Results 31 to 40 of about 302,520 (264)
Computing the Rank Profile Matrix [PDF]
Proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation, 2015 The row (resp. column) rank profile of a matrix describes the staircase shape of its row (resp. column) echelon form. In an ISSAC'13 paper, we proposed a recursive Gaussian elimination that can compute simultaneously the row and column rank profiles of a matrix as well as those of all of its leading sub-matrices, in the same time as state of the art ...
Jean-Guillaume Dumas +2 more
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The Minimum Rank of a Correlation Matrix [PDF]
Proceedings of the National Academy of Sciences, 1944 Not ...
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Improved inference on the rank of a matrix [PDF]
Quantitative Economics, 2018 This paper develops a general framework for conducting inference on the rank of an unknown matrixΠ0. A defining feature of our setup is the null hypothesis of the formH0:rank(Π0)≤r. The problem is of first‐order importance because the previous literature focuses onH0′:rank(Π0)=rby implicitly assuming awayrank(Π0)<r, which may lead to invalid rank ...
Chen, Qihui, Fang, Zheng
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A Gradient System for Low Rank Matrix Completion
Axioms, 2018 In this article we present and discuss a two step methodology to find the closest low rank completion of a sparse large matrix. Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to
Carmela Scalone, Nicola Guglielmi
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Geometric rank of tensors and subrank of matrix multiplication
Discrete Analysis, 2023 Geometric rank of tensors and subrank of matrix multiplication, Discrete Analysis 2023:1, 25 pp. The rank of a matrix is a parameter of obvious importance, so it is natural to wonder what the right definition is for the rank of a higher-dimensional ...
Swastik Kopparty +2 more
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Survey on Probabilistic Models of Low-Rank Matrix Factorizations
Entropy, 2017 Low-rank matrix factorizations such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) are a large class of methods for pursuing the low-rank approximation of a given data matrix.
Jiarong Shi, Xiuyun Zheng, Wei Yang
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Low-rank matrix decompositions for ab initio nuclear structure
Physics Letters B, 2021 The extension of ab initio quantum many-body theory to higher accuracy and larger systems is intrinsically limited by the handling of large data objects in form of wave-function expansions and/or many-body operators.
A. Tichai +5 more
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Spreadsheets in Education, 2022 A 1981 mathematical article by Kenneth S. Miller has considered the inverse of the sum of matrices. The ingenuity of the Miller approach is that it has reached an expression without the entanglement of the inverse of the sum of some other matrices. The
Clarence C.Y. Kwan
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The Permanent Rank of a Matrix
Journal of Combinatorial Theory, Series A, 1999 The permanent rank of an \(n\)-square matrix \(A\) (perrank(\(A\))) is defined to be the order of the largest square submatrix of \(A\) with nonzero permanent. Let \(F\) be a field with \(\text{char}(F) = p \not= 2\). Among others, the author proves the following results: (i) For any matrix \(A\), \(\text{perrank}(A) \geq \frac{1}{2} \text{rank}(A)\). (
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ASSIST: Refinement of a Benefits Navigator Intervention Among Low‐Income Pediatric Oncology Families
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background/Objectives Children with cancer living in poverty experience worse survival and quality of life. Interventions connecting low‐income families to benefits (e.g., Supplemental Nutrition Assistance Program [SNAP] improve health outcomes; yet nearly 50% of SNAP‐eligible pediatric oncology families are unenrolled.
Puja J. Umaretiya +11 more
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