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Recap: Subgradients

By Stephen Boyd - Stanford
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Lecture Description

Recap: Subgradients, Subgradients And Sublevel Sets, Quasigradients, Optimality Conditions - Unconstrained, Example: Piecewise Linear Minimization, Optimality Conditions - Constrained, Directional Derivative And Subdifferential, Descent Directions, Subgradients And Distance To Sublevel Sets, Descent Directions And Optimality, Subgradient Method, Step Size Rules, Assumptions, Convergence Results, Aside: Example: Applying Subgradient Method To Abs(X)

Course Description

Related Resources

Transcript   |  Subgradient Methods

Course Index

  1. Basic Rules for Subgradient Calculus
  2. Recap: Subgradients
  3. Convergence Proof, Stopping Criterion
  4. Project Subgradient For Dual Problem
  5. Stochastic Programming
  6. Addendum: Hit-And-Run CG Algorithm
  7. Example: Piecewise Linear Minimization
  8. Recap: Ellipsoid Method
  9. Comments: Latex Typesetting Style
  10. Decomposition Applications
  11. Sequential Convex Programming
  12. Recap: 'Difference Of Convex' Programming
  13. Recap: Conjugate Gradient Method
  14. Methods (Truncated Newton Method)
  15. Recap: Example: Minimum Cardinality Problem
  16. Model Predictive Control
  17. Stochastic Model Predictive Control
  18. Recap: Branch And Bound Methods, Basic Idea, Unconstrained, Nonconvex Minimization