Stanford EE364a Lecture 3: Convex Functions — First/Second-Order Conditions & Epigraphs
Samsung Professor in the School of Engineering
Covers Jensen's inequality, Hessian positive semidefiniteness ∇²f(x) ⪰ 0, log-sum-exp, matrix-fractional functions, and pointwise supremum rules.
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on convex and concave functions for the course, Convex Optimization I (EE 364A).
Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.
Stanford EE364a Lecture 2: Affine Sets, Convex Sets, Cones & Hyperplanes
Next · Lecture 4Stanford EE364a Lecture 4: Convex Optimization Problems — LP, QP, SOCP & SDP
