GitHub

A Python demonstration of speeding up matrix multiplication and reducing memory using a quantum-inspired low-rank SVD trick, with examples in AI.

This repository demonstrates a powerful, classical linear algebra technique—low-rank approximation via Singular Value Decomposition (SVD)—to dramatically accelerate common matrix operations like GEMM ...
JSTOR Daily

ON THE OPTIMIZATION OF EXTRAPOLATION METHODS FOR SINGULAR LINEAR SYSTEMS

Journal of Computational Mathematics, Vol. 26, No. 2 (March 2008), pp. 227-239 (13 pages) We discuss semiconvergence of the extrapolated iterative methods for solving singular linear systems. We ...
JSTOR Daily

ON BLOCK PRECONDITIONERS FOR PDE-CONSTRAINED OPTIMIZATION PROBLEMS

Recently, Bai proposed a block-counter-diagonal and a block-counter-triangular preconditioning matrices to precondition the GMRES method for solving the structured system of linear equations arising ...
IEEE

Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates

Abstract: This paper addresses the distributed optimization problem with privacy concerns over time-varying unbalanced networks, where agents collaborate to optimize the average of local objective ...