Year: 2022
Type: article
Abstract: We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parametrized class of convex optimization problems. CVXPYgen is based on CVXPY, a Python-embedded domain-specific language that supports a natural syntax (that follows the mathematical description) for specifying convex optimization problems. Along with the C implementation of a custom solver, CVXPYgen creates a Python wrapper for prototyping and desktop (non-embedded) applications. We give two examples, position control of a quadcopter and back-testing a portfolio optimization model. CVXPYgen outperforms a state-of-the-art code generation tool in terms of problem size it can handle, binary code size, and solve times. CVXPYgen and the generated solvers are open-source.
Source: IEEE Control Systems Letters
Authors: Maximilian Schaller, Goran Banjac, Steven Diamond, Akshay Agrawal, Bartolomeo Stellato, Stephen Boyd
Institutions: ETH Zurich, Stanford University, Princeton University
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Award: G6421654716
OpenAlex ID: W4285140529