SCQPTH: an efficient differentiable splitting method for convex quadratic programming

TL;DR

SCQPTH introduces a fast, differentiable splitting method for convex quadratic programming based on ADMM, enabling efficient large-scale QP solutions and gradient computations, with open-source implementation.

Contribution

The paper presents SCQPTH, a novel differentiable ADMM-based solver for convex QPs, optimized for large-scale problems and integrated with automatic features.

Findings

Achieves 1-10x speedup over existing differentiable QP solvers.

Suitable for large-scale QPs with 100-1000 variables and thousands of constraints.

Open-source Python implementation with advanced features.

Abstract

We present SCQPTH: a differentiable first-order splitting method for convex quadratic programs. The SCQPTH framework is based on the alternating direction method of multipliers (ADMM) and the software implementation is motivated by the state-of-the art solver OSQP: an operating splitting solver for convex quadratic programs (QPs). The SCQPTH software is made available as an open-source python package and contains many similar features including efficient reuse of matrix factorizations, infeasibility detection, automatic scaling and parameter selection. The forward pass algorithm performs operator splitting in the dimension of the original problem space and is therefore suitable for large scale QPs with $100-1000$ decision variables and thousands of constraints. Backpropagation is performed by implicit differentiation of the ADMM fixed-point mapping. Experiments demonstrate that for large scale QPs, SCQPTH can provide a $1\times - 10\times$ improvement in computational efficiency in comparison to existing differentiable QP solvers.