A parallel tensor algorithm for nonlinear optimization
作者:
D. Conforti,
L. Grandinetti,
R. Musmanno,
期刊:
Optimization Methods and Software
(Taylor Available online 1994)
卷期:
Volume 3,
issue 1-3
页码: 125-142
ISSN:1055-6788
年代: 1994
DOI:10.1080/10556789408805560
出版商: Gordon and Breach Science Publishers
关键词: unconstrained optimization;nonlinear optimization;curvilinear path;tensor algorithm;parallel computing;automatic differentiation
数据来源: Taylor
摘要:
A new iterative algorithm for solving unconstrained optimization problems is introduced. It is based on the construction, at each iteration, of a curvilinear path to be searched for a local solution. Since the curvilinear path is denned by using a tensor of third order partial derivatives of the objective function, efficient and reliable implementations can benefit of powerful computational tools like parallel computing and automatic differentiation. Computational experiments were carried out with the aim to compare the proposed algorithm with well known Newton type algorithms. It turns out that the proposed algorithm is very efficient especially in the case of badly scaled and ill-conditioned problems.
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