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Stanford EE364a: Convex Optimization I →0/22
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Mathematical Optimization & Why Convexity Is the Watershed

1:20:32
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Stanford EE364a Lecture 2: Affine Sets, Convex Sets, Cones & Hyperplanes

1:16:51
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Stanford EE364a Lecture 3: Convex Functions — First/Second-Order Conditions & Epigraphs

1:17:14
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Stanford EE364a Lecture 4: Convex Optimization Problems — LP, QP, SOCP & SDP

1:13:38
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Stanford EE364a Lecture 5: The Lagrangian, Dual Function & Slater's Condition

1:16:10
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Stanford EE364a Lecture 6: Saddle-Point Interpretation, KKT Conditions & Perturbation

1:09:19
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Lecture 7 | Convex Optimization I

1:14:37
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Lecture 8 | Convex Optimization I (Stanford)

1:16:30
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Lecture 9: Duality Theory (EE364a)

1:16:36
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Lecture 10: Approximation and Fitting (EE364a)

1:17:55
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Lecture 11: Statistical Estimation (EE364a)

1:17:03
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Lecture 12: Geometric Problems (EE364a)

1:16:05
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Lecture 13: Geometric Problems Continued (EE364a)

1:15:17
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Lecture 14: Numerical Linear Algebra Background (EE364a)

1:10:12
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Lecture 15: Unconstrained Minimization (EE364a)

1:16:45
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Lecture 16: Equality Constrained Minimization (EE364a)

1:13:59
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Lecture 17: Equality Constrained Minimization Continued (EE364a)

1:19:25
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Lecture 18: Interior-Point Methods (EE364a)

1:16:53
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Lecture 19: Advanced Topics and Applications (EE364a)

1:15:02
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Lecture 1: Subgradients (EE364B)

1:01:37
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Lecture 2: Subgradients Continued (EE364B)

1:07:27
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Lecture 3: Subgradient Methods (EE364B)

1:14:45

Lecture 12: Geometric Problems (EE364a)

Stephen BoydStanford University

Professor

1:16:05·2008·graduate level

This lecture introduces the application of convex optimization to geometric problems in engineering, including topics such as extremal volume and minimax problems. Students learn to formulate and solve spatial optimization challenges effectively.

EngineeringConvex OptimizationSignal Processing & RoboticsGeometric OptimizationExtremal Volume

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on geometric problems in the context of electrical engineering and 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.

Previous · Lecture 11

Lecture 11: Statistical Estimation (EE364a)

Next · Lecture 13

Lecture 13: Geometric Problems Continued (EE364a)

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