

Course Information When: TuTh 12:30pm  1:45pm Where: CSI 3120 Instructor: Tom Goldstein Office Hours: Th 23pm, AVW 3141 TA: Peter Sutor Office Hours: Wed 1:302:30 Final Exam: Tue May 17 at 1:30pm in CSIC 3120 Homework All homework should be submitted on the UMD submit server. Instructions from last year's course can be found here. Homework 1  gradients, linear classifiers, and FFTs Homework 2  convex functions Homework 3  gradient methods Homework 4  duality : latex source Homework 5  forwardbackward splitting Homework 6  Lagrangian Methods Course Description This is an introductory survey of optimization. Special emphasis will be put methods with applications in machine learning, model fitting, and image processing. There are no formal prerequisites for this course, however students should have a strong background in applied mathematics (especially linear algebra) and computer programming. Students' grades will be based on completion of the following:
Topics Covered
Book & Other Sources All course materials are available for free online. Suggested reading material for various topics includes: Numerical Linear Algebra: Numerical Linear Algebra by Trefethen and Bau L1 models and sparsity: Sparse modeling for Image and Vision Processing Convex functions and gradient methods: Convex Optimization by Boyd and Vandenberghe Convergence rates for gradient methods: Optimal Rates in Convex Optimization Proximal methods: A Field Guide to ForwardBackward Splitting ADMM: Fast Alternating Direction Optimization Methods Consensus ADMM: Distributed Optimization and Statistical Learning Unwrapped ADMM: Unwrapping ADMM PDHG: Adaptive PrimalDual Hybrid Gradient Methods SGD: Incremental Gradient, Subgradient, and Proximal Methods SGD convergence rates: Stochastic Gradient Descent for NonSmooth Optimization MonteCarlo: An Introduction to MCMC for Machine Learning Barrier Methods: Convex Optimization by Boyd and Vandenberghe, chapter 11 PrimalDual Interior Point Methods: Nocedal and Wright, chapter 14 Semidefinite programming: Vandenbergh and Boyd Metric learning: Distance Metric Learning for LMNN Restricted Access Lecture Slides 
