Fall 2012
- 8/15: Zhimin Peng, Distributed optimization via ADMM. [Slides]
- 8/30: Summary of summer research (I)
- 9/06: Summary of summer research (II); Wotao Yin
- 9/11: Yangyang Xu, Block coordinate descent for multi-convex optimization
- 9/20: Yin Zhang, Null space conditions, nonnegativity, Vandermonde matrix, etc.
- 9/27: Ming Yan, Sparse optimization coding, adaptive outlier pursuit
- 10/09: Yangyang Xu, Accelerated block-coordinate relaxation for regularized optimization (by Stephen Wright) [slides]
- 10/18: CAAM Special Lecture by Steve Wright [link]
- 10/23: Wei Deng, A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets (by N. Le Roux, M. Schmidt, F. Bach) [paper] [slides]
- 10/30: Zhimin Peng, Efficiency of coordinate descent methods on huge-scale optimization problems (by Y. Nesterov) [slides]
- 11/01: Genevera Allen, Modern Statistical Methods for Massive Data
- 11/06: Jorge Castenon, The Convex Optimization Approach to Regret Minimization (by Elad Hazan) [slides]
- 11/08: Brendt Edmunds, Incremental gradient, subgradient, and proximal methods for convex optimization: A Survey (by D. P. Bertsekas)
- 11/13: Hui Zhang, The minimization of restricted strongly convex functions with applications [slides]
- 11/20: Ming Yan, Hybrid Deterministic-Stochastic Methods for Data Fitting (by M. Friedlander, M. Schmidt)
- 11/27: Lijun Xu, Efficiency of randomized coordinate descent methods on optimization problems with linearly coupled constraints (by I. Necoara, Y. Nesterov, and F. Glineur) [slides]
- 11/29: Ruru Hao, Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization (by M. Schmidt, N. L. Roux, F. Bach) [slides]