Numerical methods for solving a class of matrix equations arising from inference for ranked set sampling on imperfect ranking

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED Journal of Computational and Applied Mathematics Pub Date : 2025-08-01 Epub Date: 2025-01-14 DOI:10.1016/j.cam.2025.116519
Qiang Niu , Binrui Shen , Yenan Wang
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Abstract

In this paper, we investigate some numerical methods for a class of matrix equations with constraints arising from inference for ranked set sampling on imperfect ranking. Based on the structure of the matrix equation, two classes of numerical methods are studied to solve the problem. The first idea is to treat the problem as a simplified Riccati equation, then a Schur method and a square-root method are derived. The second idea is entirely novel, which is based on an extended Krylov subspace originated from the doubly stochastic property of the related matrices. The performance and efficiency of all the numerical solvers are verified by numerical examples.
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求解一类不完全排序排序集抽样推理引起的矩阵方程的数值方法
本文研究了一类矩阵方程的数值解法,该类方程由于不完全排序上的排序集抽样推理而产生约束。基于矩阵方程的结构,研究了求解该问题的两类数值方法。首先将该问题视为简化的Riccati方程,然后推导出Schur法和平方根法。第二个想法是完全新颖的,它是基于扩展的Krylov子空间,起源于相关矩阵的双重随机性质。通过算例验证了所有数值求解方法的性能和效率。
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来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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