EEC 227C: Convex Optimization and Approximation

Lectures

• Lecture 1 (Introduction to Convex Optimization)

• Lecture 2 (The Complexity of Optimization)

• Lecture 3 (Gradient Descent)

• Lecture 4 (Subgradient Descent Method)

• Lecture 5 (Mirror Descent)

• Lecture 6 (Exponential Gradient Descent)

• Lecture 7 (Mirror Descent)

• Lecture 8 (Lp ball Optimization and Graph Matching)

• Lecture 9 (Lazy Mirror Descent)

• Lecture 10 (Adaptive Mirror Descent and Bundle Method)

• Lecture 11 (Level Method)

• Lecture 12 (Acceleration of Gradient Methods)

• Lecture 13 (Accelerated Gradient Descent)

• Lecture 14 (History of Interior Point Method and Analysis of Newton's Method)

• Lecture 15 (Newton's Method and Self-concordance)

• Lecture 16 (Self-concordant Functions and Newton's Method)

• Lecture 17 (Newton's Method, Path Following and Self-concordant Barrier)

• Lecture 18 (Central Path)

• Lecture 19 (Potential Reduction Interior Point Method)

• Lecture 20 (Primal-Dual Interior Point Methods)

• Lecture 21 (Second Order Methods for General Function Optimization)

• Lecture 22 (Convergence of Cubic Regularization)

• Lecture 23 (Continuing Cubic-Regularized Newton's Method)

• Lecture 24 (Stochastic Gradient Method)

• Lecture 25 (Stochastic Variance Reduced Gradient Method)

• Lecture 26 (Geometrization and Randomized Coordinate Descent)

• Lecture 27 (Smoothing Techniques)