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Globally Convergent Interior-Point Algorithm for Nonlinear Programming

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出版年:Journal of Optimization Theory and Applications vol. 125, no. 3 (Jun 2005), p. 497
第一著者: Akrotirianakis, I
その他の著者: Rustem, B
出版事項:
Springer Nature B.V.
主題:
Algorithms
Optimization
オンライン･アクセス:Citation/Abstract
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520 3 |a This paper presents a primal-dual interior-point algorithm for solving general constrained nonlinear programming problems. The inequality constraints are incorporated into the objective function by means of a logarithmic barrier function. Also, satisfaction of the equality constraints is enforced through the use of an adaptive quadratic penalty function. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Finally, extensive computational results show that the algorithm can solve large and difficult problems in an efficient and robust way. 
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653 |a Optimization 
700 1 |a Rustem, B 
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