Self-scaling variable metric algorithms without line search for unconstrained minimization
نویسندگان
چکیده
منابع مشابه
Self-Scaling Variable Metric Algorithms Without Line Search for Unconstrained Minimization*
This paper introduces a new class of quasi-Newton algorithms for unconstrained minimization in which no line search is necessary and the inverse Hessian approximations are positive definite. These algorithms are based on a two-parameter family of rank two, updating formulae used earlier with line search in self-scaling variable metric algorithms. It is proved that, in a quadratic case, the new ...
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This paper introduces a new class of quasi-Newton algorithms for unconstrained minimization in which no line search is necessary and the inverse Hessian approximations are positive definite. These algorithms are based on a two-parameter family of rank two, updating formulae used earlier with line search in self-scaling variable metric algorithms. It is proved that, in a quadratic case, the new ...
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Recent attempts to assess the performance of SSVM algorithms for unconstrained minimization problems differ in their evaluations from earlier assessments. Nevertheless, the new experiments confirm earlier observations that, on certain types of problems, the SSVM algorithms are far superior to other variable metric methods. This paper presents a critical review of these recent assessments and di...
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ژورنال
عنوان ژورنال: Mathematics of Computation
سال: 1973
ISSN: 0025-5718
DOI: 10.1090/s0025-5718-1973-0329259-8