cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia
提出一个基于GPU的线性规划求解器cuPDLP.jl，在Julia中实现重新启动原始对偶混合梯度法，性能可与Gurobi等商业求解器媲美，尤其在大规模实例上加速显著。
The paper cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia, by Haihao Lu and Jinwen Yang, addresses a fundamental question in large-scale optimization: Can modern GPUs be effectively leveraged for linear programming? The authors introduce cuPDLP.jl, a GPU-based solver implementing a restarted primal-dual hybrid gradient method entirely in Julia. Through extensive benchmarking on standard LP test sets, including MIPLIB relaxations and Mittelmann’s benchmarks, the study demonstrates that cuPDLP.jl achieves performance comparable to state-of-the-art commercial solvers such as Gurobi. Notably, the GPU implementation exhibits significant speedups on large-scale instances, highlighting the potential of first-order methods and GPU architectures for scalable optimization. The results mark a notable step toward high-performance LP solvers designed for modern hardware.