4 papers · 1 filter
Query Efficient Structured Matrix Learning
Noah Amsel, Pratyush Avi, Tyler Chen +5
We study the problem of learning a structured approximation (low-rank, sparse, banded, etc.) to an unknown matrix given access to matrix-vector product (matvec) queries of the…
Quasi-optimal hierarchically semi-separable matrix approximation
Noah Amsel, Tyler Chen, Feyza Duman Keles +4
We present a randomized algorithm for producing a quasi-optimal hierarchically semi-separable (HSS) approximation to an matrix using only matrix-vector products wit…
The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm
Noah Amsel, David Persson, Christopher Musco +1
Computing the polar decomposition and the related matrix sign function has been a well-studied problem in numerical analysis for decades. Recently, it has emerged as an important s…
Randomized block-Krylov subspace methods for low-rank approximation of matrix functions
David Persson, Tyler Chen, Christopher Musco
The randomized SVD is a method to compute an inexpensive, yet accurate, low-rank approximation of a matrix. The algorithm assumes access to the matrix through matrix-vector product…