Solving Linear Programs in the Current Matrix Multiplication Time

Author:

Cohen Michael B.1,Lee Yin Tat2,Song Zhao3

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, Massachusetts

2. The University of Washington 8 MSR Redmond, Seattle, WA, USA

3. The University of Texas at Austin, Princeton, NJ, USA

Abstract

This article shows how to solve linear programs of the form min Ax = b , x ≥ 0 c x with n variables in time O * (( n ω + n 2.5−α/2 + n 2+1/6 ) log ( n /δ)), where ω is the exponent of matrix multiplication, α is the dual exponent of matrix multiplication, and δ is the relative accuracy. For the current value of ω δ 2.37 and α δ 0.31, our algorithm takes O * ( n ω log ( n /δ)) time. When ω = 2, our algorithm takes O * ( n 2+1/6 log ( n /δ)) time. Our algorithm utilizes several new concepts that we believe may be of independent interest: • We define a stochastic central path method. • We show how to maintain a projection matrix √ W A ( AWA ) −1 AW in sub-quadratic time under \ell 2 multiplicative changes in the diagonal matrix W .

Funder

Amazon

NSF

Schmidt Foundation

Google

Ma Huateng Foundation

DARPA/SRC

Simons Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Hardware and Architecture,Information Systems,Control and Systems Engineering,Software

Reference62 articles.

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2. Fast Matrix Multiplication

3. Jan van den Brand Binghui Peng Zhao Song and Omri Weinstein. 2020. Training (overparametrized) neural networks in near-linear time. Retrieved from https://arxiv.org/pdf/2006.11648.pdf. Jan van den Brand Binghui Peng Zhao Song and Omri Weinstein. 2020. Training (overparametrized) neural networks in near-linear time. Retrieved from https://arxiv.org/pdf/2006.11648.pdf.

4. An homotopy method for l p regression provably beyond self-concordance and in input-sparsity time

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