This paper discusses a general recursive algorithm for the minimization of a nonlinear objective function subject to nonlinear inequality and equality constraints. The proposed method includes both Wilson's and Ro...
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Despite of their excellent numerical performance for solving practical nonlinear programming problems, the theoretical convergence behavior of generalized reduced gradient algorithms has been investigated very seldom ...
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ABSTRACT: The optimization of real‐time operations for a single reservoir system is studied. The objective is to maximize the sum of hourly power generation over a period of one day subject to constraints of hourly p...
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A modified flexible tolerance method for nonlinear optimization is presented. The modification incorporates a random search technique with the flexible tolerance method, making the flexible tolerance method more effic...
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In this paper, we propose a new multiattribute decision making (MADM) method based on the proposed nonlinear programming (NLP) model, the Gini coefficient, and the proposed score function (SF) of interval-valued intui...
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We consider a local solution of a nonlinear programming problem satisfying the condition of Mangasarian and Fromovitz, We first compute an estimate of the variation in the multipliers, relating it to the variation of ...
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In this paper, we provide novel generalizations of the D-type-I functions by relaxing the D-Preinvexity functions using the mean-valued theorem. Under these new generalizations of the DPreinvexity type-I functions, we...
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In this paper, we provide novel generalizations of the D-type-I functions by relaxing the D-Preinvexity functions using the mean-valued theorem. Under these new generalizations of the DPreinvexity type-I functions, we also study the optimality solutions for multiple objective nonlinear programming problems. We also establish and illustrate the duality theorems (weak, strong, and converse) for the Wolf-type and Mond-Weir-type for multiple objective nonlinear programming problems using these new generalizations of the D-Preinvexity type-I functions.
In this paper a fast computational method for a class of nonlinear bilevel programming problems is proposed. In these problems, the lower-level problem can be decomposed into some paratactic and independent sub-proble...
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ISBN:
(纸本)9781424406043
In this paper a fast computational method for a class of nonlinear bilevel programming problems is proposed. In these problems, the lower-level problem can be decomposed into some paratactic and independent sub-problems. First, by Karush-Kuhn-Tucker optimality, the stationary-points of these sub-problems corresponding to the upper-level variables can be determined. As a result, this kind of nonlinear bilevel programming is transformed into a single level optimization problem. The hybrid genetic algorithm is then adopted to solve this single optimization problem. Simulation results on 18 benchmark problems show that the proposed method is able to solve effectively the bilevel programming problems such that their global optima can be found, with high convergent speed and less computational cost compared to other existing algorithms.
Filter methods, introduced by Fletcher and Leyffer for nonlinear programming are characterized by the use of the dominance concept of multi-objective optimization, instead of a penalty parameter whose adjustment can b...
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ISBN:
(数字)9783642161674
ISBN:
(纸本)9783642161667
Filter methods, introduced by Fletcher and Leyffer for nonlinear programming are characterized by the use of the dominance concept of multi-objective optimization, instead of a penalty parameter whose adjustment can be problematic. This paper presents a way to implement a filter based approach to solve a nonlinear bilevel programming problem in a linear approximations framework. The approach presented is based on the trust region idea from nonlinear programming, combined with filter-SQP algorithm, smooth and active sets techniques. The restoration procedure introduced in our algorithm consists in computing a rational solution.
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