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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 8 | Convex Optimization I (Stanford)

Stephen BoydStanford University

Professor of Electrical Engineering

1:16:30·2008·graduate level

This lecture, part of Stanford's EE364A series, introduces the fundamental concepts of duality in convex optimization, exploring its theoretical underpinnings and practical applications within electrical engineering.

EngineeringConvex OptimizationClassical & Statistical MechanicsSignal Processing & RoboticsDuality Theory

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on duality in the realm of electrical engineering and how it is utilized 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.

Previous · Lecture 7

Lecture 7 | Convex Optimization I

Next · Lecture 9

Lecture 9: Duality Theory (EE364a)

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