Videos#

Leveraging AMPL, Python, and AI to Democratize Creation of Decision Optimization Applications#

📆 Thursday, July 11, 2024

Optimization is critical to the successful use of analytics in enterprises of all kinds. In this presentation, you’ll learn how you can create and deploy successful decision optimization applications, rapidly and reliably – by connecting AMPL’s powerful and intuitive modeling tools to Python’s extensive development ecosystem, and to the impressive code-writing abilities of AI. AMPL also reduces the modeling barrier substantially, through automated reformulations that enable a consistent interface to all of the most widely used solver packages. By breaking down a complex process into manageable steps, our approach is accessible to domain experts and software developers of varied skill levels and backgrounds.

Key Takeaways:

  • Learn how to leverage ChatGPT to generate ideas, build models, and write code effortlessly.

  • Understand the advantages of using AMPL for clean, easy-to-learn syntax that facilitates communication and collaboration.

  • Explore practical demonstrations on Google Colab and Streamlit for model creation, visualization, and stakeholder collaboration.

Who Should Attend:

  • Industry professionals seeking to enhance their optimization skills.

  • Students aiming to break into the tech industry amidst limited junior hires.

  • Educators interested in integrating cutting-edge tools into their curriculum.

Tools and Resources Highlighted:

  • AMPL: Experience the ease of a specialized optimization language.

  • ChatGPT: Simplify model building and coding.

  • Google Colab: Collaborate on pre-built models.

  • Streamlit: Visualize models and make informed decisions.

Speakers

  • Filipe Brandão: Head of Development, AMPL Optimization Inc.

  • Bob Fourer: President and Co-Founder of AMPLOptimization Inc.

Optimization modeling with AMPL, Nextmv, and Streamlit#

📆 Thursday, May 30, 2024

Decision science applications used in industry need to be adaptable to changing business requirements and accessible for stakeholder buy-in and team collaboration.

One way to achieve this is by integrating AMPL for modeling and solving, Nextmv for model deployment and testing, and Streamlit for visualization and user interaction. Together, these three technologies provide decision science and operations research teams the opportunity to accelerate real-world impact of decision models.

In this techtalk, join Filipe Brandão, AMPL Head of Development, and Nicole Misek, Nextmv VP of Engineering, as they discuss the value of integrating these tools together, share insights into the process, and walk through a live demo. They’ll do so through the lens of a facility location optimization example that leverages stochastic models and accounts for multiple constraints (e.g., transportation costs, labor availability, etc.).

Teaching, Learning, and Applying Optimization: AMPL’s Intuitive Modeling Meets the Python Ecosystem#

📆 Wednesday, December 13, 2023

Optimization is part of any program in Operations Research or Analytics, but the curriculum must steadily evolve to remain relevant. Following an introductory example, this presentation takes you on a tour through new developments in the AMPL modeling language and system that have been changing the ways that large-scale optimization is taught and learned:

  • A more natural approach to describing optimization problems. Students can write many common logical conditions, “not-quite-linear” functions, and nonlinear functions the way they think about them, without having to learn complicated and error prone reformulations.

  • A Python-first alternative to learning AMPL and model building. New teaching materials leverage the power of Jupyter notebooks and Google Colab to bring modern computing to the study of optimization.

  • Faster, easier importing of data and exporting of results. The AMPL Python interface (amplpy) efficiently connects model sets and parameters to Python’s native data structures and Pandas dataframes. An all-new spreadsheet interface reads and writes .xlsx and .csv files, with added support for two dimensional spreadsheet tables.

  • Streamlined application development. Python scripts can be turned quickly into illustrative applications using amplpy, Pandas, and the Streamlit app framework.

Adding Optimization to Your Applications#

In this INFORMS webinar, learn the fundamentals of AMPL’s model-based approach to building optimization applications quickly and reliably:

  • Understand of the optimization modeling lifecycle and the components of the modern optimization toolchain.

  • Appreciate the benefits of model-based optimization for fast development and reliable deployment, including the advantages of modeling and modeling languages versus programming and programming languages for optimization.

  • See case studies of successful model-based optimization applications across a variety of business types.

Introduction to the AMPL APIs#

Build optimization into your applications with AMPL’s programming interfaces for C++, C#, Java, MATLAB, Python, and R. Access AMPL models and run AMPL commands from your programs, and exchange data directly and efficiently between AMPL parameters & variables and your preferred data structures.