nvxpy 0.1.3

A Python DSL for simply and easily formulating and solving non-convex optimization problems.

NVXPY

Overview

NVXPY is a Python-based Domain Specific Language (DSL) designed for formulating and solving non-convex programs using a natural, math-inspired API. It is designed to have as similar an interface to CVXPY as possible.

NVXPY is not a solver, it uses the solvers exposed by the minimize method in SciPy.

Features

• Simple, concise, and generic interface
• Mediocre efficiency for expression tree evaluations (estimated at 60% - 99% native NumPy)
• Handles gradients seemlessly, even for custom and non-Autograd functions

Installation

NVXPY can be installed from PyPi using:

pip install nvxpy

and has the following dependencies:

• Python >= 3.11
• NumPy >= 2.3
• SciPy >= 1.15
• Autograd >= 1.8

Usage

The following is a simple example to get started with NVXPY:

import numpy as np
import nvxpy as nvx

x = nvx.Variable((3,))
x.value = np.array([-5.0, 0.0, 0.0]) # NLPs require a seed.

x_d = np.array([5.0, 0.0, 0.0])

obj = nvx.norm(x - x_d)
constraints = [nvx.norm(x) >= 1.0] # Non-convex!

prob = nvx.Problem(nvx.Minimize(obj), constraints)
prob.solve(solver=nvx.SLSQP)

print(f'optimized value of x: {x.value}')

The above code will likely get stuck at a locally optimal solution. To reach the globally optimal solution, we can adjust the seed and re-solve as follows:

x.value = np.array([-5.0, 1.0, 1.0])
prob.solve(solver=nvx.SLSQP)

print(f'globally optimal value of x: {x.value}')

Limitations

NVXPY is in early development. The most pressing issues are as follows:

• Only supports SciPy-based solvers. Ideally most other NLP solvers should be easy to add, such as IPOPT, which provides a similar API to the minimize function from SciPy.
• No plans to support integer programming any time soon. One potential solution is to make a SLP-inspired solver based on the MILP solver from SciPy to enforce integrality constraints. However, any custom solver would probably be out of the scope of this project.
• Small amount of atomic operations and sets.
• Unknown and untested edge cases.

Development

To contribute to NVXPY, clone the repository and install the development dependencies:

git clone https://github.com/landonclark97/nvxpy.git
cd nvxpy
poetry install --with dev

Running Tests

Tests are written using pytest. To run the tests, execute:

poetry run pytest

License

Apache 2.0

Contact

For any inquiries or issues, please contact Landon Clark at [e-mail].