banach空间中多目标问题的弱pareto最优性

M. Minami
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引用次数: 3

摘要

在上一篇论文([5])中,我们研究了局部凸线性拓扑空间上的普通多目标凸规划,其目标函数和约束函数是连续凸的,但并不总是Gateaux可微分的。在这种情况下,我们证明了由一个次微分公式给出的广义Kuhn-Tucker条件对于弱Pareto最优是充分必要的。本文考虑了Banach空间上目标函数和约束函数局部为Lipschitzian但不总是凸的普通多目标规划,并导出了Clarke广义梯度([1])给出的Kuhn-Tucker形式作为弱Pareto最优的必要条件。定理2.1是对Schechter([6])的定理1.1的推广,该定理1.1涉及具有标值目标函数的普通程序。在本文中,X和X*为实巴拿赫空间及其连续对偶,其原点分别记为0和0*。用0表示空集。
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WEAK PARETO OPTIMALITY OF MULTIOBJECTIVE PROBLEM IN A BANACH SPACE
In the previous paper ([5]), we studied the ordinary multiobjective convex program on a locally convex linear topological space in the case that the objective functions and the constraint functions were continuous and convex, but not always Gateaux differ entiable. In the case, we showed that the generalized Kuhn-Tucker conditions given by a subdifferential formula were necessary and sufficient for weak Pareto optimum. In this paper, we consider the ordinary multiobjective program on a Banach space in the case that objective functions and constraint functions are locally Lipschitzian but not always convex, and derive Kuhn-Tucker forms given by Clarke's generalized gradients ([1]) as necessary conditions for weak Pareto optimum. Theorem 2.1 is a generalization of Theorem 1.1 of Schechter ([6]) which is concerned to ordinary pro gram with a scalar-valued objective function. In this paper, X and X* are a real Banach space and its continuous dual, whose origins are denoted by 0 and 0*, respectively. By 0 we denote the empty set.
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A STOCHASTIC APPROXIMATION WITH A SEQUENCE OF DEPENDENT RANDOM VARIABLES SILENT-NOISY DUEL WITH UNCERTAIN EXISTENCE OF THE SHOT ON CERTAIN APPROXIMATIONS OF POWER OF A TEST PROCEDURE USING TWO PRELIMINARY TESTS IN A MIXED MODEL WEAK PARETO OPTIMALITY OF MULTIOBJECTIVE PROBLEM IN A BANACH SPACE RANK TESTS OF PARTIAL CORRELATION
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