optlang

What it is and what it does

Optlang is a Python library that lets you define linear, mixed-integer, and quadratic optimization problems using symbolic math expressions (via sympy), then solve them through a unified interface to multiple solver backends. Instead of writing problems in a solver-specific format, you declare variables with bounds, build constraints and objectives from symbolic expressions, combine them into a Model, and call optimize()—the same code can then switch between GLPK, CPLEX, Gurobi, or other solvers without rewriting the problem formulation.

The library is built on sympy for symbolic math and swiglpk for GLPK access by default, with optional support for commercial solvers (CPLEX, Gurobi) and open-source alternatives (scipy, osqp). It's actively maintained, supports Python 3.9–3.13, and carries no known security vulnerabilities. It's commonly used in scientific and operations research workflows where problem portability and clean problem definition matter.

Use it for:

• Define and solve linear programming problems (e.g., resource allocation, production planning) without learning solver-specific syntax.
• Prototype optimization models in Python that can later be solved with different backends (GLPK, CPLEX, Gurobi) by changing one import.
• Formulate mixed-integer programs for scheduling, routing, or combinatorial optimization using symbolic constraints.
• Build quadratic programming solvers for portfolio optimization or least-squares problems via optional solvers.
• Integrate optimization into scientific workflows where sympy expressions are already used for symbolic math.

Worth the install?

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

Optlang formulates and solves linear, mixed-integer, and quadratic optimization problems using symbolic expressions from sympy, with pluggable solver backends including GLPK, CPLEX, and Gurobi.

Yes. Optlang is actively maintained, has low install friction, carries no known vulnerabilities, and offers a clean Pythonic API for optimization problems. It's a good fit if you need to formulate and solve LP/MILP/QP problems with solver flexibility. Install it if you're doing operations research, scientific computing, or constraint-based modeling in Python.

Install

optlang on PyPI

pip install optlang

uv add optlang

poetry add optlang

Installing optlang

Before you install

Low friction: pure Python wheel with two runtime dependencies (sympy and swiglpk). Actively maintained as of June 2026 with recent releases.

License in practice

Apache-2.0 is permissive; you may use, modify, and distribute optlang freely in commercial or proprietary projects provided you include the license notice.

Quickstart

pip install optlang

from optlang import Model, Variable, Constraint, Objective

x1 = Variable('x1', lb=0)
x2 = Variable('x2', lb=0)
c1 = Constraint(x1 + x2, ub=100)
obj = Objective(10*x1 + 6*x2, direction='max')

model = Model()
model.objective = obj
model.add([c1])
status = model.optimize()
print(model.objective.value)

Requires Python >= 3.9; swiglpk (GLPK interface) installs by default but optional solvers (cplex, gurobipy, scipy, osqp) must be installed separately if needed.

Verify before relying

• Whether the package handles numerical stability or precision guarantees for large-scale problems.
• Performance characteristics when solving problems with thousands of variables or constraints.
• Completeness of support for all GLPK solver features through the swiglpk interface.

Package facts

Evidence: optlang-1.9.1-py2.py3-none-any.whl

Keywords: optimization, mathematical programming, heuristic optimization, sympy

Tags

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