Convex Optimization I

Course Description

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.

Course Details

  • Grading Basis: Letter Grade or Credit/No Credit
  • Cross-Listings: CME 364A, CS 334A

Prerequisites

Linear algebra, such as EE 263; basic probability

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