Optimization Direct at INFORMS Annual Meeting, Seattle, 20-23 October, 2019

Technology Tutorial at INFORMS

A DOCplex and ODH|CPLEX Python primer

Sunday, October 20, 1:30pm-2:15pm

This short tutorial shows participants how to build a basic model using the DOCplex API in Python. This session includes setting the Python environment, reading data from a csv or spreadsheet, creating variables, objective functions, constraints, solving the model, and returning the results. Additionally this session points the participants to further reading so that they may expand their capabilities. Furthermore we will present the brand new ODH|CPLEX API for Python, which improves solution times for large models.

Pre meeting workshops at INFORMS, Seattle

Methodologies for Deploying applications that combine machine learning tools and optimization solvers (CPLEX/ ODH|CPLEX)

Saturday, October 19, 1pm-3:30pm Presented by: Robert Ashford & Alkis Vazacopoulos

Organizations are increasingly hiring Data Scientists with Open Source skills. They leverage the capabilities and work with Open Source tools like R, Python, Spark, as well as integration with CPLEX and large data. Come learn how to integrate with Open Source tools to enable clients to get the best of both worlds (Open Source programming and the Modeler GUIs for those who prefer not to code).

Furthermore we will review the latest developments/results in CPLEX Optimization Studio and the new ODH+CPLEX.

Introduction & Recent Optimization Case Studies
Alkis Vazacopoulos & Sumeet Parashar, Optimization Direct

Practical Guidelines for Solving Difficult Mixed
Integer Programs, Version 2.0
Ed Klotz, IBM

Solving Hard Mixed Integer programming problems with ODH|CPLEX
Robert Ashford, Optimization Direct

Beyond Python: Julia as a framework for Optimization
Martin Shell


PDFs Slides 1   Slides 2   Slides 3   Slides 4   Slides 5   Slides 6