Scalable Computational Methods with Recent Applications
For computations with many variables in optimization or solving large systems
in numerical linear algebra, developing efficient methods is highly desirable.
This talk introduces an approach for large-scale optimization with sparse
linear equality constraints that exploits computationally efficient orthogonal projections. For approximately solving large linear systems, (randomized) sketching methods are becoming increasingly popular. By recursively augmenting a deterministic sketching matrix, we develop a method with a finite termination property that compares favorably to randomized methods. Moreover, we describe
the construction of logical linear systems that can be used in e.g., COVID-19 pooling tests, and a nonlinear least-squares method that addresses large data sizes in machine learning.
Tuesday, October 12, 2021
11:00AM Zoom ID 970 1854 2148
Center for Computational Mathematics9500 Gilman Dr. #0112La Jolla, CA 92093-0112Tel: (858)534-9056