SB Parameters fine-tuning #105
Description
Currently, the parameters of the SB algorithm are hardcoded. Though they work quite well on unconstrained problems, we noticed a huge performance drop on constrained problems converted to QUBO/Ising formulations.
The purpose of this issue is to start a discussion and share ideas on how these parameters could be made scalable. First ideas include pre-determined sets of parameters for given problem typologies, machine learning for fine-tuning, grid-searches, ...
All help is welcome!