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Titlebook: Non-Convex Multi-Objective Optimization; Panos M. Pardalos,Antanas ?ilinskas,Julius ?ilinsk Book Aug 20171st edition Springer Internationa

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樓主: 冰凍
41#
發(fā)表于 2025-3-28 16:05:26 | 只看該作者
Worst-Case Optimal Algorithmsl algorithms are constructed for the cases of passive (non-adaptive) and sequential (adaptive) search in [.]. These results are the generalization to the multi-objective case of the results by Sukharev who investigated the worst-case optimal single-objective optimization algorithms in?[., .].
42#
發(fā)表于 2025-3-28 18:57:39 | 只看該作者
43#
發(fā)表于 2025-3-28 23:14:28 | 只看該作者
44#
發(fā)表于 2025-3-29 04:33:35 | 只看該作者
Approximation and Complexitycomplexity theories have been developed for the investigation of problems of continuous nature. For the fundamentals of the complexity of real number algorithms we refer to?[., .], and for the complexity of problems of mathematical programming to?[., ., .].
45#
發(fā)表于 2025-3-29 09:47:50 | 只看該作者
Statistical Models Based Algorithmsmization point of view, properties of . as non-differentiability, non-convexity, and multimodality cannot be excluded. Difficulties of the black-box global optimization of expensive functions are well known from the experience gained in the single-objective case.
46#
發(fā)表于 2025-3-29 12:47:57 | 只看該作者
47#
發(fā)表于 2025-3-29 17:21:42 | 只看該作者
Multi-Objective Branch and Boundpresents an unexplored subset of feasible decisions. The iteration has three main components: selection of the subset to be processed, branching corresponding to subdivision of the subset, and bound calculation.
48#
發(fā)表于 2025-3-29 22:23:15 | 只看該作者
49#
發(fā)表于 2025-3-30 03:45:33 | 只看該作者
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