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First-Order Methods for Linear Programming

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arxiv 2403.14535 v1 pith:THJ3JL74 submitted 2024-03-21 math.OC

Haihao Lu This is my paper · ORCID
classification math.OC
keywords linearprogrammingfirst-ordermethodsoptimizationalgorithmicarticledevelopment
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Linear programming is the seminal optimization problem that has spawned and grown into today's rich and diverse optimization modeling and algorithmic landscape. This article provides an overview of the recent development of first-order methods for solving large-scale linear programming.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Finite Horizon Optimization: Framework and Applications

    math.OC 2024-12 reject novelty 6.0 of 10

    A finite-horizon stepsize rule for the primal-dual method on LP, found via a 4x4 SDP, is claimed to accelerate convergence at the T-th iteration and to give about 3.9x speedup on Netlib instances.

  2. A quantum dual logarithmic barrier method for linear optimization

    math.OC 2024-12 reject novelty 6.0 of 10

    A dual-only quantum interior point method for linear optimization with inexact Newton directions and O(√n) iteration complexity, using QLSA and tomography.

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