S_n-1
newedcommandMCbfrm MC
newedcommandMCqbfrm MCQP
newedcommandMCqqbfrm MCQQ
newedcommandMCsdpbfrm MCSDP
newedcommandhsH_sigma
newedcommandSDPbfrm SDP
newedcommandadjrm adj
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newedcommandrankrm rank
newedcommandspanlrm span
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newedcommanddomrm dom
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newedcommandDiagrm Diag
newedcommanddDiagrm dDiag
newedcommandDdiagrm Ddiag
newedcommandconvrm conv
newedcommandgaprm gap
newedcommandoptvalrm optval
newedcommandNucal N
newedcommandRnmathcal R^n
newedcommandSnmathcal S^n
newedcommandRtnmathcal R^scriptsizepmatrixncr 2
newedcommandRtnpmathcal R^scriptsizepmatrixn+1cr 2
newedcommandMnmathcal M^n
newedcommandMSmathcal M_S
newedcommandNSmathcal N_S
newedcommandMSimathcal M_S^-1
newedcommandCpmathcal C
newedcommandpmathcal P
newedcommandppmathcal P_Sn
newedcommandAmathcal A
newedcommandFmathcal F
newedcommandNmathcal N
newedcommandFliftcal F_n
newedcommandbeqbeginequation
newedcommandbetbegintable
newedcommandeeqendequation
newedcommandbeqrbegineqnarray
newedcommandfa forall
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newedcommandsncal S_n
newedcommandhncal H_n
newedcommandgcal G
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newedcommandbprnoindentbf Proof. hspace1 em
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newedcommandvSmatrm vSmat
newedcommandSmatrm S2mat
newedcommandsMatrm sMat
newedcommandhMatrm hMat
newedcommandvsMatrm vsMat
newedcommandkmatrm Mat
newedcommandsdiagrm sdiag
newedcommandKprodotimes
newedcommandKcalcal K
newedcommandHcalcal H
newedcommandtran^t
newedcommandCmathbb C
newedcommandgesemsucceq
newedcommandlesempreceq
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nc proofbf Proof. rm nr
nc Mmncal M_m,n
nc kwqqpQP
nc kwqqpsQPs
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begindocument
bibliographystyleplain
maketitle
begincenter
hrefhttp://www.uwaterloo.ca/University of Waterloo
hrefhttp://www.math.uwaterloo.ca/CandO_Dept/homepage.htmlDepartment of Combinatorics & Optimization
Waterloo, Ontario N2L 3G1, Canada
Research Report CORR 2001-??
endcenter
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bf Key words:
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footnotetext
URL for papers:
hrefhttp://orion.math.uwaterloo.ca/ hwolkowi/henry/reports/ABSTRACTS.htmlhttp://orion.math.uwaterloo.ca/~ hwolkowi/henry/reports/ABSTRACTS.html
par
beginabstract
endabstract
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tableofcontents
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sectionIntroduction
The theory and methods of optimization are in constant use in branches of
e.g. mathematics and engineering.
The aim of this course is to assist the student, researcher, and teacher to
obtain an understanding of the basic theory and the current
state of the art in numerical methods for optimization.
The modern era of Optimization (within Operations Research) can be said
to have begun with Dantzig's simplex method in 1949. Since then the
field has advanced tremendously. In particular, the introduction of
interior point methods has dramatically increased the size of problems
that can be solved.
par
This course will begin with an introduction of a general nonlinear
program (NLP). We then develop the theory and methods for unconstrained
optimization and for solving nonlinear equations. This leads to methods
for solving general NLPs and also to special models such as semidefinite
programming, SDPs.
par
No text will be used. However, an excellent reference is the book:
Numerical Optimization, by Jorge Nocedal, and Stephen Wright, 1999,
Springer Verlag.
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sectionBackground
beginenumerate
item
introduce the general NLP model and its properties
item
outline several applications
endenumerate
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sectionUncontrained Minimization
beginenumerate
item
optimality conditions
item
Algorithms
beginenumerate
item
line search methods: least change secant methods
item
trust region methods
endenumerate
endenumerate
par
sectionContrained Minimization Theory
beginenumerate
item
optimality conditions
item
duality
item
algorithms
beginenumerate
item
interior point methods
item
Sequential Quadratic Programming (SQP) methods
endenumerate
endenumerate
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sectionSpecial Models
beginenumerate
item
Semidefinite Programming and applications
endenumerate
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bibliography.master,.psd,.publs
par
enddocument
Next: About this document ...
Henry Wolkowicz
2002-03-25