Lecture: Project Subgradient For Dual Problem

Tim Papandreou - Stanford

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Lecture Description

Project Subgradient For Dual Problem, Subgradient Of Negative Dual Function, Example (Strictly Convex Quadratic Function Over Unit Box), Subgradient Method For Constrained Optimization, Convergence, Example: Inequality Form LP, Stochastic Subgradient Method, Noisy Unbiased Subgradient, Stochastic Subgradient Method, Assumptions, Convergence Results, Convergence Proof, Stochastic Programming

Course Description

Continuation of Convex Optimization I.

Topics include: Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications.

from course: Convex Optimization II


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