Differentiation Through Black-Box Quadratic Programming Solvers

Connor W. Magoon, Fengyu Yang, Noam Aigerman, Shahar Z. Kovalsky

Keywords: optimization, differentiable optimization, quadratic programming

TL;DR: We introduce dQP, a solver-agnostic, modular framework for differentiable quadratic programming (QP), enabling plug-and-play differentiation with any solver and demonstrating advantages in large-scale sparse problems.

Abstract: Differentiable optimization has attracted significant research interest, particularly for quadratic programming (QP). Existing approaches for differentiating the solution of a QP with respect to its defining parameters often rely on specific integrated solvers. This integration limits their applicability, including their use in neural network architectures and bi-level optimization tasks, restricting users to a narrow selection of solver choices. To address this limitation, we introduce **dQP**, a modular and solver-agnostic framework for plug-and-play differentiation of virtually any QP solver. A key insight we leverage to achieve modularity is that, once the active set of inequality constraints is known, both the solution and its derivative can be expressed using simplified linear systems that share the same matrix. This formulation fully decouples the computation of the QP solution from its differentiation. Building on this result, we provide a minimal-overhead, open-source implementation (<https://github.com/cwmagoon/dQP>) that seamlessly integrates with over 15 state-of-the-art solvers. Comprehensive benchmark experiments demonstrate dQP’s robustness and scalability, particularly highlighting its advantages in large-scale sparse problems.

Primary Area: Optimization (e.g., convex and non-convex, stochastic, robust)

Submission Number: 23094