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Incremental Gauss--Newton Methods with Superlinear Convergence Rates

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arxiv 2407.03195 v1 pith:QNTHIFH3 submitted 2024-07-03 math.OC cs.LG

classification math.OCcs.LG
keywords convergencemethodmethodsratesuperlineargauss--newtonincrementalnonlinear
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This paper addresses the challenge of solving large-scale nonlinear equations with H\"older continuous Jacobians. We introduce a novel Incremental Gauss--Newton (IGN) method within explicit superlinear convergence rate, which outperforms existing methods that only achieve linear convergence rate. In particular, we formulate our problem by the nonlinear least squares with finite-sum structure, and our method incrementally iterates with the information of one component in each round. We also provide a mini-batch extension to our IGN method that obtains an even faster superlinear convergence rate. Furthermore, we conduct numerical experiments to show the advantages of the proposed methods.

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  1. An Enhanced Levenberg--Marquardt Method via Gram Reduction

    math.OC 2024-12 conditional novelty 6.0 of 10

    Reusing the Gram matrix for m iterations in a Levenberg-Marquardt method yields global convergence with O(d^3/epsilon + d^2/epsilon^2) total cost and local superlinear rate.

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