Solving Linear Programs with Sqrt(rank) Linear System Solves
Yin Tat Lee, Aaron Sidford

TL;DR
This paper introduces an efficient algorithm for linear programming that reduces iteration complexity to approximately the square root of the matrix rank, and applies it to improve maximum flow problem solutions.
Contribution
It presents a novel algorithm with fewer linear system solves per iteration and provides a deterministic barrier function, advancing interior point methods and maximum flow algorithms.
Findings
Achieves $ ilde{O}( ext{sqrt}(rank(A)) ext{log}(1/ε))$ iteration complexity.
Improves maximum flow algorithm to $ ilde{O}(|E| ext{sqrt}(|V|) ext{log}(U))$ time.
Provides a deterministic polynomial-time self-concordant barrier for polytopes.
Abstract
We present an algorithm that given a linear program with variables, constraints, and constraint matrix , computes an -approximate solution in iterations with high probability. Each iteration of our method consists of solving linear systems and additional nearly linear time computation, improving by a factor of over the previous fastest method with this iteration cost due to Renegar (1988). Further, we provide a deterministic polynomial time computable -self-concordant barrier function for the polytope, resolving an open question of Nesterov and Nemirovski (1994) on the theory of "universal barriers" for interior point methods. Applying our techniques to the linear program formulation of maximum flow yields an time…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Stochastic Gradient Optimization Techniques · Advanced Optimization Algorithms Research
