A General and Streamlined Differentiable Optimization Framework

TL;DR

This paper introduces an improved, unified differentiable optimization framework within Julia that simplifies computing sensitivities for complex models, enabling easier integration of optimization into machine learning and decision-making workflows.

Contribution

It presents a general, user-friendly API for differentiable optimization that handles multiple parameters and integrates seamlessly with the Julia ecosystem, including nonconvex problems.

Findings

Successfully applied to economic dispatch and portfolio optimization

Enabled gradient-based energy market bidding strategies

Facilitated Sobolev-style training of optimization proxies

Abstract

Differentiating through constrained optimization problems is increasingly central to learning, control, and large-scale decision-making systems, yet practical integration remains challenging due to solver specialization and interface mismatches. This paper presents a general and streamlined framework-an updated DiffOpt.jl-that unifies modeling and differentiation within the Julia optimization stack. The framework computes forward - and reverse-mode solution and objective sensitivities for smooth, potentially nonconvex programs by differentiating the KKT system under standard regularity assumptions. A first-class, JuMP-native parameter-centric API allows users to declare named parameters and obtain derivatives directly with respect to them - even when a parameter appears in multiple constraints and objectives - eliminating brittle bookkeeping from coefficient-level interfaces. We illustrate these capabilities on convex and nonconvex models, including economic dispatch, mean-variance portfolio selection with conic risk constraints, and nonlinear robot inverse kinematics. Two companion studies further demonstrate impact at scale: gradient-based iterative methods for strategic bidding in energy markets and Sobolev-style training of end-to-end optimization proxies using solver-accurate sensitivities. Together, these results demonstrate that differentiable optimization can be deployed as a routine tool for experimentation, learning, calibration, and design-without deviating from standard JuMP modeling practices and while retaining access to a broad ecosystem of solvers.