Results 1 to 10 of about 2,198,085 (171)
Numerical range of a doubly stochastic matrix [PDF]
Linear Algebra and its Applications, 1991 The numerical range of an \(n\times n\) complex matrix A is the set \(W(A)=\{x^*Ax:\) \(x\in {\mathbb{C}}^ n\), \(x^*x=1\}\). It is shown that for A doubly stochastic and \(n=3\), W(A) is the convex hull of the point 1 and a certain ellipse or is a line segment or a triangle.
Nylen, Peter, Tam, Tin-Yau
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Maximal doubly stochastic matrix centralizers [PDF]
Linear Algebra and its Applications, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cruz, Henrique F. da +3 more
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Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering [PDF]
European Journal of Operational Research, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Julien Ah-Pine
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A Semismooth Newton-Type Method for the Nearest Doubly Stochastic Matrix Problem
Mathematics of Operations ResearchWe study a semismooth Newton-type method for the nearest doubly stochastic matrix problem where the nonsingularity of the Jacobian can fail. The optimality conditions for this problem are formulated as a system of strongly semismooth functions. We show that the nonsingularity of the Jacobian does not hold for this system.
Xinxin Li, Henry Wolkowicz, Hao Hu
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Structured Doubly Stochastic Matrix for Graph Based Clustering [PDF]
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016 As one of the most significant machine learning topics, clustering has been extensively employed in various kinds of area. Its prevalent application in scientific research as well as industrial practice has drawn high attention in this day and age. A multitude of clustering methods have been developed, among which the graph based clustering method ...
Xiaoqian Wang 0001 +2 more
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Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms
Proceedings of the Genetic and Evolutionary Computation Conference, 2023 Problems with solutions represented by permutations are very prominent in combinatorial optimization. Thus, in recent decades, a number of evolutionary algorithms have been proposed to solve them, and among them, those based on probability models have received much attention.
Valentino Santucci, Josu Ceberio
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Clustering by Low-Rank Doubly Stochastic Matrix Decomposition
CoRR, 2012 Clustering analysis by nonnegative low-rank approximations has achieved remarkable progress in the past decade. However, most approximation approaches in this direction are still restricted to matrix factorization. We propose a new low-rank learning method to improve the clustering performance, which is beyond matrix factorization. The approximation is
Zhirong Yang, Erkki Oja
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Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decomposition [PDF]
, 2018 Word embedding, which encodes words into vectors, is an important starting point in natural language processing and commonly used in many text-based machine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning.
Denis Sedov, Zhirong Yang
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Reduction of a matrix with positive elements to a doubly stochastic matrix [PDF]
Proceedings of the American Mathematical Society, 1967 expressed in the form T = D1A D2, where D1 and D2 are diagonal matrices with strictly positive diagonal elements. The matrices D1 and D2 are themselves unique up to a scalar factor. The existence of T, D1 and D2 is established by a "constructive" but "limiting" procedure in [2].
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Matrix scaling and explicit doubly stochastic limits [PDF]
Linear Algebra and its Applications, 2019 18 pages. This article is a shortened version of arXiv:1902.04544 and has been accepted to appear in The Journal of Linear Algebra and its ...
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