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Abstract: In this paper we present a new algorithm for finding the unconstrained minimum of a twice--continuously di#erentiable function f(x) in n variables. This algorithm is based on a conic model function, which does not involve the conjugacy matrix or the Hessian of the model function. The basic idea in this paper is to accelerate the convergence of the conic method choosing more appropriate points x 1 , x 2 , . . . , x n+1 to apply the conic model. To do this, we apply in the gradient of f a... (Update)
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BibTeX entry: (Update)
@misc{ manoussakis-dimensionreducing,
author = "G. E. Manoussakis and T. N. Grapsa and C. A. Botsaris",
title = "A Dimension-Reducing Conic Method for Unconstrained Optimization",
url = "citeseer.ist.psu.edu/manoussakis03dimensionreducing.html" }
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