Learn · Stanford Engineering Everywhere

CourseEE364B18 lectures21 h 58 min

Convex Optimization II

Stephen Boyd · Stanford University

Start with lecture 1 At Stanford Engineering Everywhere

About

Continuation of Convex Optimization I. 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. Course requirements include a substantial project.

Prerequisites: Convex Optimization I

Lectures

  1. Course Logistics, Course Organization, Course Topics, Subgradients, Basic Inequality, Subgradient Of A Function, Subdifferential, Subgradient Calculus, Some Basic Rules (For Subgradient Calculus), Pointwise Supremum, Weak Rule For… Lecture 1 · 1 h 2 min · Video
  2. Recap: Subgradients, Subgradients And Sublevel Sets, Quasigradients, Optimality Conditions – Unconstrained, Example: Piecewise Linear Minimization, Optimality Conditions – Constrained, Directional Derivative And Subdifferential, Descent… Lecture 2 · 1 h 7 min · Video
  3. Convergence Proof, Stopping Criterion, Example: Piecewise Linear Minimization, Optimal Step Size When F* Is Known, Finding A Point In The Intersection Of Convex Sets, Alternating Projections, Example: Positive Semidefinite Matrix… Lecture 3 · 1 h 15 min · Video
  4. Project Subgradient For Dual Problem, Subgradient Of Negative Dual Function, Example (Strictly Convex Quadratic Function Over Unit Box), Subgradient Method For Constrained Optimization, Convergence, Example: Inequality Form LP, Stochastic… Lecture 4 · 1 h 19 min · Video
  5. Stochastic Programming, Variations (Of Stochastic Programming), Expected Value Of A Convex Function, Example: Expected Value Of Piecewise Linear Function, On-Line Learning And Adaptive Signal Processing, Example: Mean-Absolute Error… Lecture 5 · 1 h 16 min · Video
  6. Addendum: Hit-And-Run CG Algorithm, Maximum Volume Ellipsoid Method, Chebyshev Center Method, Analytic Center Cutting-Plane Method, Extensions (Of Cutting-Plane Methods), Dropping Constraints, Epigraph Cutting-Plane Method, PWL Lower… Lecture 6 · 1 h 12 min · Video
  7. Example: Piecewise Linear Minimization, ACCPM With Constraint Dropping, Epigraph ACCPM, Motivation (For Ellipsoid Method), Ellipsoid Algorithm For Minimizing Convex Function, Properties Of Ellipsoid Method, Example (Using Ellipsoid… Lecture 7 · 1 h 14 min · Video
  8. Recap: Ellipsoid Method, Improvements (To Ellipsoid Method), Proof Of Convergence, Interpretation Of Complexity, Deep Cut Ellipsoid Method, Ellipsoid Method With Deep Objective Cuts, Inequality Constrained Problems, Stopping Criterion,… Lecture 8 · 1 h 11 min · Video
  9. Comments: Latex Typesetting Style, Recap: Primal Decomposition, Dual Decomposition, Dual Decomposition Algorithm, Finding Feasible Iterates, Interpretation, Decomposition With Constraints, Primal Decomposition (With Constraints)… Lecture 9 · 1 h 10 min · Video
  10. 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… Lecture 10 · 1 h 17 min · Video
  11. Sequential Convex Programming, Methods For Nonconvex Optimization Problems, Sequential Convex Programming (SCP), Basic Idea Of SCP, Trust Region, Affine And Convex Approximations Via Taylor Expansions, Particle Method, Fitting Affine Or… Lecture 11 · 1 h 16 min · Video
  12. Recap: 'Difference Of Convex' Programming, Alternating Convex Optimization, Nonnegative Matrix Factorization, Comment: Nonconvex Methods, Conjugate Gradient Method, Three Classes Of Methods For Linear Equations, Symmetric Positive… Lecture 12 · 1 h 13 min · Video
  13. Recap: Conjugate Gradient Method, Recap: Krylov Subspace, Spectral Analysis Of Krylov Sequence, A Bound On Convergence Rate, Convergence, Residual Convergence, CG Algorithm, Efficient Matrix-Vector Multiply, Shifting, Preconditioned… Lecture 13 · 1 h 15 min · Video
  14. Methods (Truncated Newton Method), Convergence Versus Iterations, Convergence Versus Cumulative CG Steps, Truncated PCG Newton Method, Truncated Newton Interior-Point Methods, Network Rate Control, Dual Rate Control Problem, Primal-Dual… Lecture 14 · 1 h 13 min · Video
  15. Recap: Example: Minimum Cardinality Problem, Interpretation As Convex Relaxation, Interpretation Via Convex Envelope, Weighted And Asymmetric L_1 Heuristics, Regressor Selection, Sparse Signal Reconstruction, L_1-Norm Methods For… Lecture 15 · 1 h 3 min · Video
  16. Model Predictive Control, Linear Time-Invariant Convex Optimal Control, Greedy Control, 'Solution' Via Dynamic Programming, Linear Quadratic Regulator, Finite Horizon Approximation, Cost Versus Horizon, Trajectories, Model Predictive… Lecture 16 · 1 h 19 min · Video
  17. Stochastic Model Predictive Control, Causal State-Feedback Control, Stochastic Finite Horizon Control, 'Solution' Via Dynamic Programming, Independent Process Noise, Linear Quadratic Stochastic Control, Certainty Equivalent Model… Lecture 17 · 1 h 17 min · Video
  18. Announcements, Recap: Branch And Bound Methods, Basic Idea, Unconstrained, Nonconvex Minimization, Lower And Upper Bound Functions, Branch And Bound Algorithm, Comment: Picture Of Branch And Bound Algorithm In R^2, Comment: Binary Tree,… Lecture 18 · 1 h 19 min · Video

Syllabus and materials

At Stanford Engineering Everywhere, as the course publishes them.

Credits and licence

Institution
Stanford University
Department
Electrical Engineering
Teacher
Stephen Boyd
Level
Undergraduate
Language
English
Subjects
Engineering
Source
Stanford Engineering Everywhere
Licence
CC BY-NC-SA 4.0, as Stanford Engineering Everywhere states it. Non-commercial use only. Stanford Engineering Everywhere's terms
Attribution
Stanford Engineering Everywhere, Stanford University. Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, unless otherwise expressly indicated.
Who can watch
Stanford Engineering Everywhere gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed