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Stanford EE364a: Convex Optimization I →0/22
1

Mathematical Optimization & Why Convexity Is the Watershed

1:20:32
2

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 2: Subgradients Continued (EE364B)

Stephen BoydStanford University

Professor

1:07:27·2008·graduate level

This session continues the exploration of subgradients, examining their calculus rules, optimality conditions, and applications in extending gradient-based methods. It provides a deeper understanding of handling non-smoothness.

MathematicsEngineeringSystems SecurityCalculus & AnalysisConvex OptimizationSubgradient Calculus

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues subgradients.

This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications.

Previous · Lecture 20

Lecture 1: Subgradients (EE364B)

Next · Lecture 22

Lecture 3: Subgradient Methods (EE364B)

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