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• W2018624971 abstract "We consider Newton-like line search descent methods for solving non-linear least-squares problems. The basis of our approach is to choose a method, or parameters within a method, by minimizing a variational measure which estimates the error in an inverse Hessian approximation. In one approach we consider sizing methods and choose sizing parameters in an optimal way. In another approach we consider various possibilities for hybrid Gauss-Newton/BFGS methods. We conclude that a simple Gauss-Newton/BFGS hybrid is both efficient and robust and we illustrate this by a range of comparative tests with other methods. These experiments include not only many well known test problems but also some new classes of large residual problem." @default.
• W2018624971 created "2016-06-24" @default.
• W2018624971 creator A5018398367 @default.
• W2018624971 creator A5088683774 @default.
• W2018624971 date "1985-05-01" @default.
• W2018624971 modified "2023-10-17" @default.
• W2018624971 title "Variational Methods for Non-Linear Least-Squares" @default.
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• W2018624971 doi "https://doi.org/10.1057/jors.1985.68" @default.
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