Branch- and bound algorithms for solving global optimization problems with Lipschitzian structure
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Publication:3789345
DOI10.1080/02331938808843322zbMath0645.90065OpenAlexW1982658920MaRDI QIDQ3789345
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Publication date: 1988
Published in: Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331938808843322
convergencebranch-and-boundglobal minimumLipschitz continuous functionspartition refinementconstrained global minimizationpartition selectionsequentially generated subintervals
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Cites Work
- Unnamed Item
- Unnamed Item
- On the global minimization of concave functions
- Extended univariate algorithms for \(n\)-dimensional global optimization
- A general class of branch-and-bound methods in global optimization with some new approaches for concave minimization
- On the convergence of global methods in multiextremal optimization
- Global optimization on convex sets
- A stochastic method for global optimization
- Convergence properties of stochastic optimization procedures
- Globally convergent methods for n-dimensional multiextremal optimization
- Axiomatic approach to statistical models and their use in multimodal optimization theory
- A Sequential Method Seeking the Global Maximum of a Function
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