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arXiv · 2401.06809

Greedy Newton: Newton's Method with Exact Line Search

Betty Shea·Mark Schmidt
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Abstract

{A defining characteristic of Newton's method is local superlinear convergence within a neighbourhood of a strict local minimum. However, outside this neighborhood Newton's method can converge slowly or even diverge. A common approach to dealing with non-convergence is using a step size that is set by an Armijo backtracking line search. With suitable initialization the line-search preserves local superlinear convergence, but may give sub-optimal progress when not near a solution. In this work we consider Newton's method under an exact line search, which we call ``greedy Newton'' (GN). We show that this leads to an improved global convergence rate, while retaining a local superlinear convergence rate. We empirically show that GN may work better than backtracking Newton by allowing significantly larger step sizes.

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Betty Shea, Mark Schmidt. 2024-01-10. Greedy Newton: Newton's Method with Exact Line Search. https://doi.org/10.1007/s11590-025-02220-6

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