Subspace injections
Linear Algebra seminar by Joel Tropp, California Institute of Technology
Hosted by Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University
Wednesday 09:00 New York (GMT-5)
Recording available
Abstract
Joel Tropp studies structured dimension reduction through the injectivity of random maps, motivated by fast low-rank approximation and least-squares regression. This viewpoint sharpens guarantees for sparse maps and gives exponential improvements for tensor-product dimension reduction. Experiments assess the resulting structured random matrices on synthetic problems and scientific applications. Joint work with Chris Camaño, Ethan Epperly, and Raphael Meyer, available as arXiv:2508.21189.
Topics
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