TY - JOUR

T1 - Faster Randomized Interior Point Methods for Tall/Wide Linear Programs

AU - Chowdhury, Agniva

AU - Dexter, Gregory

AU - London, Palma

AU - Avron, Haim

AU - Drineas, Petros

N1 - Publisher Copyright:
© 2022 Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, and Petros Drineas.

PY - 2022/9/1

Y1 - 2022/9/1

N2 - Linear programming (LP) is an extremely useful tool which has been successfully applied to solve various problems in a wide range of areas, including operations research, engineering, economics, or even more abstract mathematical areas such as combinatorics. It is also used in many machine learning applications, such as `1-regularized SVMs, basis pursuit, nonnegative matrix factorization, etc. Interior Point Methods (IPMs) are one of the most popular methods to solve LPs both in theory and in practice. Their underlying complexity is dominated by the cost of solving a system of linear equations at each iteration. In this paper, we consider both feasible and infeasible IPMs for the special case where the number of variables is much larger than the number of constraints. Using tools from Randomized Linear Algebra, we present a preconditioning technique that, when combined with the iterative solvers such as Conjugate Gradient or Chebyshev Iteration, provably guarantees that IPM algorithms (suitably modified to account for the error incurred by the approximate solver), converge to a feasible, approximately optimal solution, without increasing their iteration complexity. Our empirical evaluations verify our theoretical results on both real-world and synthetic data.

AB - Linear programming (LP) is an extremely useful tool which has been successfully applied to solve various problems in a wide range of areas, including operations research, engineering, economics, or even more abstract mathematical areas such as combinatorics. It is also used in many machine learning applications, such as `1-regularized SVMs, basis pursuit, nonnegative matrix factorization, etc. Interior Point Methods (IPMs) are one of the most popular methods to solve LPs both in theory and in practice. Their underlying complexity is dominated by the cost of solving a system of linear equations at each iteration. In this paper, we consider both feasible and infeasible IPMs for the special case where the number of variables is much larger than the number of constraints. Using tools from Randomized Linear Algebra, we present a preconditioning technique that, when combined with the iterative solvers such as Conjugate Gradient or Chebyshev Iteration, provably guarantees that IPM algorithms (suitably modified to account for the error incurred by the approximate solver), converge to a feasible, approximately optimal solution, without increasing their iteration complexity. Our empirical evaluations verify our theoretical results on both real-world and synthetic data.

KW - Interior Point Methods

KW - Linear Programming

KW - Randomized Linear Algebra

UR - http://www.scopus.com/inward/record.url?scp=85148086343&partnerID=8YFLogxK

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AN - SCOPUS:85148086343

SN - 1532-4435

VL - 23

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

M1 - 336

ER -