用 ℓ0 惩罚最小二乘法预测配对相互作用亲和力--基于非平滑双目标优化的方法*

IF 1.4 3区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Optimization Methods & Software Pub Date : 2024-01-24 DOI:10.1080/10556788.2023.2280784

Pauliina Paasivirta, Riikka Numminen, Antti Airola, Napsu Karmitsa, Tapio Pahikkala

引用次数: 0

摘要

本文介绍了一种新颖的基于非平滑优化的方法 LMBM-Kronℓ0LS，用于解决大规模配对相互作用亲和力预测问题。LMBM-Kronℓ0LS的目的是产生...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Predicting pairwise interaction affinities with ℓ0-penalized least squares–a nonsmooth bi-objective optimization based approach*

In this paper, we introduce a novel nonsmooth optimization-based method LMBM-Kronℓ0LS for solving large-scale pairwise interaction affinity prediction problems. The aim of LMBM-Kronℓ0LS is to produ...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.