Modern optimization methods in Python

This tutorial will introduce modern tools for solving optimization problems. We will start by introducing the cost function, and it’s use in local and global optimization. We will then address how to monitor and diagnose your optimization convergence and results, tune your optimizer, and utilize compound termination conditions. This tutorial will discuss building and applying box constraints, penalty functions, and symbolic constraints. We will then demonstrate methods to efficiently reduce search space through the use of robust optimization constraints and kernel transformations. Real-world inverse problems can be expensive, thus we will show how to enable your optimization to seamlessly leverage parallel computing. Large-scale optimizations also can greatly benefit from efficient solver restarts and the saving of state. This tutorial will cover using asynchronous computing for results caching and archiving, dynamic real-time optimization, and dimensional reduction. Next we will discuss new optimization methods that leverage parallel computing to perform fast global optimizations and n-dimensional global searches.

The audience need not be an expert in optimization, but should have interest in solving real-world optimization problems. We will begin with introductory optimizations, learning how to build confidence in understanding your results. By the end of the tutorial, participants will have knowledge of how to use modern constrained nonlinear optimization tools.