Convex Optimization I & II (EE 364A&B) Stanford
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Details
Description: (EE364A)Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear
and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control,
analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. (EE364B) Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Exploiting
problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications.
Resources: OpenCourseware from Yale, UC Berkeley, Stanford, MIT along with many of the World's finest University's.
Language: English
Units: 37
Lesson content
- Lec 1 Intro (EE 364A)
- Lec 2 convex sets
- Lec 3 convex and concave functions
- Lec 4 convex functions
- Lec 5 convex optimization problems
- Lec 6 convex optimization problems (cont)
- Lec 7 convex optimization problems 3
- Lec 8 duality in the realm
- Lec 9 duality 2
- Lec 10 approximation and fitting
- Lec 11 statistical estimation
- Lec 12 geometric problems
- Lec 13 geometric problems (cont)
- Lec 14 numerical linear algebra
- Lec 15 unconstrained minimization
- Lec 16 equality constrained minimization
- Lec 17 equality constrained minimization
- Lec 18 interior-point methods
- Lec 19
- Lec 1 Intro & Subgradients (EE364B)
- Lec 2 subgradients cont.
- Lec 3 subgradient methods
- Lec 4 subgradient methods for constrained problems
- Lec 5 stochastic programing, localization, cutting-plane methods
- Lec 6 localization, cutting-plane methods, Analytic center cutting-plane
- Lec 7 Analytic center cutting-plane method, Ellipsoid methods
- Lec 8 primal and dual decomposition
- Lec 9 primal and dual decomposition
- Lec 10 Decomposition Applications
- Lec 11 Sequential Convex Programming
- Lec 12 Sequential Convex, Conjugate Gradient Methods
- Lec 13 Conjugate Gradient Methods, Truncated Newton Method.
- Lec 14 Truncated Newton Method, L1-Norm Methods Convex-Cardinality Problems.
- Lec 15 L1 Methods for Convex-Cardinality Problems
- Lec 16 Model Predictive Control
- Lec 17 Stochastic Model Predictive Control, Branch-and-bound methods
- Lec 18 Branch-and-bound methods
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