Optimization by Move--Class Deflation
Reimer Kuehn, Yu-Cheng Lin, Gerhard Poeppel
Abstract
A new approach to combinatorial optimization based on systematic move-class deflation is proposed. The algorithm combines heuristics of genetic algorithms and simulated annealing, and is mainly entropy-driven. It is tested on two problems known to be NP hard, namely the problem of finding ground states of the SK spin--glass and of the 3-D J spin-glass. The algorithm is sensitive to properties of phase spaces of complex systems other than those explored by simulated annealing, and it may therefore also be used as a diagnostic instrument. Moreover, dynamic freezing transitions, which are well known to hamper the performance of simulated annealing in the large system limit are not encountered by the present setup.
Create a lesson
Related papers
Robust Golden Ratio universality as a consequence of a circulant symmetry
Luka Paljk, Johannes Schmidt, Vladislav Popkov
Replica Transition at Measurement-Altered Ising Criticality
Kabir Khanna, Sara Murciano, Romain Vasseur
Scalene Yang-Baxter triples realize all nearest-neighbour Hamiltonians with global additive symmetries
Pramod Padmanabhan, Somnath Maity, Akash Sinha et al.
Graph-theoretic framework for semi-Markov networks: transient and stationary occupation probabilities, hitting times and weak ergodicity breaking
Julian Voits
Spectral optimization of spherical lattices and applications to classical and quantum interacting spin systems
Jonas Völler, Grigorios Makris, Fabian Hassler et al.
Entropic-ordered superfluid
Isabella Zane, Andrew Lucas