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Decomposition Applications

By Stephen Boyd - Stanford
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Lecture Description

Decomposition Applications, Rate Control Setup, Rate Control Problem, Rate Control Lagrangian, Aside: Utility Functions, Rate Control Dual, Dual Decomposition Rate Control Algorithm, Generating Feasible Flows, Convergence Of Primal And Dual Objectives, Maximum Capacity Violation, Single Commodity Network Flow Setup, Network Flow Problem, Network Flow Lagrangian, Network Flow Dual, Recovering Primal From Dual, Dual Decomposition Network Flow Algorithm, Electrical Network Analogy, Example: Minimum Queueing Delay, Optimal Flow, Convergence Of Dual Function, Convergence Of Primal Residual, Convergence Of Dual Variables, Aside: More Complicated Problems

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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