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- W4387560131 abstract "We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and inexact Newton methods for nonlinear systems of equations. Random models are formed using suitable sampling strategies for the matrices involved in the deterministic models. The analysis of the expected number of iterations needed in the worst case to achieve a desired level of accuracy in the first-order optimality condition provides guidelines for applying sampling and enforcing, with fixed probability, a suitable accuracy in the random approximations. Results of the numerical validation of the algorithms are presented." @default.
- W4387560131 created "2023-10-12" @default.
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- W4387560131 date "2023-10-09" @default.
- W4387560131 modified "2023-10-18" @default.
- W4387560131 title "Inexact Newton methods with matrix approximation by sampling for nonlinear least-squares and systems" @default.
- W4387560131 doi "https://doi.org/10.48550/arxiv.2310.05501" @default.
- W4387560131 hasPublicationYear "2023" @default.
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