arXiv:2404.08073v3 Announce Type: replace-cross Abstract: Bregman proximal-type algorithms (BPs), such as mirror descent, have become popular tools in machine learning and data science for exploiting problem structures through non-Euclidean geometries. In this paper, we show that BPs can get trapped near a class of non-stationary points, which we term \emph{spurious stationary points}. Such stagnation can persist for any finite number of iterations if the gradient of the Bregman kernel is not Lipschitz continuous, even in convex problems. The root cause lies in a fundamental contrast in descen
This paper addresses a fundamental algorithmic challenge encountered in machine learning optimization, published as part of ongoing academic research in AI.
While highly technical, this work could contribute to incremental improvements in AI algorithm stability and performance, particularly in non-Euclidean optimization problems.
This research identifies a specific class of limitations in certain optimization algorithms (Bregman Proximal-type algorithms), potentially leading to more robust algorithm design in the future.
- · AI researchers
- · Optimization algorithm developers
Improved understanding of the theoretical limitations of certain AI optimization algorithms.
Development of more stable and performant AI and machine learning models in specific problem domains.
Potentially faster and more reliable training of complex AI systems due to refined optimization techniques.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG