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. Leastsquares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interiorpoint 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, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Lall, S. (PI)
;
Pathak, R. (PI)
;
Angeris, G. (TA)
;
Choudhary, D. (TA)
;
Gu, A. (TA)
;
Jiang, Q. (TA)
;
Kim, J. (TA)
;
Mani, N. (TA)
;
Momeni, A. (TA)
;
Pathak, R. (TA)
;
Shah, K. (TA)
;
Song, R. (TA)
;
Tefagh, M. (TA)
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