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Non-Convex Optimization by Hamiltonian Alternation
[article]
2022
arXiv
pre-print
A major obstacle to non-convex optimization is the problem of getting stuck in local minima. We introduce a novel metaheuristic to handle this issue, creating an alternate Hamiltonian that shares minima with the original Hamiltonian only within a chosen energy range. We find that repeatedly minimizing each Hamiltonian in sequence allows an algorithm to escape local minima. This technique is particularly straightforward when the ground state energy is known, and one obtains an improvement even
arXiv:2206.14072v1
fatcat:wmgkeeldxjhfnn7s7btuo2pptq