IBM ILOG CPLEX has been a well known and widely used large-scale solver for over three decades. Its efficiency and robustness have been demonstrated through varied applications in thousands of commercial installations worldwide.
Linear and quadratic optimization in continuous and integer variables. Support is provided for both convex and nonconvex quadratic objectives, and for convex quadratic constraints.
Logical implications in the form of “indicator” constraints. Convex quadratic expressions in objectives, and convex quadratic constraints of elliptic and conic types.
For continuous problems, primal and dual simplex, interior-point (barrier); for integer problems, advanced branch-and-bound with presolve, feasibility heuristics, and cut generators. For continuous problems comprised mostly or entirely of linear network flow constraints, network simplex.
Shared-memory parallel processing for barrier, branch-and-bound. Concurrent optimization by several methods to determine best choice. Special facilities for parameter tuning and infeasibility diagnosis.
CPLEX for AMPL is a specialized version of the solver, designed exclusively for use within the AMPL environment. It includes key enhancements that make the solver even more powerful, allowing it to handle larger, more complex optimization problems with greater efficiency and speed.
CPLEX for AMPL is more than just a solver—it’s an enhanced optimization experience. AMPL provides a purpose-built solver interface that allows CPLEX to handle large-scale optimization problems with greater efficiency and scalability. By leveraging AMPL’s modeling language, users gain faster insights, improved workflow efficiency, and a better return on their solver investment, all while maintaining the flexibility and power of CPLEX.
AMPL’s solver interface optimizes problem structures before they reach the solver, reducing computation time and improving efficiency. By intelligently restructuring mathematical programs, AMPL allows CPLEX to solve models faster and more effectively, making it easier to tackle complex decision-making challenges across industries.
One of AMPL’s key advantages is its ability to automatically reformulate problems into solver-friendly formats. This ensures that even highly complex or nonlinear models are structured in a way that CPLEX can process more efficiently, avoiding bottlenecks and improving convergence speed. Whether simplifying constraints, handling logical expressions, or restructuring large models, AMPL ensures optimal solver performance.
To help users get the most out of CPLEX, AMPL offers consulting services and dedicated support plans for solver tuning and model optimization. Our experts provide solver diagnostics, advanced tuning, and optimization strategies to improve computation times and solver performance. Whether refining a problem formulation or troubleshooting solver behavior, AMPL’s specialized services ensure users achieve the best possible results with CPLEX.
Access this world-class solver in the AMPL License Portal, available on Windows, Linux, and macOS
Clpex can be used with AMPL from various programming languages using our dedicated APIs
For Python enthusiasts, CPLEX is also accessible as a module with amplpy, blending seamlessly with your Python projects.
How to install using amplpy:
# Install Python API for AMPL:
$ python -m pip install amplpy --upgrade
# Install AMPL & solver modules:
$ python -m amplpy.modules install cplex # install CPLEX
# Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses):
$ python -m amplpy.modules activate <your-license-uuid>How to use:
from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="cplex", cplex_options="option1=value1 option2=value2")For learning optimization and benchmarking solvers – a one-time 1 month free trial of CPLEX is available with a free Community Edition license.
For building and testing optimization models with commercial solver integration and full support, ensuring a smooth path to enterprise deployment.
AMPL starting at
$300 / month
Billed annually, includes updates and basic support.
AMPL starting at
$395 / month
Billed annually, includes updates and basic support.
Build a custom plan – designed for large teams, multiple processes, high computational demands, or unique workflows.
All custom licenses built to your specific needs.
AMPL empowers businesses across diverse industries to make smarter decisions, improve efficiency, and maximize performance through optimization. From supply chain logistics to financial modeling, our expertise helps organizations tackle real-world challenges with precision and speed.
AMPL and CPLEX help financial institutions develop robust risk assessment and portfolio optimization models. By leveraging CPLEX’s powerful solver capabilities, firms can efficiently manage credit risk, optimize asset allocation, and streamline investment strategies while complying with regulatory constraints.
CPLEX and AMPL enable manufacturers to optimize production schedules, minimize waste, and balance supply with demand. With advanced solver capabilities, companies can dynamically adjust workflows, reduce downtime, and improve operational efficiency across global supply chains.
AMPL and CPLEX are widely used in logistics to solve large-scale routing, scheduling, and network flow problems. Businesses use our solver-enhanced modeling tools to reduce transportation costs, improve delivery reliability, and optimize fleet utilization while adapting to real-time constraints.
Get the most out of CPLEX with these essential resources—documentation, tutorials, and expert guides to help you optimize efficiently.
CPLEX for AMPL Documentation
Your go-to reference for using CPLEX, covering everything from installation to advanced solver techniques.
CPLEX Parameters & Options
Fine-tune CPLEX’s performance with a full list of solver parameters and configuration options.
MP Modeling Guide
Learn how to use the AMPL MP library to create flexible, solver-agnostic optimization models that work seamlessly with CPLEX.
CPLEX for AMPL on Google Colab
Run CPLEX with AMPL directly in your browser using pre-configured Google Colab notebooks—no installation required.
CPLEX for AMPL Change Log
Stay up to date with the latest improvements, bug fixes, and feature updates for the CPLEX-AMPL interface.
How to Use CPLEX for AMPL
Best practices for setting up CPLEX with AMPL, running models, and optimizing solver performance.
CPLEX excels in solving large-scale optimization problems across various domains, including logistics, supply chain management, finance, manufacturing, and energy. It’s particularly effective for problems involving complex decision-making processes and large datasets.
CPLEX can be used from several programming languages via AMPL APIs, including Python, R, Java, C++, C# and MATLAB. This flexibility allows users to integrate CPLEX into a wide range of applications and software environments.
Yes, CPLEX can be integrated with various data analysis and machine learning tools. Its compatibility via AMPL with popular programming languages and data science platforms enables seamless integration in complex data-driven optimization projects.
Key features of CPLEX include advanced presolving techniques, parallel processing capabilities, robust branch-and-cut algorithms for MIP, and specialized heuristics for quickly finding feasible solutions. These features collectively enhance its efficiency and solution quality.
CPLEX supports business decision-making by providing optimized solutions to complex problems. It helps companies reduce costs, improve operational efficiency, and make strategic decisions based on accurate, data-driven insights. Whether it’s optimizing supply chains, managing financial risks, or planning production, CPLEX provides a solid foundation for informed decision-making.
Access a complete optimization application building platform with simple pricing, or work with our team to design a custom package specifically for your business needs.
Get in touch to book a time for us to talk about your specific needs, and demo real solutions.
From startups to Fortune 500s, explore how companies use AMPL to optimize complex problems.