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Diagonal approximation of the Hessian by finite differences for unconstrained optimization. (English) Zbl 1441.49029
Summary: A new quasi-Newton method with a diagonal updating matrix is suggested, where the diagonal elements are determined by forward or by central finite differences. The search direction is a direction of sufficient descent. The algorithm is equipped with an acceleration scheme. The convergence of the algorithm is linear. The preliminary computational experiments use a set of 75 unconstrained optimization test problems classified in five groups according to the structure of their Hessian: diagonal, block-diagonal, band (tri- or penta-diagonal), sparse and dense. Subject to the CPU time metric, intensive numerical experiments show that, for problems with Hessian in a diagonal, block-diagonal or band structure, the algorithm with diagonal approximation of the Hessian by finite differences is top performer versus the well-established algorithms: the steepest descent and the Broyden-Fletcher-Goldfarb-Shanno. On the other hand, as a by-product of this numerical study, we show that the Broyden-Fletcher-Goldfarb-Shanno algorithm is faster for problems with sparse Hessian, followed by problems with dense Hessian.
49M15 Newton-type methods
65K05 Numerical mathematical programming methods
90C30 Nonlinear programming
Full Text: DOI
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