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 19 (F) 3:40 PM
5 Part 3: Projected and Proximal Gradient Methods
6-7 Part 4: KKT Conditions and Duality
8-9 Part 5: Dual-based Methods
10 Part 6: Newton and Quasi-Newton Methods

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) Subgradient Methods
Part 3 Jan 29 (M) Projected (Sub)Gradient Methods, Mirror Descent Methods
Jan 31 (W) Proximal Operator, Proximal Gradient Methods
Feb 2 (F) Accelerated Proximal Gradient Methods, Proximal Point Methods
Part 4 Feb 5 (M) KKT Conditions 1
Feb 7 (W) KKT Conditions 2
Feb 9 (F) Duality 1
Feb 12 (M) Duality 2
Feb 14 (W) Duality 3
Part 5 Feb 16 (F) Dual (Sub)Gradient Methods
Feb 19 (M) 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 Methods
Feb 28 (W) Convergence Rate of Newton Methods 1
Mar 2 (F) Convergence Rate of Newton Methods 2
Mar 5 (M) Variants of Newton Methods (Damped Newton and Quasi-Newton Methods)