Optimization infrastructure for the worldโs most complex decision systems
AMPL is a modern optimization platform used by organizations in energy, logistics, finance, and advanced analytics to design and deploy large-scale optimization models integrated directly into production systems.
Optimization infrastructure for the worldโs most complex decision systems.
AMPL is a modern optimization platform used by organizations in energy, logistics, finance, and advanced analytics to design and deploy large-scale optimization models integrated directly into production systems.
AMPL sits at the center of modern optimization systems
AMPL is designed for large-scale decision systems where optimization models involve millions of variables, complex discrete and nonlinear constraints, and evolving business rules that quickly exceed the capabilities of spreadsheets or ad-hoc scripting. These models are often embedded directly into operational workflows, connected to enterprise data pipelines, analytics environments, and production software systems. By separating model logic from solver technology, AMPL provides the flexibility to evaluate and deploy models across leading commercial and open-source solvers without rewriting model code. The result is a stable modeling platform capable of supporting mission-critical optimization systems that evolve and operate reliably over years of development and real-world use.
The AMPL Optimization Platform
Infrastructure for building and operating optimization systems
From modeling to deployment, AMPL provides the infrastructure required to build and operate large-scale optimization systems. Define complex models with a purpose-built modeling language, connect them to modern data and analytics environments, solve them with leading commercial solvers, and deploy them into production decision systems.
AMPL Modeling
Express complex optimization models with clarity and control
AMPLโs purpose-built modeling language allows teams to define complex optimization models clearly and maintainably, separating model logic from data and solver configuration.
Design models that scale from early experimentation to production deployment while maintaining full control over constraints, objectives, and solver behavior.