EE364A - Convex Optimization I

Convex Optimization I is Stephen Boyd's EE364A, published in full on Stanford Engineering Everywhere. It concentrates on recognising and solving the convex optimization problems that arise in engineering: convex sets, functions and problems, the basics of convex analysis, least-squares, linear and quadratic programs, semidefinite programming, minimax and extremal-volume problems, optimality conditions, duality theory and theorems of alternatives, and interior-point methods, with applications in signal processing, control, circuit design, computational geometry, statistics and mechanical engineering. The page asks only for good knowledge of linear algebra; exposure to numerical computing and optimization helps but is not required, and the applications are kept simple.

SEE provides 19 lecture videos with transcripts, a complete set of lecture notes organised by topic, review-session notes, reading assignments keyed to the Boyd and Vandenberghe textbook, problem sets, exams and the CVX software guide. Access is free without registration under a CC BY-NC-SA 4.0 licence, with no credit, certificate or instructor feedback.