---
id: docplex
version: "2.32.264"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# docplex — The IBM Decision Optimization CPLEX Modeling for Python

License: permissive · Maintenance: active · Downloads: 78.4K/mo

## What it is and what it does

Docplex is IBM's Python modeling library for building optimization problems that can be solved by IBM ILOG CPLEX Optimization Studio. It provides two main modules: docplex.mp for linear and quadratic programming, and docplex.cp for constraint programming. The library lets you define decision variables, constraints, and objectives in Python, then submit them to a CPLEX solver for execution.

The package is designed for developers who need to add optimization capabilities to applications—scheduling, resource allocation, planning, or other decision-making problems. It requires only six as a runtime dependency but mandates that CPLEX Optimization Studio be installed separately on your system to actually solve models. Recent versions add support for running OPL models directly from Python and work with modern Python versions including 3.13 and 3.14.

Use it for:

- Build linear and quadratic programming models in Python and solve them with CPLEX without writing solver-specific syntax
- Embed constraint programming problems into Python applications for scheduling, sequencing, or resource optimization tasks
- Run OPL (Optimization Programming Language) models from Python using native integration instead of external .dat files
- Extract and process optimization results (variables, objectives, solution pools) in Python-friendly formats like dictionaries and pandas DataFrames
- Develop end-to-end optimization pipelines that combine data preparation, model building, solving, and result analysis in a single Python workflow

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Modeling library for building and solving mathematical optimization problems using IBM CPLEX and CP Optimizer solvers, with two namespaces for linear/quadratic and constraint programming respectively.

Yes, with conditions. Install docplex if you have IBM ILOG CPLEX Optimization Studio already deployed or can obtain it, and you need to model optimization problems in Python. The library is actively maintained, permissively licensed, and well-integrated with Python data tools. Without CPLEX Studio installed separately, the package alone cannot solve models—it is a modeling layer only. The high install friction reflects this external dependency, not the package quality.

## Install

pip install docplex
uv add docplex
poetry add docplex

## Installing docplex

Before you install: High install friction: the package itself is straightforward, but solving models requires IBM ILOG CPLEX Optimization Studio V12.8 or later to be installed separately on your machine. Maintenance is active with recent releases.

License in practice: Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most projects that can meet the external CPLEX solver dependency.

Quickstart:

pip install docplex

from docplex.mp.model import Model

mdl = Model(name='example')
x = mdl.continuous_var(name='x')
y = mdl.continuous_var(name='y')
mdl.maximize(x + y)
mdl.add_constraint(x + y <= 10)
solution = mdl.solve()

IBM ILOG CPLEX Optimization Studio V12.8 or later must be installed on your machine to solve models; the package alone only provides modeling capabilities.

Verify before relying:

- Whether numpy 2.0 compatibility mentioned in changelog applies to current version 2.32.264
- Specific Python version support (requires_python is unspecified in metadata)
- Whether OPL model support in 2.32.264 requires additional CPLEX Studio features or configuration

## Package facts

- License: Apache 2.0 (permissive)
- Python support: unspecified
- Install friction: high
- Maintenance: active
- Downloads: 78.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

mathematical optimization modeling, cplex python interface, constraint programming solver, linear programming library, ibm decision optimization, quadratic optimization python, cp optimizer modeling, optimization-modeling, ibm-cplex, constraint-programming

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