# cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language

arXiv: https://arxiv.org/abs/2312.14832 · HTML: https://mlsanity.com/paper/2312.14832/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2023.

## Built on by (most cited first, 7 of 7)

- [PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming](https://mlsanity.com/paper/2406.01908/) (2024)
- [MPAX: Mathematical Programming in JAX](https://mlsanity.com/paper/2412.09734/) (2024)
- [Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems](https://mlsanity.com/paper/2507.21162/) (2025)
- [Gradient Methods with Online Scaling Part I. Theoretical Foundations](https://mlsanity.com/paper/2505.23081/) (2025)
- [Scalable First-order Method for Certifying Optimal k-Sparse GLMs](https://mlsanity.com/paper/2502.09502/) (2025)
- [From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal k-Sparse GLMs](https://mlsanity.com/paper/2605.22188/) (2026)
- [Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming](https://mlsanity.com/paper/2610.01546/) (2026)

## Abstract (arXiv)

A recent GPU implementation of the Restarted Primal-Dual Hybrid Gradient Method for Linear Programming was proposed in Lu and Yang (2023). Its computational results demonstrate the significant computational advantages of the GPU-based first-order algorithm on certain large-scale problems. The average performance also achieves a level close to commercial solvers for the first time in history. However, due to limitations in experimental hardware and the disadvantage of implementing the algorithm in Julia compared to C language, neither the commercial solver nor cuPDLP reached their maximum efficiency. Therefore, in this report, we have re-implemented and optimized cuPDLP in C language. Utilizing state-of-the-art CPU and GPU hardware, we extensively compare cuPDLP with the best commercial solvers. The experiments further highlight its substantial computational advantages and potential for solving large-scale linear programming problems. We also discuss the profound impact this breakthrough may have on mathematical programming research and the entire operations research community.