Stanford EE364a Lecture 5: The Lagrangian, Dual Function & Slater's Condition
Samsung Professor in the School of Engineering
Why the Lagrange dual problem is ALWAYS convex even when the primal is nonconvex—plus weak duality bounds, Slater's constraint qualification, and strong duality.
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on the different problems that are included within convex optimization 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 4: Convex Optimization Problems — LP, QP, SOCP & SDP
Next · Lecture 6Stanford EE364a Lecture 6: Saddle-Point Interpretation, KKT Conditions & Perturbation
