In this work, we improved the analysis of the running time of SparseGPT [Frantar, Alistarh
ICML 2023] from $ O (d^{3}) $ to $ O (d^{\omega}+ d^{2+ a+ o (1)}+ d^{1+\omega (1, 1, a)-a}) …

The success of deep learning comes at a tremendous computational and energy cost, and
the scalability of training massively overparametrized neural networks is becoming a real …

Finding a good approximation of the top eigenvector of a given dxd matrix A is a basic and
important computational problem, with many applications. We give two different quantum …

We develop a new parallel algorithm for minimizing Lipschitz, convex functions with a
stochastic subgradient oracle. The total number of queries made and the query depth, ie, the …

Approximating invariant subspaces of generalized eigenvalue problems (GEPs) is a
fundamental computational problem at the core of machine learning and scientific …

We provide a fast distributed algorithm for detecting $ h $-cycles in the\textsf {Congested
Clique} model, whose running time decreases as the number of $ h $-cycles in the graph …

This paper studies second-order optimization methods solving monotone nonlinear
equation problems (MNE) and minimization problems (Min) in a $ d $ dimensional vector …

The tree edit distance (TED) between two rooted ordered trees with $ n $ nodes labeled
from an alphabet $\Sigma $ is the minimum cost of transforming one tree into the other by a …

In this work we revisit the arithmetic and bit complexity of Hermitian eigenproblems. We first
provide an analysis for the divide-and-conquer tridiagonal eigensolver of Gu and Eisenstat …

In this paper, we show new data structures maintaining approximate shortest paths in sparse
directed graphs with polynomially bounded non-negative edge weights under edge …