Optimization formulations of data analysis problems

Many of the computational problems that arise in data analysis and machine learning can be expressed mathematically as optimization problems. Indeed, much new algorithmic research in optimization is being driven by the need to solve large, complex problems of this kind. In this talk, we describe a number of canonical problems in data analysis and review their optimization formulations. We will cover support vector machines / kernel learning, logistic regression (including regularized and multiclass variants), matrix completion, deep learning, and several other paradigms.

Bio:

Stephen J. Wright holds the George B. Dantzig Professorship, the Sheldon Lubar Chair, and the Amar and Balinder Sohi Professorship of Computer Sciences at the University of Wisconsin-Madison. His research is in computational optimization and its applications to many areas of science and engineering, including data science. Prior to joining UW-Madison in 2001, Wright held positions at North Carolina State University (1986-90), Argonne National Laboratory (1990-2001), and the University of Chicago (2000-2001). He holds a Ph.D. in Mathematics from the University of Queensland, awarded in 1984. He has served as Chair of the Mathematical Optimization Society and as a Trustee of SIAM. He is a Fellow of SIAM. In 2014, he won the W.R.G. Baker Award from IEEE.

Wright is the author / coauthor of widely used text and reference books in optimization including "Primal Dual Interior-Point Methods" and "Numerical Optimization". He has published widely on optimization theory, algorithms, software, and applications.

Wright is current editor-in-chief of the SIAM Journal on Optimization and previously served as associate editor or editor-in-chief of Mathematical Programming (Series A), Mathematical Programming (Series B), SIAM Review, SIAM Journal on Scientific Computing, and several other journals and book series.