Finding Minima in Complex Landscapes: Annealed, Greedy and Reluctant Algorithms
Pierluigi Contucci, Cristian Giardina', Claudio Giberti, Cecilia Vernia
Abstract
We consider optimization problems for complex systems in which the cost function has a multivalleyed landscape. We introduce a new class of dynamical algorithms which, using a suitable annealing procedure coupled with a balanced greedy-reluctant strategy drive the systems towards the deepest minimum of the cost function. Results are presented for the Sherrington-Kirkpatrick model of spin-glasses.
Create a lesson
Related papers
Unifying spherical convolutions through lifting
J. D. McEwen
Perturbed isotropic oscillators with inversion symmetry
Antonella Marchesiello, Rita Mastroianni, Giuseppe Pucacco
Critical Ising model as the M(4,3) minimal model of conformal field theory
Dmitry Chelkak, Konstantin Izyurov, Kalle Kytölä
A Sklar-Type Decomposition for Positive Binary Distributions and a Gibbs Interpretation
Yi-Yu Lin
Lieb-Robinson bounds for irreversible dynamics via adaptive Dyson expansion
Carla Rubiliani, Marius Wesle, Tom Wessel
An elementary approach to the Szegő theorems
Israel Klich, Ravisankar Rajagopal