A Technical Note on the Implementation and Use of PDCS

TL;DR

This paper details the implementation of PDCS, a first-order solver for large-scale conic optimization, including algorithmic innovations, practical usage instructions, and interface support, facilitating efficient problem solving.

Contribution

It introduces the implementation details and practical usage of PDCS, a novel first-order conic solver with adaptive techniques and interface support.

Findings

Provides detailed implementation and usage instructions.

Demonstrates practical effectiveness through code examples.

Supports interfaces with JuMP and CVXPY.

Abstract

This technical note documents the implementation and use of the Primal-Dual Conic Programming Solver (PDCS), a first-order solver for large-scale conic optimization problems introduced by Lin et al. (arXiv:2505.00311). It describes the algorithmic and implementation details underlying PDCS, including the restarted primal-dual hybrid gradient method framework, adaptive step-size selection, adaptive reflected Halpern iterations, adaptive restarts, and diagonal preconditioning. It also provides practical instructions for using PDCS, including its interfaces with JuMP and CVXPY, solver options, and illustrative code examples. PDCS is available at https://github.com/ZikaiXiong/PDCS under the Apache License 2.0.