On the convergence rate issues of general Markov search for global minimum

DOI10.1007/s10898-017-0544-7zbMath1386.60247OpenAlexW2733784916WikidataQ59609470 ScholiaQ59609470MaRDI QIDQ1685583

Dawid Tarłowski

Publication date: 14 December 2017

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

Full work available at URL: https://doi.org/10.1007/s10898-017-0544-7

zbMATH Keywords

global optimizationsimulated annealingconvergence rateself-adaptationaccelerated random searchMarkov search

Mathematics Subject Classification ID

Discrete-time Markov processes on general state spaces (60J05) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Asymptotic stability in control theory (93D20)

Related Items (2)

On geometric convergence rate of Markov search towards the fat target ⋮ Average convergence rate of evolutionary algorithms in continuous optimization

Uses Software

• Global Optimization Toolbox For Maple
• MultiMin

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The robust constant and its applications in random global search for unconstrained global optimization
• Nonautonomous stochastic search in global optimization
• Markov chains and stochastic stability
• Global convergence of discrete-time inhomogeneous Markov processes from dynamical systems perspective
• Random linkage: A family of acceptance/rejection algorithms for global sation
• Convergence and first hitting time of simulated annealing algorithms for continuous global optimization
• Convergence of the simulated annealing algorithm for continuous global optimization
• Evolution strategies. A comprehensive introduction
• Quantitative bounds on convergence of time-inhomogeneous Markov chains
• Global optimization in action. Continuous and Lipschitz optimization: algorithms, implementations and applications
• Nonautonomous stochastic search for global minimum in continuous optimization
• Derivative-free optimization: a review of algorithms and comparison of software implementations
• Improving simulated annealing through derandomization
• Monotonous random search on a torus: integral upper bounds for the complexity
• Stochastic global optimization.
• Optimization of stochastic systems. Topics in discrete-time systems
• Randomized Smoothing for Stochastic Optimization
• Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations
• Convergence properties of simulated annealing for continuous global optimization
• Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains
• Pure Random Search with exponential rate of convergency
• Convergence properties of stochastic optimization procedures
• Minimization by Random Search Techniques
• Convergence theorems for a class of simulated annealing algorithms on ℝ^d
• Numerical Optimization
• Convergence of simulated annealing using Foster-Lyapunov criteria
• On Accelerated Random Search
• Real Analysis and Probability
• On the convergence rate of the Markov homogeneous monotone optimization method
• Convergence of a simulated annealing algorithm for continuous global optimization.
• Theory of genetic algorithms

This page was built for publication: On the convergence rate issues of general Markov search for global minimum