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November 9, 20250 citationsOpen Access

Technical results on the convergence of quasi-Newton methods for nonsmooth optimization

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BGBennet Gebken

Key Points

  • Convergence behavior of quasi-Newton methods highlights the importance of eigenvalues in nonsmooth functions, guiding theoretical understanding.
  • Observational analysis drawn from numerical experiments shows how eigenvalues influence convergence in piecewise differentiable functions.
  • Theoretical exploration into the quasi-Newton matrix reveals critical aspects related to eigenvalues and optimization settings.
  • Findings point to the need for further exploration into eigenvalue behavior, indicating potential pathways for future research.

Abstract

It is well-known by now that the BFGS method is an effective method for minimizing nonsmooth functions. However, despite its popularity, theoretical convergence results are almost non-existent. One of the difficulties when analyzing the nonsmooth case is the fact that the secant equation forces certain eigenvalues of the quasi-Newton matrix to vanish, which is a behavior that has not yet been fully analyzed. In this article, we show what kind of behavior of the eigenvalues would be sufficient to be able to prove the convergence for piecewise differentiable functions. More precisely, we derive assumptions on the behavior from numerical experiments and then prove criticality of the limit under these assumptions. Furthermore, we show how quasi-Newton methods are able to explore the piecewise structure. While we do not prove that the observed behavior of the eigenvalues actually occurs, we believe that these results still give insight, and a certain intuition, for the convergence for nonsmooth functions.

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Cite This Study

Bennet Gebken (2025) studied this question.

synapsesocial.com/papers/690fdcdaf60c54d04ea3817bhttps://doi.org/10.48550/arxiv.2511.03296
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