Titlebook: Global Optimization with Non-Convex Constraints; Sequential and Paral Roman G. Strongin,Yaroslav D. Sergeyev Book 2000 Springer Science+Bus

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書(shū)目名稱Global Optimization with Non-Convex Constraints影響因子(影響力)




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https://doi.org/10.1007/978-1-4615-4677-1Computer; STATISTICA; algorithms; computer science; global optimization; mathematics; optimization; science

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Parallel Global Optimization Algorithms and Evaluation of the Efficiency of ParallelismThe general global optimization problem of finding a global minimizer .* and the global minimum .(.*) of the multiextremal function .(.) defined over a domain M, i.e., ., arises in different applications and numerical methods are used to find .-optimal solutions to this problem.

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Global Optimization under Non-Convex Constraints — The Index ApproachConsider the constrained global minimization problem . where the objective function ., henceforth denoted ..., i.e., ...(.) = .(.), and left-hand sides .., 1 ≤ . ≤ ., of the constraints are assumed to be Lipschitzian respectively with constants .., 1 ≤ . ≤ . + 1, and, in general, are multi-extremal.

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