cuPDLPx: A Further Enhanced GPU-Based First-Order Solver for Linear Programming
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Abstract (arXiv)
We introduce cuPDLPx, a further enhanced GPU-based first-order solver for linear programming. Building on the recently developed restarted Halpern PDHG for LP, cuPDLPx incorporates a number of new techniques, including a new restart criterion and a PID-controlled primal weight update. These improvements are carefully tailored for GPU architectures and deliver substantial computational gains. Across benchmark datasets, cuPDLPx achieves 2.5x-5x speedups on MIPLIB LP relaxations and 3x-6.8x on Mittelmann's benchmark set, with particularly strong improvements in high-accuracy and presolve-enabled settings. The solver is publicly available at https://github.com/MIT-Lu-Lab/cuPDLPx.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jul 2025.
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Citing papers per year: 2026: 5.
5 papers.
- 2026Batched First-Order Methods for Parallel LP Solving in MIPcited by 1
- 2026Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programmingcited by 0
- 2026Dual-guided Hierarchical Edge Localization for Large-scale Optimal Transport Across Dimensionscited by 0
- 2026Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programmingcited by 0
- 2026GPU-Accelerated Bregman Douglas-Rachford Splitting for Discrete Optimal Transportcited by 0
- 2026GPU-Accelerated Bregman Douglas-Rachford Splitting for Discrete Optimal Transport3 Oct 2026
- 2026Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming1 Oct 2026
- 2026Dual-guided Hierarchical Edge Localization for Large-scale Optimal Transport Across Dimensions11 Sep 2026
- 2026Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming7 Jun 2026
- 2026Batched First-Order Methods for Parallel LP Solving in MIP29 Jan 2026