Generation of Custom Solvers in Rust for Convex Optimization
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Abstract (arXiv)
We introduce cvxgenrust, an open-source tool for generating custom Rust code that solves families of parameterized convex optimization problems modeled in CVXPY. cvxgenrust canonicalizes a problem family, extracts affine maps to Clarabel cone-program data, and generates a specialized Rust crate that updates parameters and calls Clarabel natively at runtime. The generated solver can also be exposed to Python and registered as a custom CVXPY solver. Our code generator supports a wide range of convex optimization problems up to semidefinite programs and exponential-cone problems. Numerical experiments show reduced runtime relative to direct CVXPY solves and performance comparable to CVXPYgen on shared problem classes.
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References · 14
- 2021Convex optimizationcited by 2,515
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
- 2019Differentiable Convex Optimization Layerscited by 465
- 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 161
- 2019acados: a modular open-source framework for fast embedded optimal controlcited by 68
- 2024Clarabel: An interior-point solver for conic programs with quadratic objectivescited by 44
- 2014Convex Optimization in Juliacited by 9
- 2019Least Squares Auto-Tuningcited by 5
- 2024Markowitz Portfolio Construction at Seventycited by 5
- 2025Differentiating Through a Quadratic Cone Programcited by 5
- 2022Portfolio Construction with Gaussian Mixture Returns and Exponential Utility via Convex Optimizationcited by 4
- 2025Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Accelerationcited by 4
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- 2022Embedded Code Generation with CVXPYcited by 3
- 2025Automatic Generation of Explicit Quadratic Programming Solverscited by 1
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