edX MOOC / Non-credit 0

Convex Optimization

Name: Convex Optimization
Availability: InStock

Stanford University

About this course

This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: 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, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

74/100

CourseAsk score

What the provider tells you: 31/45
Who stands behind it: 35/35
How complete the listing is: 8/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

understanding convex sets and functions
formulating and solving least-squares and quadratic programming problems
applying interior-point methods
utilizing duality theory in optimization

Course objectives

recognize and classify convex optimization problems
solve practical optimization challenges in diverse fields
analyze optimality conditions and their implications

Machine Learning #machine learning #least squares #statistics #linear programming #finance #mechanical engineering #signal processing #convex optimization #optimization theory #dual theory #interior-point methods #quadratic programming #semidefinite programming #control engineering

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