利用贝叶斯推理逼近赫塞斯矩阵：随机优化中准牛顿方法的新方法

IF 1.4 3区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Optimization Methods & Software Pub Date : 2024-04-29 DOI:10.1080/10556788.2024.2339226

André Gustavo Carlon, Luis Espath, Raúl Tempone

引用次数: 0

摘要

在随机优化中使用准牛顿方法并非易事，因为很难从噪声梯度中提取曲率信息。此外，对噪声梯度进行预处理也是一项艰巨的任务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization

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来源期刊

Optimization Methods & Software 工程技术-计算机：软件工程

CiteScore

4.50

自引率

0.00%

发文量

审稿时长

7 months

期刊介绍： Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Topics include: Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.