EEE461 Numerical Optimization
lecture notes: Lecture Notes on Numerical Optimization, Moritz Diehl
course based on: Numerical Optimization, Moritz Diehl
DERS UYGULAMA BELGESİ (SYLLABUS)
weekly schedule
| Week | Part | Topic |
|---|---|---|
| 1 | fundamental concepts | introduction to optimization |
| 2 | fundamental concepts | types of optimization problems |
| 3 | fundamental concepts | convex optimization, Lagrangian function and duality |
| 4 | unconstrained optimization | optimality conditions for unconstrained problems |
| 5 | unconstrained optimization | gradient descent, Newton-type methods |
| 6 | exercises and applications with numerical computing (midterm) | |
| 7 | review and preparation for the exam (midterm) | |
| midterm week | midterm exam | |
| 8 | unconstrained optimization | globalization strategies |
| 9 | unconstrained optimization | calculating derivatives |
| 10 | constrained optimization | optimality conditions for equality constrained problems |
| 11 | constrained optimization | equality constrained optimization algorithms |
| 12 | constrained optimization | optimality conditions for inequality constrained problems |
| 13 | constrained optimization | inequality constrained optimization algorithms |
| 14 | constrained optimization | optimal control |
| 15 | exercises and applications with numerical computing (final) | |
| 16 | review and preparation for the exam (final) |
Resources
Textbooks
An Introduction to Optimization (Chong&Zak)
Convex Optimization (Boyd&Vandenberghe)
Numerical Optimization (Nocedal&Wright)
Constrained Optimization and Lagrange Multiplier Methods (Bertsekas)
Convex Optimization Theory (Bertsekas)
Convex Optimization Algorithms (Bertsekas)
Computational Optimization Open Textbook (Cornell University)
Convex Optimization: Algorithms and Complexity (Bubeck)
Youtube playlists
Introduction to Applied Linear Algebra (Stephen Boyd)
Convex Optimization (Stephen Boyd)
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