A set of simple low-cost presolve rules captures most of Gurobi's reduction and yields end-to-end speedups for GPU first-order LP solvers.
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Distributed-memory IPMs deliver speed-ups on block-structured energy optimization problems while GPU FOMs scale well but produce solutions with higher infeasibility that may still be usable.
Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.
citing papers explorer
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Presolving for GPU-Accelerated First-Order LP Solvers
A set of simple low-cost presolve rules captures most of Gurobi's reduction and yields end-to-end speedups for GPU first-order LP solvers.
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Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers
Distributed-memory IPMs deliver speed-ups on block-structured energy optimization problems while GPU FOMs scale well but produce solutions with higher infeasibility that may still be usable.
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Empirical Asymptotic Runtime Analysis of Linear Programming Algorithms
Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.