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fast-cma-es reviews and mentions
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Optimization problem with complex constrain
essentially the accumulated value of the portfolio after 50 years not clear to me how this can be linear - looks quite "exponential" without knowing the details. Can you exploit the "has to be greater than 0" condition to simplify the constraint into a linear one? "because at each time step there will be a decision" probably means the answer is "no". But don't overestimate the complexity of nonlinear optimizaiton (see for instance https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/CryptoTrading.adoc ), most of the complexity is hidden in the algorithm itself not visible for the user.
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what methods can be used to solve a TP-BVP with variable control?
What about combining a fast numerical integrator like https://github.com/esa/torchquad or https://github.com/AnyarInc/Ascent with a fast parallel CMA-ES implementation like https://github.com/dietmarwo/fast-cma-es/blob/master/fcmaes/cmaescpp.py ? A numerical integrator allows you to implement variable control and a fast non-derivative optimizer can solve any related optimization problem.
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Quality Diversity Optimization for Expensive Simulations
A new tutorial how to apply QD-optimization to expensive simulations: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Diversity.adoc .
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New Fast Python CVT MAP-Elites + CMA-ES implementation
There is a new implementation of Python CVT MAP-Elites + CMA-ES available. It is presented at https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/MapElites.adoc applying it to ESAs very hard Cassini2 space mission planning optimization benchmark.
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Performance of Evolutionary Algorithms for Machine Learning
I tried to answer these questions in EvoJax.adoc
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Optimization for Quantum Computer Simulations
Here https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Quant.adoc is a new tutorial how to apply optimization in the context of simulated quantum algorithms. It is based on https://qiskit.org/textbook/ch-applications/vqe-molecules.html#Example-with-a-Single-Qubit-Variational-Form but provides more reliable methods utilizing parallelism. This makes not much sense (yet) when the backend is a real quantum computer, but most simulators scale bad when using multi-threading or an GPU. So it is better to switch parallelism off for the simulation and utilize the better scaling parallel optmization provides, specially if a modern many-core CPU is available.
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Transaction and Payment Optimization Problem
https://github.com/dietmarwo/fast-cma-es/blob/master/examples/subset.py implements the problem using parallel continuous optimization collecting different optimal solutions. Not much faster than GLPK_MI, but utilizing modern many-core CPUs when you are looking for a list of alternative solutions. Increase the number of retrys when you want more solutions.
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A new fast local search heuristic for a location problem
Do you mind if I apply the generic optimization approach shown here: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/OneForAll.adoc to this problem to compare results? I see you collected a huge number of benchmark instances. Are there solutions proven to be optimal availabe for these?
- New generic method to solve MMKP and VRPTW
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29 Python real world optimization tutorials
using Python you may get some inspiration here: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Tutorials.adoc
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A note from our sponsor - SaaSHub
www.saashub.com | 30 Apr 2024
Stats
dietmarwo/fast-cma-es is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of fast-cma-es is Python.
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