Almost surely convergent global optimziation algorithm using noise-corrupted observations
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Publication:1579659
DOI10.1023/A:1004661730014zbMath0966.90061OpenAlexW40564758MaRDI QIDQ1579659
Publication date: 6 May 2001
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1023/a:1004661730014
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Cites Work
- Optimization by Simulated Annealing
- Simulated annealing simulated
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- Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
- The uniform convergence of nearest neighbor regression function estimators and their application in optimization
- On the numerical solutions of stochastic optimization problem
- Annealing of Iterative Stochastic Schemes
- A Kiefer-Wolfowitz algorithm with randomized differences
- A Globally Convergent Stochastic Approximation
- Random Search in the Presence of Noise, with Application to Machine Learning
- Asymptotic Global Behavior for Stochastic Approximation and Diffusions with Slowly Decreasing Noise Effects: Global Minimization via Monte Carlo
- Stochastic Estimation of the Maximum of a Regression Function
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