fast-cma-es
pycma
fast-cma-es | pycma | |
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12 | 4 | |
106 | 1,032 | |
- | 1.7% | |
7.2 | 6.8 | |
6 months ago | about 1 month ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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fast-cma-es
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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
pycma
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How to solve Optimization Problem
There is a notebook on how to handle constraints with the pycma package: https://github.com/CMA-ES/pycma/blob/development/notebooks/notebook-usecases-constraints.ipynb
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Optiseek - a collection of single-objective optimization algorithms for multi-dimensional functions with a uniform format
It you want to implement more single objective meta heuristic, you should take a look at CMA-ES
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Code for PI2-CMA Policy Search
If anyone is wondering, I found a CMAES implementation here https://github.com/CMA-ES/pycma
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Metaheurisric : what neighborhood for a vector of real parameters ?
If it's not convex (multimodal, whatever), try an algorithm like CMA-ES for these sorts of problems https://github.com/CMA-ES/pycma. Other alternatives: particle swarm optimization or differential evolution.
What are some alternatives?
optiseek - An open source collection of single-objective optimization algorithms for multi-dimensional functions.
scikit-opt - Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
Multi-UAV-Task-Assignment-Benchmark - A Benchmark for Multi-UAV Task Allocation of an Extended Team Orienteering Problem
ExpensiveOptimBenchmark - Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Ascent - A fast and flexible C++ simulation engine and differential equation solver.
torchquad - Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX
evojax