{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"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.","skillfed_tags":["optimization","linear-programming","symbolic-math"],"use_cases":["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."],"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()\u2014the same code can then switch between GLPK, CPLEX, Gurobi, or other solvers without rewriting the problem formulation.\n\nThe 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\u20133.13, and carries no known security vulnerabilities. It's commonly used in scientific and operations research workflows where problem portability and clean problem definition matter.","worth_installing":"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."},"id":"optlang","links":{"html":"https://skillfed.io/packages/optlang","md":"https://skillfed.io/packages/optlang.md","pypi":"https://pypi.org/project/optlang/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-02","license_spdx":null,"license_treatment":"permissive","name":"optlang","python_support":"supports_current","summary":"Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK)."},"popularity":{"monthly_downloads":83241,"position":14089,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.9.1"}