mmahelpy

mmahelpy is a small, stateful implementation of the Method of Moving Asymptotes (MMA). It solves a separable convex approximation at each call and retains the design and asymptote history for the next outer iteration.

The package is independent of finite-element and topology-optimization libraries. Supply objective and inequality-constraint values and gradients at the current point. Constraints use the convention g(x) <= 0.

Install

pip install mmahelpy

Example

import numpy as np
from mmahelpy import MMA, MMAOptions

optimizer = MMA(MMAOptions(move_limit=0.2))
x = np.array([0.8, 0.8])
lower = np.zeros(2)
upper = np.ones(2)

for _ in range(30):
    # Minimize x[0]**2 + x[1]**2 subject to x[0] + x[1] >= 0.9.
    objective = float(x @ x)
    objective_gradient = 2.0 * x
    constraints = np.array([0.9 - x.sum()])
    constraint_jacobian = np.array([[-1.0, -1.0]])
    result = optimizer.step(
        x,
        objective,
        objective_gradient,
        constraints,
        constraint_jacobian,
        lower,
        upper,
    )
    x = result.x

print(x)

Create one MMA object per optimization run. Its step method returns an MMAResult with the next design, dual multipliers, approximate constraint values, and dual-solver status. Call reset() to reuse the object for a new problem. Multiple inequality constraints are supported.

Development

PYTHONPATH=src pytest

Both the distribution and Python import are named mmahelpy. Scikit-Topt depends on mmahelpy and adapts its public MMA_Config to the generic optimizer for topology optimization. The solver focuses on continuous, differentiable problems with box bounds and inequality constraints.

Release

Update the version in pyproject.toml, create a matching GitHub release such as v0.1.1, and publish it. The workflow runs the test suite on Python 3.10–3.13 before publishing to PyPI with Trusted Publishing. Configure the PyPI trusted publisher for repository kevin-tofu/mmahelpy, workflow workflow.yml, and environment pypi before creating a release.