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NeurIPS2023顶会

Block Broyden's Methods for Solving Nonlinear Equations

Chengchang Liu, Cheng Chen, Luo Luo, John C. S. Lui

2023年份
5被引次数
3顶会引用

摘要

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden’s methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden’s method has a faster condition-number-free convergence rate than existing Broyden’s methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden’s method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden’s method outperforms bad Broyden’s method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis.

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