Los puntos clave no están disponibles para este artículo en este momento.
Abstract The steepest descent method has a rich history and is one of the simplest and best known methods for minimizing a function. While the method is not commonly used in practice due to its slow convergence rate, understanding the convergence properties of this method can lead to a better understanding of many of the more sophisticated optimization methods. Here, we give a short introduction and discuss some of the advantages and disadvantages of this method. Some recent results on modified versions of the steepest descent method are also discussed. WIREs Comp Stat 2010 2 719–722 DOI: 10.1002/wics.117 This article is categorized under: Algorithms and Computational Methods > Numerical Methods
Building similarity graph...
Analyzing shared references across papers
Loading...
Juan Meza (Fri,) studied this question.
synapsesocial.com/papers/6a0fe8212badbc352afeee42 — DOI: https://doi.org/10.1002/wics.117
Juan Meza
University of California, Merced
Wiley Interdisciplinary Reviews Computational Statistics
Lawrence Berkeley National Laboratory
Building similarity graph...
Analyzing shared references across papers
Loading...
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: