The Future of Optimization is Shared: Introducing the AMPL Model Repository
Stop reinventing the wheel. Discover how a centralized hub for mathematical optimization models is accelerating innovation and bridging the gap between academia and industry. Introduction: The Challenge of Isolated Knowledge In the world of mathematical optimization, a common frustration unites business leaders, researchers, and modelers alike: the immense difficulty of finding the right model for […]
How the AMPL Ecosystem Helps Companies Grow
“From finance to supply chains, from energy to healthcare — today’s companies face complexity at every turn. Spreadsheets and ad-hoc tools are no longer enough. What’s needed is an ecosystem that connects data, analytics, forecasts, and optimization into decisions that drive growth. That’s exactly what the AMPL ecosystem delivers” From Requirements to Solutions Every company […]
Amplpy Best Practices
Supercharge Your Optimization with Our New AMPL & Python Guide Hello Amazing Customers! We’re pleased to share our comprehensive guide designed to help you master optimization modeling with AMPL and Python. Whether you’re fine-tuning supply chains, crunching data, or making smarter decisions, it’s your blueprint for smarter, faster, and more efficient solutions. What’s Inside? Here’s a quick […]
Official AMPL Extension for Visual Studio
We’re excited to announce the official release of the AMPL extension for Visual Studio Code is now available on the Visual Studio Marketplace! 🎉
Whether you’re modeling energy systems, optimizing supply chains, or teaching mathematical programming, this extension brings AMPL right into your favorite Python development environment.
From Patterns to Precision: Machine Learning & Mathematical Optimization for Real-World Challenges
Machine Learning (ML) and Mathematical Optimization (MO) are two powerful tools for addressing complex problems across various fields. While ML uses data to identify patterns and make predictions, MO focuses on finding the best possible solution within a defined set of constraints. Understanding their unique strengths, limitations, and synergistic potential enables effective application in problem-solving.
Breaking Barriers in Optimization: AMPL’s Early Results with NVIDIA cuOpt
In testing NVIDIA cuOpt, the results speak for themselves. | In the world of optimization, speed is everything. Whether optimizing supply chains, scheduling transportation, or tackling complex energy market decisions, the ability to solve large-scale mathematical programming problems quickly can mean the difference between an efficient operation and costly delays.
AMPL: A Specialized Optimization Language Built for Industry, Businesses, and Complex Tasks
Every business makes decisions—but not every business makes them optimally.
Optimization is the difference between guessing and knowing, between wasting resources and maximizing efficiency. It’s about finding the best possible solution to complex challenges—faster, smarter, and with real-world constraints in mind.
5 Reasons It’s Crucial to Use More Powerful Modeling Tools Like AMPL—Even in the Age of Python
Every business makes decisions—but not every business makes them optimally.
Optimization is the difference between guessing and knowing, between wasting resources and maximizing efficiency. It’s about finding the best possible solution to complex challenges—faster, smarter, and with real-world constraints in mind.
What is Optimization?
Every business makes decisions—but not every business makes them optimally.
Optimization is the difference between guessing and knowing, between wasting resources and maximizing efficiency. It’s about finding the best possible solution to complex challenges—faster, smarter, and with real-world constraints in mind.
Using ChatGPT for AMPL Models and Streamlit Apps
This guide will help you leverage ChatGPT to generate ideas for AMPL models, write these models, and create simple Streamlit apps for visualization. It is designed for data scientists, OR specialists, and students to save time and streamline their workflow.