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Saddle-Point Interpretation, KKT Conditions & Perturbation

Samsung Professor in the School of Engineering

1:09:19·2008·graduate level

Derives Karush-Kuhn-Tucker optimality conditions, complementary slackness, and shadow-price sensitivity analysis ∂p*/∂uᵢ = -λᵢ*.

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on convex optimization problems 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.

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