A Practical GPU-Enhanced Matrix-Free Primal-Dual Method for Large-Scale Conic Programs

PDCS and cuPDCS combine matrix-free primal-dual methods with GPU computation for large-scale conic programs. The solver uses sparse matrix-vector products rather than matrix factorizations, with adaptive step sizes, restarts, and diagonal preconditioning. Supported problems include linear, second-order cone, convex quadratic, and exponential cone programs.