Convergence of Simulated Annealing Using Kinetic Langevin Dynamics

06/13/2022
by   Xuedong He, et al.
0

We study the simulated annealing algorithm based on the kinetic Langevin dynamics, in order to find the global minimum of a non-convex potential function. For both the continuous time formulation and a discrete time analogue, we obtain the convergence rate results under technical conditions on the potential function, together with an appropriate choice of the cooling schedule and the time discretization parameters.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/03/2021

Simulated annealing from continuum to discretization: a convergence analysis via the Eyring–Kramers law

We study the convergence rate of continuous-time simulated annealing (X_...
research
03/01/2017

Convergence rate of a simulated annealing algorithm with noisy observations

In this paper we propose a modified version of the simulated annealing a...
research
09/30/2020

Some Remarks on Replicated Simulated Annealing

Recently authors have introduced the idea of training discrete weights n...
research
02/06/2023

Unified Software Design Patterns for Simulated Annealing

Any optimization algorithm programming interface can be seen as a black-...
research
01/04/2018

Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation

Gradient Symbolic Computation is proposed as a means of solving discrete...
research
01/29/2019

An accelerated variant of simulated annealing that converges under fast cooling

Given a target function U to minimize on a finite state space X, a propo...
research
11/19/2020

On the convergence of an improved discrete simulated annealing via landscape modification

In this paper, we propose new Metropolis-Hastings and simulated annealin...

Please sign up or login with your details

Forgot password? Click here to reset