CO367/CM442
Fall 2017
Principles of nonlinear continuous optimization, that is, minimizing an
objective function that depends nonlinear and continuously on unknown
variables that satisfy constraints. Convex optimization will be
introduced. Applications to data-mining and machine learning ("data
science") will also be introduced.
Nonlinear Optimization
(
Course Information/Syllabus)
Instructor:
Henry Wolkowicz
(MC6312;;519-888-4567x35589)
- Events/News
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syllabus for W17
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marking for W17
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Judgment in Seattle: A Theorem Goes on Trial: "Comparative Advantage"
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Student Academic Discipline Policy:
Copying assignments is contrary to University policy. You must work on
your assignments on your own. (Late assign. are not accepted.)
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INCs are granted only with serious justification.
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Nonlinear Programming, (MITOPENCOURSEWARE)
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An algorithm that minimizes anything/everything
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You Can Do Anything With a Math Degree
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Time:T-R 4:00--5:20PM (Thurs. Sept. 7 to Thurs. Nov 28, 2017)
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Location: MC4064
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Office Hour:
Henry Wolkowicz: Wed. 3PM and Fri. 2PM
.
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TA: Stefan Sremac; office hour Thurs. 1PM, MC 6011
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Midterm: IN CLASS: Thurs. Oct 19, closed book; no calculators.
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FINAL EXAM: MC6486; date Friday Dec. 8,
11:30-2:00PM; closed book; no calculators
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Marking Scheme:
HW (6-7 assigns) 30%; Midterm 30%; Final 40%
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see
Waterloo LEARN for Assignments/Solutions,
Partial Lecture Notes, etc...
Instructor: Henry Wolkowicz (MC6312;;519-888-4567x35589)
- Events/News
- syllabus for W17
- marking for W17
- Judgment in Seattle: A Theorem Goes on Trial: "Comparative Advantage"
- Student Academic Discipline Policy: Copying assignments is contrary to University policy. You must work on your assignments on your own. (Late assign. are not accepted.)
- INCs are granted only with serious justification.
- Nonlinear Programming, (MITOPENCOURSEWARE)
- An algorithm that minimizes anything/everything
- You Can Do Anything With a Math Degree
-
additional references:
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Nocedal and Wright, Numerical Optimization, 2nd Edition
Boyd and Vandenberghe, Convex Optimization (online).
Numerical Methods for Unconstrained Optimization and Nonlinear Equations, by J. E. Dennis, Jr. and Robert B. Schnabel, see chapters 3,4 for lin. alg/calculus background. - The Mathematics of Nonlinear Programming, (Undergraduate Texts in Mathematics) Anthony L. Peressini, Francis E. Sullivan, J.J. Jr. Uhl Published by Springer (1993-06-17).
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Prerequisites:
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One of CO 250, 352, 255) and MATH 128 with a grade of at least 70% or
MATH 138 or 148.
(Not open to General Mathematics students)
Last Modified: Sunday 3 December 2017
