Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how statistical estimation can be used in 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.
Complete Playlist for the Course:
EE 364A Course Website:
Stanford University Channel on YouTube:
Tagged under: science,electrical,engineering,technology,convex,optimization,parametric,distribution,estimation,Gaussian,Laplacian,detection,probability,hypothesis,testing,squares
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