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<p>Gradient computation of multivariate distribution functions calls for considerable effort. Hence coordinate descent and derivative-free approaches are attractive. This paper deals with constrained convex problems. We perform random descent steps in an approximation scheme that is an inexact cutting-plane method from a dual viewpoint. We prove that the scheme converges and present a computational study comparing different descent methods applied in the approximation scheme.</p>

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Random Descent Steps in a Probability Maximization Scheme

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月18日

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  • Edit Csizmás,
  • Rajmund Drenyovszki,
  • Tamás Szántai,
  • Csaba I. Fábián

摘要

Gradient computation of multivariate distribution functions calls for considerable effort. Hence coordinate descent and derivative-free approaches are attractive. This paper deals with constrained convex problems. We perform random descent steps in an approximation scheme that is an inexact cutting-plane method from a dual viewpoint. We prove that the scheme converges and present a computational study comparing different descent methods applied in the approximation scheme.