xpresslibs

What it is and what it does

xpresslibs is a commercial mathematical optimization solver wrapped for Python, allowing you to define and solve optimization problems ranging from simple linear programs to complex mixed-integer nonlinear problems. You describe your problem using Python syntax—defining variables with bounds and types, setting an objective function, and adding constraints—then call optimize() to invoke the underlying FICO Xpress engine.

The package bundles the compiled Xpress Optimizer library itself, so installation includes native binaries for your platform rather than just Python code. It has no runtime Python dependencies, making it self-contained once installed. A community license is included free; commercial deployments or access to advanced solver features may require a paid FICO license.

Use it for:

• Solve supply chain optimization problems with integer constraints and large numbers of variables
• Formulate and solve portfolio optimization or financial planning models with quadratic objectives
• Prototype mixed-integer nonlinear programs for engineering design or resource allocation
• Solve Linear Programming (LP), Quadratic Programming (QP), and Second-Order Conic Programming (SOCP) problems
• Deploy optimization models on Linux, macOS, and Windows platforms with consistent behavior

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Python interface to the FICO Xpress Optimizer for creating and solving linear, quadratic, conic, and nonlinear optimization problems, including their mixed-integer variants.

Yes, if you need to solve optimization problems and can accept a proprietary solver with a free community license tier. The package is actively maintained, has no Python dependency overhead, and runs on major platforms. Verify that the community license meets your use case and that your system can accommodate the compiled native libraries before committing to it in production.

Install

xpresslibs on PyPI

pip install xpresslibs

uv add xpresslibs

poetry add xpresslibs

Installing xpresslibs

Before you install

Medium install friction due to compiled native libraries across multiple platforms (macOS ARM64, Linux x86_64 and ARM64, Windows x64). Package is actively maintained with a release 42 days ago. No runtime Python dependencies, reducing downstream compatibility issues.

License in practice

Proprietary software governed by the Xpress Shrinkwrap License Agreement. A community license is included with the package at no cost, but commercial use or advanced features may require a paid license. License treatment is unclear in metadata.

Quickstart

pip install xpresslibs

import xpress as xp
p = xp.problem(name='example')
x1 = p.addVariable(vartype=xp.integer, lb=-10, ub=10)
x2 = p.addVariable()
p.setObjective(x1**2 + 2*x2)
p.addConstraint(x1 + 3*x2 >= 4)
p.optimize()
print(p.getSolution(x1), p.getSolution(x2))

FICO Xpress Optimizer library must be installed on the system; community license is included but commercial use may require a separate license agreement with FICO.

Verify before relying

• Whether the community license has restrictions on problem size, solver features, or commercial deployment
• Specific Python version compatibility (requires_python is unspecified in metadata)
• Whether GPU support via CUDA is production-ready or remains beta-only in version 9.9.1
• Integration capabilities with NumPy and callback support for custom solver logic

Package facts

Evidence: xpresslibs-9.9.1-py3-none-macosx_14_0_arm64.whl; xpresslibs-9.9.1-py3-none-manylinux1_x86_64.whl; xpresslibs-9.9.1-py3-none-manylinux2014_aarch64.whl; xpresslibs-9.9.1-py3-none-win_amd64.whl

Keywords: optimization, mip, minlp, xpress

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