ExpensiveOptimBenchmark VS fast-cma-es

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.

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.

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.

A new tutorial how to apply QD-optimization to expensive simulations: https://github.com/dietmarwo/fast-cma-es/blob/master/tutorials/Diversity.adoc .

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.

I tried to answer these questions in EvoJax.adoc

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.

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.

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?

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