Speaker: Jonathan Weare, NYU (Joint Seminar with Applied Math)
Title: Fast randomized iterative numerical linear algebra for quantum chemistry and other applications
Abstract: I will discuss a family of recently developed stochastic techniques for linear algebra problems involving very large matrices. These methods can be used to, for example, solve linear systems, estimate eigenvalues/vectors, and apply a matrix exponential to a vector, even in cases where the desired solution vector is too large to store. The first incarnations of this idea appear for dominant eigenproblems arising in statistical physics and in quantum chemistry and were inspired by the real space diffusion Monte Carlo algorithm which has been used to compute chemical ground states for small systems since the 1970's. I will discuss our own general framework for fast randomized iterative linear algebra as well share a very partial explanation for their effectiveness. I will also report on the progress of an ongoing collaboration aimed at developing fast randomized iterative schemes specifically for applications in quantum chemistry. This talk is based on joint work with Lek-Heng Lim, Timothy Berkelbach, Sam Greene, and Rob Webber.
Bio: Jonathan Weare is currently an associate professor of mathematics in the Courant Institute of Mathematical Sciences at New York University. Previously he was an associate professor in the statistics department and in the James Franck Institute at the University of Chicago and, before that, an assistant professor in the mathematics department there. Before moving to Chicago Jonathan was a Courant Instructor of mathematics at NYU and a PhD student in mathematics at the University of California at Berkeley.