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IEMS 459: Convex Optimization


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Prerequisites

450-2 is recommended but not required

Description

The course will take an in-depth look at the main concepts and algorithms in convex optimization. The goal is to develop expert knowledge in duality and in the design and analysis of algorithms for convex optimization. Emphasis is on proof techniques and understanding the mechanisms that drive convergence of algorithms.

Topics

  • Lagrangian and Fenchel Duality
  • Gradient projection and proximal algorithms
  • Incremental gradient methods and randomization
  • Coordinate descent and accelerated gradient methods
  • Sub-differential calculus and sub-gradient methods
  • Applications

Textbook : Convex Optimization Algorithms by Bertsekas