scikit-opt VS fast-cma-es

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