Improving simulated annealing through derandomization

DOI10.1007/s10898-016-0461-1zbMath1372.90084arXiv1505.03173OpenAlexW1765974657MaRDI QIDQ2397439

Luke Bornn, Mathieu Gerber

Publication date: 22 May 2017

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1505.03173

zbMATH Keywords

global optimizationsimulated annealingthreshold acceptingquasi-Monte Carlorandomized quasi-Monte Carlo

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (2)

On the convergence rate issues of general Markov search for global minimum ⋮ The continuous single-source capacitated multi-facility Weber problem with setup costs: formulation and solution methods

Uses Software

• Algorithm 823

Cites Work

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• Global optimization of statistical functions with simulated annealing
• Point sets and sequences with small discrepancy
• A modification to the new version of the Price's algorithm for continuous global optimization problems
• Threshold accepting: A general purpose optimization algorithm appearing superior to simulated annealing
• Fast simulated annealing in \(\mathbb R^d\) with an application to maximum likelihood estimation in state-space models
• On the convergence of ``threshold accepting
• Adaptive simulated annealing for optimization in signal processing applications
• Adaptive random search in quasi-Monte Carlo methods for global optimization
• Very fast simulated re-annealing
• Convergence properties of simulated annealing for continuous global optimization
• Diffusions for Global Optimization
• Recursive Stochastic Algorithms for Global Optimization in $\mathbb{R}^d $
• Simulated annealing process in general state space
• Convergence theorems for a class of simulated annealing algorithms on ℝ^d
• Simulated annealing for maximum a posteriori parameter estimation of hidden Markov models
• Convergence of simulated annealing using Foster-Lyapunov criteria
• Metropolis-Type Annealing Algorithms for Global Optimization in $\mathbb{R}^d $
• Localization of Search in Quasi-Monte Carlo Methods for Global Optimization
• Modeling Nonstationary Processes Through Dimension Expansion
• Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods
• Sequential Quasi Monte Carlo
• Extensible Grids: Uniform Sampling on a Space Filling Curve
• Algorithm 823
• Remarks on a Multivariate Transformation
• Convergence of a simulated annealing algorithm for continuous global optimization.

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