nexus-opt

One modelling API. Rust core. Every problem class.

Linear, mixed-integer, quadratic, conic (SOCP/SDP), nonlinear, black-box, multi-objective and stochastic programming — behind a single interface, so changing problem class doesn't mean changing library.

License: MIT Python Rust

Website · User guide · Energy layer

Install

pip install nexus-opt

Prebuilt wheels for Linux (glibc and musl; x86_64 / aarch64 / i686), macOS (arm64 and x86_64) and Windows (x64 and x86). The crate builds as an abi3 extension, so one wheel per platform serves every CPython from 3.9 up — and no Rust toolchain is needed to install.

Not yet on PyPI. The wheel matrix is built and verified but publication is pending. Until then, see Building from source.

Sixty seconds

import nexus_opt as nx

m = nx.Model()
x = m.variable("x", lower=0)
y = m.variable("y", lower=0)

m.add(x + y >= 10)
m.add(x <= 6)
m.minimize(2 * x + 3 * y)

r = m.solve()
print(r.status, r.objective, r.value(x), r.value(y))
# optimal 24.0 6.0 4.0

Variables are continuous, integer or binary. Constraints are written with ordinary <=, >=, == and compile straight to Rust structures; nx.sum() reduces expressions in parallel.

What's in it

Quadratic and conic. Quadratic objectives compile to native quadratic terms. Second-order cones via add_soc([head, x1, ..., xk]) (imposing t >= ||x||₂), PSD cones over the lower-triangular entries of a symmetric matrix, and quad_form(model, xs, P) which builds xᵀPx and validates that P is PSD.

Nonlinear. sin, cos, sqrt and division are first-class expression operators, not wrappers. Nonlinear constraints live in a parallel container, so the LP/QP presolve path stays untouched and bit-identical.

Black-box and metaheuristics. Differential evolution, particle swarm, simulated annealing and CMA-ES:

r = nx.solve_de(lambda x: x[0]**2 + x[1]**2, [(-5, 5), (-5, 5)])

nx.solve_portfolio(...) races several algorithms against the same objective and returns the best.

Multi-objective and stochastic. pareto_frontier(...), stochastic_solve(...).

Scale. solve_parallel_de, solve_admm, and PersistentHighs for warm-started re-solves across a sequence of related problems.

See WIKI.md for the full API reference.

Who uses it

nexus-energy is built directly on this core — capacity expansion, unit commitment and OPF at exact parity with PyPSA and GenX, several times faster. If you want the energy-system layer rather than the solver, start there.

Benchmarks

benchmarks/ holds the comparison harness and stored results (benchmarks/_results/*.jsonl) against scipy, cvxpy, PuLP, Pyomo, Optuna and Nevergrad.

Third-party solvers appear there only as comparison rows — they are never wrapped inside the library.

Building from source

git clone https://github.com/VishalRam24/nexus-opt
cd nexus-opt
maturin build --release
pip install target/wheels/*.whl

Needs a Rust toolchain. Run the tests with uv run pytest.

Licence

MIT — see LICENSE.

Project links

Data verified by PyPI on Jul 30, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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Latest release

1 maintainer

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Avatar for VishalRam24 from gravatar.com VishalRam24

Credits

Author: Vishal Ram Sanmuga Magesh

GitHub Statistics

Data verified by PyPI on Jul 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.9

Tags

optimisation optimization linear-programming milp conic socp solver rust

Classifiers

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