Two trust-region methods for systems of mixed nonlinear equalities, general inequalities and simple bounds are proposed. The first method is based on a Gauss–Newton model, the second one is based on a regularized Gauss–Newton model and results to be a Levenberg–Marquardt method. The globalization strategy uses affine scaling matrices arising in bound-constrained optimization. Global convergence results are established and quadratic rate is achieved under an error bound assumption. The numerical efficiency of the new methods is experimentally studied.
Trust-region quadratic methods for nonlinear systems of mixed equalities and inequalities
PORCELLI, MARGHERITA
2009
Abstract
Two trust-region methods for systems of mixed nonlinear equalities, general inequalities and simple bounds are proposed. The first method is based on a Gauss–Newton model, the second one is based on a regularized Gauss–Newton model and results to be a Levenberg–Marquardt method. The globalization strategy uses affine scaling matrices arising in bound-constrained optimization. Global convergence results are established and quadratic rate is achieved under an error bound assumption. The numerical efficiency of the new methods is experimentally studied.File in questo prodotto:
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