This seminar is not currently active. If you would like to start organizing this, you should (1) talk to other grad students and make sure there is enough interest that you will have speakers every week and (2) talk to the Galois Group president to get funding for snacks for the seminar. (Note that the student run research seminar, or SRRS, is an active student run weekly seminar and most people wanting to present do so there as of the 23-24 school year.)

This student-run reading group meets weekly to discuss methods and applications of optimization.

Here is a list of resources which group members have recommended.

Boyd's Convex Optimization Video Lectures

Hastie, Tibshirani & Wainwright Statistical Learning with sparsity Primarily about Lasso and its generalizations from a statistical perspective.

Off the Convex Path A blog with reflections on the methods of convex optimization.

I'm a Bandit A blog on Probability, Optimization, and Statistics. The “Complexities of Optimization” course notes on this site are a great introduction to research-level optimization.

For Fall 2017, the seminar is held on Tuesdays from 1:10-2:00p in MSB 3240

Dates:

• 10/3: Organizational Meeting
• 10/10: Robert: A Stochastic Approximation Method and Slides
• 10/17: Will
• 10/24: Kirill
• 10/31: Ji
• 11/7: Yunshen
• 11/21: Mikhail
• 11/28: David W.
• 12/5: Robert (special time of 12-1 pm, in room 2240)
• 12/12: (Finals Week)

Spring 2017:

• 4/7: No seminar (SIAM regional meeting)
• 4/14: Robert: Total-Variation Penalized Density Estimation
• 4/21: Jiawei: Dual Feasible Functions for Integer Programming
• 4/28: Will: Three Excursions around Conic Duality (Relevant material: Conic Optimization and Symmetric Cones, by L. Vandenberghe)
• 5/5: Gabe Reinforcement Learning
• 5/12: No seminar (BayOpt conference)
• 5/19: David H: Graph Clustering
• 5/26: Yunshen: Constrained Eigenvalue Problems Ref 1Ref 2Ref 3Ref 4
• 6/2: Ji: Saddle points in non-convex optimization Ref 1Ref 2Ref 3
• 6/8: David W: Practice qual

Winter 2017:

• 1/19: Roundtable talks
• 1/26: Julio: Royset, Johannes O., and Roger JB Wets. "Variational Theory for Optimization under Stochastic Ambiguity." (2016).
• 2/2: David H: Park, J. and Boyd, S. "Concave Quadratic Cuts for Mixed-Integer Quadratic Problems." (2015).
• 2/9: David W: See the text by Hastie et al. Also see Website for GLMNet package.
• 2/17 (3p): Robert Douglas-Rachford Splitting in the Context of Proximal Point Algorithms
• 2/23: Ji: Rong Ge, Jason D. Lee, and Tengyu Ma. "Matrix Completion has No Spurious Local Minimum"
• 3/2: Will: Eriksson, Olsson, Kahl. "Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints"
• 3/10 (3p): Yiqun: Hastie, Tibshirani & Wainwright Statistical Learning with sparsity
• 3/16: Mikhail: Multi-Objective Optimization Using Evolutionary Algorithms