基于向量k-范数和DC规划的稀疏优化及其在支持向量机特征选择中的应用

IF 1.6 2区数学 Q2 MATHEMATICS, APPLIED

Computational Optimization and Applications Pub Date : 2023-07-12 DOI:10.1007/s10589-023-00506-y

M. Gaudioso, G. Giallombardo, G. Miglionico

引用次数: 1

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines

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Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines

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来源期刊

Computational Optimization and Applications 数学-应用数学

CiteScore

3.70

自引率

9.10%

发文量

审稿时长

10 months

期刊介绍： Computational Optimization and Applications is a peer reviewed journal that is committed to timely publication of research and tutorial papers on the analysis and development of computational algorithms and modeling technology for optimization. Algorithms either for general classes of optimization problems or for more specific applied problems are of interest. Stochastic algorithms as well as deterministic algorithms will be considered. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. Topics of interest include, but are not limited to the following: Large Scale Optimization, Unconstrained Optimization, Linear Programming, Quadratic Programming Complementarity Problems, and Variational Inequalities, Constrained Optimization, Nondifferentiable Optimization, Integer Programming, Combinatorial Optimization, Stochastic Optimization, Multiobjective Optimization, Network Optimization, Complexity Theory, Approximations and Error Analysis, Parametric Programming and Sensitivity Analysis, Parallel Computing, Distributed Computing, and Vector Processing, Software, Benchmarks, Numerical Experimentation and Comparisons, Modelling Languages and Systems for Optimization, Automatic Differentiation, Applications in Engineering, Finance, Optimal Control, Optimal Design, Operations Research, Transportation, Economics, Communications, Manufacturing, and Management Science.