Reinforcement learning configuration interaction
Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration
interaction (FCI) wave function, yielding significant computational savings and wave function …
interaction (FCI) wave function, yielding significant computational savings and wave function …
Improved Algorithms for Low Rank Approximation from Sparsity∗
DP Woodruff, T Yasuda - Proceedings of the 2022 Annual ACM-SIAM …, 2022 - SIAM
We overcome two major bottlenecks in the study of low rank approximation by assuming the
low rank factors themselves are sparse. Specifically,(1) for low rank approximation with …
low rank factors themselves are sparse. Specifically,(1) for low rank approximation with …
[PDF][PDF] Algorithms for Matrix Approximation: Sketching, Sampling, and Sparse Optimization
T Yasuda - 2024 - reports-archive.adm.cs.cmu.edu
The approximation of matrices by smaller, simpler, or structured matrices is a fundamental
problem in various fields of mathematics and computer science including numerical linear …
problem in various fields of mathematics and computer science including numerical linear …