Random Restarts in Global Optimization
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Summary
Stochastic multistart methods for global optimization, which combine local search with random initialization, and their parallel implementations are studied, and it is shown that in a minimax sense the optimal restart distribution is uniform.
- Type
- article
- Published
- 2009-12-07
- Cited by
- 28
- References
- 11
- OpenAlex
- https://openalex.org/W13549336
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:349492
Keywords
Computer science, Environmental science
References
- Parallel and distributed computation
- Numerical methods for unconstrained optimization and nonlinear equations
- Stochastic techniques for global optimization: A survey of recent advances
- Parallel Speed-Up of Monte Carlo Methods for Global Optimization
- Moment Convergence of Sample Extremes
- Matrix Iterative Analysis
- Minimization by Random Search Techniques
- The Asymptotic Theory of Extreme Order Statistics
- Real and Complex Analysis
- A Global Optimization
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- Stochastic Speculative Computation Method and its Application to Monte Carlo Molecular Simulation
- Improving the run time of the (1 + 1) evolutionary algorithm with luby sequences
- Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
- Passive Gust Loads Alleviation in a Truss-Braced Wing Using an Inerter-Based Device
- Analysis of the Efficiency of Speculative Parallel Computation
- Restarting Algorithms: Sometimes There Is Free Lunch
- Stochastic optimization with adaptive restart: a framework for integrated local and global learning
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- Learning to Initialize Gradient Descent Using Gradient Descent
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