数学优化问题的混合障碍-惩罚方法的构造全局收敛性

Q4 Decision Sciences Pesquisa Operacional Pub Date : 2020-01-01 DOI:10.1590/0101-7438.2020.040.00217467
Porfirio Suñagua, A. Oliveira
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引用次数: 1

摘要

。本文在barrier和penalty泛型算法的基础上,提出了一种求解约束非线性规划问题的泛型混合双参数barrier-penalty方法。当可行集由等式和不等式函数约束定义时,可以提供显式的障碍函数和惩罚函数。在这种情况下,约束函数和目标函数的连续性和可微性可以继承到惩罚函数上。本工作的主要贡献是建设性地证明了所提出的混合方法生成的序列具有全局收敛性。证明分别使用了障碍法和惩罚法全局收敛的主要结果。最后,对于一些简单的非线性问题,我们明确地推导了混合障碍-惩罚函数,并举例说明了本文所定义的所有函数。并实现了混合法迭代点生成的MATLAB代码。
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A CONSTRUCTIVE GLOBAL CONVERGENCE OF THE MIXED BARRIER-PENALTY METHOD FOR MATHEMATICAL OPTIMIZATION PROBLEMS
. In this paper we develop a generic mixed bi-parametric barrier-penalty method based upon barrier and penalty generic algorithms for constrained nonlinear programming problems. When the feasible set is defined by equality and inequality functional constraints, it is possible to provide an explicit barrier and penalty functions. If such case, the continuity and differentiable properties of the restrictions and objective functions could be inherited to the penalized function. The main contribution of this work is a constructive proof for the global convergence of the sequence generated by the proposed mixed method. The proof uses separately the main results of global convergence of barrier and penalty methods. Finally, for some simple nonlinear problem, we deduce explicitly the mixed barrier–penalty function and illustrate all functions defined in this work. Also we implement MATLAB code for generate iterative points for the mixed method.
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来源期刊
Pesquisa Operacional
Pesquisa Operacional Decision Sciences-Management Science and Operations Research
CiteScore
1.60
自引率
0.00%
发文量
19
审稿时长
8 weeks
期刊介绍: Pesquisa Operacional is published each semester by the Sociedade Brasileira de Pesquisa Operacional - SOBRAPO, performing one volume per year, and is distributed free of charge to its associates. The abbreviated title of the journal is Pesq. Oper., which should be used in bibliographies, footnotes and bibliographical references and strips.
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