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.
Supply Chain Network Design: A Practical Guide
In an era of global disruptions, sustainability demands, and rising customer expectations, Supply Chain Network Design (SCND) has become a vital capability for business success. Imagine a world where every product reaches customers faster, at lower costs, and with minimal environmental impact. This guide will show you how to make that vision a reality by designing an optimized, resilient, and future-proof supply chain.
Top Ways AMPL is Reducing the Skill Gap in Today’s Optimization Landscape
In the rapidly evolving field of optimization, bridging the skill gap between novice users and seasoned experts has become crucial. AMPL is at the forefront of this transformation, making optimization more accessible and intuitive for a broader audience. Here’s how AMPL is reducing the skill gap and empowering users to leverage their data more effectively:
Three Ways AMPL’s Natural and Intuitive Syntax Sets It Apart
Mathematical optimization is a powerful tool for solving complex problems across industries. However, the technical barrier to entry can be high. Enter AMPL (A Mathematical Programming Language), a modeling language designed to bridge the gap between mathematical formulation and computational solution. While AMPL offers many advantages, its syntax stands out as a key differentiator. Here are three ways it’s uniquely designed to make your optimization journey smoother: