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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 16: Equality Constrained Minimization (EE364a)

Stephen BoydStanford University

Professor

1:13:59·2008·graduate level

This lecture focuses on minimization problems with equality constraints, introducing Lagrange multipliers and KKT conditions for finding optimal solutions. Practical applications in electrical engineering are also explored.

EngineeringConvex OptimizationSignal Processing & RoboticsConstrained OptimizationLagrange Multipliers

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how equality constrained minimization is utilized in electrical engineering 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 15

Lecture 15: Unconstrained Minimization (EE364a)

Next · Lecture 17

Lecture 17: Equality Constrained Minimization Continued (EE364a)

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