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Creating scalable and parallelizable code in a scripting language like Python requires careful thought. This series explores ways to model optimization applications in Python using Pyomo,
an open source tool that can be used to define symbolic problems, create concrete problem
instances, and solve these instances with standard solvers. Part 1 covers the basics of modeling optimization applications using Pyomo and Python.
This article shows how to scale a Python linear optimization horizontally by combining Pyomo
with the Tornado web server, and using RabbitHQ as the message bus between the layers.
Explore ways to model optimization applications in Python using
Python Optimization Modeling Objects (Pyomo), an open source tool. You can use Pyomo to
define symbolic problems, create concrete problem instances, and solve these instances
with standard solvers. This article series shows how to leverage Pyomo's ability to
integrate with Python to model optimization applications. This first article covers the
basics. Part 2 shows how to add more tools and build a scalable architecture.