A numerically stable and efficient technique for the maintenance of positive definiteness in the Hessian for Newton-type methods |
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Authors: | John J. Spitzer |
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Affiliation: | 1. Associate Professor of Economics, State University of New York, Brockport N.Y. 14420 USA |
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Abstract: | Unconstrained optimization problems using Newton-type methods sometimes require that the Hessian matrix, G, calculated at each iteration, be modified to G1 in order to insure that the direction of search is downhill. It is shown that several previously proposed methods modify G in such a manner that G1 becomes extremely ill-conditioned even when G itself is well conditioned. The method proposed here is a modification of Greenstadt's, where bounds on the eigenvalues of G1 may be imposed such that G1 has a spectral condition number identical to G when G is well-conditioned but indefinite. The modification updates G by the addition of rank-one matrices, which are obtained by a partial eigenvalue decomposition of G, rather than a complete one as originally proposed by Greenstadt. The matrix G1 obtained in this manner is identical to the G1 obtained by Greenstadt's method, but may be computed in substantially less time. |
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