Embedded Code Generation With CVXPY

Maximilian Schaller; Goran Banjac; Steven Diamond; Akshay Agrawal; Bartolomeo Stellato; Stephen Boyd

doi:10.1109/LCSYS.2022.3173209

Embedded Code Generation With CVXPY

Maximilian Schaller, Goran Banjac, Steven Diamond, Akshay Agrawal, Bartolomeo Stellato, Stephen Boyd

Operations Research & Financial Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parameterized 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.

Original language	English (US)
Pages (from-to)	2653-2658
Number of pages	6
Journal	IEEE Control Systems Letters
Volume	6
DOIs	https://doi.org/10.1109/LCSYS.2022.3173209
State	Published - 2022

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Control and Optimization

Keywords

Computational methods
Embedded systems
Optimization

Access to Document

10.1109/LCSYS.2022.3173209

Cite this

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title = "Embedded Code Generation With CVXPY",

abstract = "We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parameterized 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.",

keywords = "Computational methods, Embedded systems, Optimization",

author = "Maximilian Schaller and Goran Banjac and Steven Diamond and Akshay Agrawal and Bartolomeo Stellato and Stephen Boyd",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2022",

doi = "10.1109/LCSYS.2022.3173209",

language = "English (US)",

volume = "6",

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journal = "IEEE Control Systems Letters",

issn = "2475-1456",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Embedded Code Generation With CVXPY

AU - Schaller, Maximilian

AU - Banjac, Goran

AU - Diamond, Steven

AU - Agrawal, Akshay

AU - Stellato, Bartolomeo

AU - Boyd, Stephen

PY - 2022

Y1 - 2022

N2 - We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parameterized 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.

AB - We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parameterized 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.

KW - Computational methods

KW - Embedded systems

KW - Optimization

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SP - 2653

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JO - IEEE Control Systems Letters

JF - IEEE Control Systems Letters

ER -

Embedded Code Generation With CVXPY

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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