Topics in Optimization, Spring 2018
Course Outline
Time and location
Time: Tuesday, Friday 12:00  1:50pm
Location: DARRIN 236
Instructor
Yangyang Xu
Office: Amos Eaton 310
Office hour: TF 10:30am  11:30am or by appointment
Email: xuy21@rpi.edu
Programming assignments
Reading materials
A fast iterative shrinkagethresholding algorithm for linear inverse problems, Beck and Teboulle, 2009.
Gradient methods for minimizing composite functions, Nesterov, 2012.
Convergence rates of inexact proximalgradient methods for convex optimization, Schmidt, Roux, and Bach, 2011.
Proximal Newtontype methods for convex optimization, Lee, Sun, and Saunders, 2012.
Robust stochastic approximation approach to stochastic programming, Nemirovski, Juditsky, Lan, and Shapiro, 2009.
Stochastic first and zerothorder methods for nonconvex stochastic programming, Ghadimi and Lan, 2013.
Accelerating stochastic gradient descent using predictive variance reduction, Johnson and Zhang, 2013.
SAGA: A fast incremental gradient method with support for nonstrongly convex composite objectives, Defazio, Bach, and LacosteJulien, 2014.
Minimizing finite sums with the stochastic average gradient, Schmidt, Roux, and Bach, 2017.
Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization, Tseng, 2001.
A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion, Xu and Yin, 2013.
Efficiency of coordinate descent methods on hugescale optimization problems, Nesterov, 2012.
A unified convergence analysis of block successive minimization methods for nonsmooth optimization, Razaviyayn, Hong, and Luo, 2013.
Calculus of the exponent of KurdykaLojasiewicz inequality and its applications to linear convergence of firstorder methods, Li and Pong, 2017.
Worstcase Complexity of Cyclic Coordinate Descent: Gap with Randomized Version, Sun and Ye, 2016.
Analyzing Random Permutations for Cyclic Coordinate Descent, Wright and Lee, 2017.
A Primer on Coordinate Descent Algorithms, She, Tu, Xu, and Yin, 2016.
