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| Grading |
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55% homeworks, 35% final project, 10% participation (tentative)
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| Homework |
- Weekly homework assignments (8 homework sets), which involve some MATLAB programming. There will be a Final Project, and no final exam.
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| Course Description |
The course concentrates on recognizing and solving convex optimization problems
that arise in engineering and sciences. Syllabus includes:
- Basics of convex analysis: Convex sets, functions, and optimization problems.
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Optimization theory: Least-squares, linear, quadratic, geometric and semidefinite programming, and
other problems. Optimality conditions, duality theory, and applications.
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Application examples from signal processing, control, communications, statistics, networks, finance and economics,
engineering design.
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| Course Objectives |
- to give students the theoretical tools and training to recognize convex optimization problems,
- to present the underlying theory of such problems, concentrating on mathematical results that are useful in computation,
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to give students experience in solving these problems, and
the background required to use the methods in their own research work.
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| Textbook and Optional References (reserved in the Engineering Library) |
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The main textbook is
Optional books that serve as secondary references:
General
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A. Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications.
MPS/SIAM Series on Optimization.
Theory
- D. Bertsekas, A. Nedic, A. Ozdaglar, Convex Analysis and Optimization. Athena Scientific.
- D. Bertsekas, Convex Optimization Theory. Athena Scientific.
- J. Borwein, A. Lewis, Convex Analysis and Nonlinear Optimization: Theory and Examples. Springer-Verlag.
- D. Luenberger, Linear and Nonlinear Programming, Addison-Wesley.
Algorithms
We won't cover algorithms in this course, but here's a good book:
- J. Nocedal and S. Wright, Numerical Optimization, Springer.
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