Syllabus

The following is a tentative syllabus for the course. Lecture notes will be available here.

Week # Description and Lecture Note Last Updated
1-2 Part 1: Convex Sets, Functions and Optimization Jan 17 (W) 3:40 PM
3-4 Part 2: Gradient and Subgradient Methods for Unconstrained Convex Optimization Jan 29 (M) 3:23 PM
5 Part 3: Projected and Proximal Gradient Methods Feb 5 (M) 3:24 PM
6-7 Part 4: KKT Conditions and Duality Feb 16 (F) 3:26 PM
8 Part 5: Dual-based Methods Feb 25 (Su) 1:18 PM
9 Part 6: Newton's Method Feb 28 (W) 3:35 PM

Daily Schedule

The following is a tentative daily schedule for the course. This page will be updated irregularly.

Date Description
Part 1 Jan 3 (W) Introduction to Convex Optimization 1
Jan 5 (F) Introduction to Convex Optimization 2, Mathematical Preliminaries
Jan 8 (M) Convex Sets
Jan 10 (W) Convex Functions
Jan 12 (F) Convex Optimization 1
Jan 17 (W) Convex Optimization 2
Part 2 Jan 18 (Th) Gradient Methods
Jan 19 (F) Convergence Rate of Gradient Methods 1
Jan 22 (M) Convergence Rate of Gradient Methods 2, Lower Complexity Bounds of Gradient Methods
Jan 24 (W) Accelerated Gradient Methods
Jan 26 (F) Subgradients
Jan 29 (M) Subgradient Methods
Part 3 Jan 31 (W) Projected Subgradient Methods, Mirror Descent Methods
Feb 2 (F) Proximal Operator, Proximal Gradient Methods
Feb 5 (M) Accelerated Proximal Gradient Methods, Proximal Point Methods
Part 4 Feb 7 (W) Optimality Conditions for Linearly Constrained Problems
Feb 9 (F) KKT Conditions 1
Feb 12 (M) KKT Conditions 2
Feb 14 (W) Lagrange Duality 1
Feb 15 (Th) Lagrange Duality 2
Feb 16 (F) Lagrange Duality 3
Part 5 Feb 19 (M) Dual Projected Subgradient Methods, Dual Proximal Gradient Methods
Feb 21 (W) Augmented Lagrangian Methods (Method of Multipliers)
Feb 23 (F) Alternating Direction Method of Multipliers (ADMM)
Part 6 Feb 26 (M) Newton's Method
Feb 28 (W) Newton's Method with Equality and Inequality Constraints
Project Mar 2 (F) Presentation
Mar 5 (M) Presentation