29 Python real world optimization tutorials

• fast-cma-es

  12 103 7.2 Python

  A Python 3 gradient-free optimization library

using Python you may get some inspiration here: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Tutorials.adoc

• ExpensiveOptimBenchmark

  1 19 3.9 Python

  Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions

  

For the problems with continous decision variables it is not trivial to come up with faster approaches on a modern many-core CPU. But even with discrete input (scheduling and planning) new continous optimizers can compete. The trick is to utilize parallel optimization runs and numba to perform around 1E6 fitness evaluations each second. Advantage is that it is much easier to create a fitness function than for instance to implement incremental score calculation in Optaplanner. And it is more flexible if you have to handle non-standard problems. For very expensive optimizations (like https://github.com/AlgTUDelft/ExpensiveOptimBenchmark) parallelization of fitness evaluation is more important than to use surrogate models.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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