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1 - 1 of 1 results for: CS 334A: Convex Optimization I

CS 334A: Convex Optimization I (CME 364A, EE 364A)

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. Prerequisite: linear algebra such as EE263, basic probability.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Boyd, S. (PI)
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