Presents the first book on matrix algorithms in concise and well tested MATLAB codes. Allows readers quick understanding of algorithms by debugging codes. Codes are useful for many numerical computations.
- Robust and stochastic optimization
- Convex analysis
- Linear programming
- Monte Carlo simulation
- Model-based estimation
- Matrix algebra review
- Probability and statistics basics
The PUNLAG seminar is intended to supplement the numerical linear algebra course sequence at Purdue. The standard course CS515 doesn't have room for a number of interesting problems -- we hope to cover some in this seminar!
R. Sharipov. (2004)cite arxiv:math/0405323Comment: The textbook, AmSTeX, 143 pages, amsppt style, prepared for double side printing on letter size paper.
Q. Qu, Z. Zhu, X. Li, M. Tsakiris, J. Wright, and R. Vidal. (2020)cite arxiv:2001.06970Comment: QQ and ZZ contributed equally to the work. Invited review paper for IEEE Signal Processing Magazine Special Issue on non-convex optimization for signal processing and machine learning. This article contains 26 pages with 11 figures.
M. Cook, A. Zare, and P. Gader. (2020)cite arxiv:2007.01263Comment: 6 pages, 4 figures, Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.