cvxgenrust

cvxgenrust takes a parameterized CVXPY optimization problem and generates a Rust solver crate tailored to that problem family. The generated crate reconstructs canonical cone-program data and solves it with Clarabel. It also includes a Python wrapper that can be registered as a custom CVXPY solve method for prototyping.

Installation

Install the released package from PyPI with:

pip install cvxgenrust

Generated solver projects use Rust, Cargo, Clarabel, and, when the Python wrapper is enabled, a PyO3/maturin build. Install a stable Rust toolchain before building or importing generated extension wrappers.

For development from this repository, use uv to manage dependencies. Once uv is installed, run:

make sync

This installs the default development environment defined by the repository Makefile.

Quick Start

Generate a small nonnegative least-squares solver as a Rust crate:

import cvxpy as cp
import cvxgenrust as cgr

m, n = 3, 2
A = cp.Parameter((m, n), name="A")
b = cp.Parameter(m, name="b")
x = cp.Variable(n, name="x")

problem = cp.Problem(
    cp.Minimize(cp.sum_squares(A @ x - b)),
    [x >= 0],
)

project = cgr.generate_code(
    problem,
    code_dir="nonneg_ls_cgr",
    module_name="nonneg_ls",
)
print("generated:", project.output_dir)

You should always set name= on CVXPY parameters and variables. The generated Rust setters, extractors, metadata, and Python wrapper use those names after code generation.

An HTML summary of the generated project is written to nonneg_ls_cgr/README.html.

You can build and run the generated Rust project with:

cd nonneg_ls_cgr
cargo run --example solve

By default, generate_code also compiles the generated Python extension wrapper into the generated project's python/ directory. Pass wrapper=False to only write the Rust crate and Python wrapper sources.

Related projects

• CVXPYgen: C code generation from CVXPY problems.
• CVXGEN: C code generation for convex optimization in MATLAB.