Results 61 to 70 of about 256,417 (191)
Star Bicolouring of Bipartite Graphs
AlgorithmsWe give an integer linear program formulation for the star bicolouring of bipartite graphs. We develop a column generation method to solve the linear programming relaxation to obtain a lower bound for the minimum number of colours needed.
Daya Gaur +2 more
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A new approximate inverse preconditioner based on the Vaidya’s maximum spanning tree for matrix equation AXB = C [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2019 We propose a new preconditioned global conjugate gradient (PGL-CG) method for the solution of matrix equation AXB = C, where A and B are sparse Stieltjes matrices. The preconditioner is based on the support graph preconditioners.
K. Rezaei, F. Rahbarnia, F. Toutounian
doaj +1 more source
Sparse Recovery of Positive Signals with Minimal Expansion [PDF]
, 2009 We investigate the sparse recovery problem of reconstructing a high-dimensional non-negative sparse vector from lower dimensional linear measurements. While much work has focused on dense measurement matrices, sparse measurement schemes are crucial in ...
Dimakis, Alexandros G. +3 more
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Random incidence matrices: moments of the spectral density
, 2001 We study numerically and analytically the spectrum of incidence matrices of random labeled graphs on N vertices : any pair of vertices is connected by an edge with probability p.
Bauer, M., Golinelli, O.
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Algebraic Optimization of Binary Spatially Coupled Measurement Matrices for Interval Passing
, 2018 We consider binary spatially coupled (SC) low density measurement matrices for low complexity reconstruction of sparse signals via the interval passing algorithm (IPA).
Habib, Salman, Kliewer, Joerg
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Deterministic Compressed Sensing Matrices From Sequences With Optimal Correlation
IEEE Access, 2019 Compressed sensing (CS) is a new method of data acquisition which aims at recovering higher dimensional sparse vectors from considerably smaller linear measurements. One of the key problems in CS is the construction of sensing matrices. In this paper, we
Zhi Gu +4 more
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Dimensionality reduction with subgaussian matrices: a unified theory [PDF]
, 2014 We present a theory for Euclidean dimensionality reduction with subgaussian matrices which unifies several restricted isometry property and Johnson-Lindenstrauss type results obtained earlier for specific data sets.
Dirksen, Sjoerd
core
, 2017 This paper presents a low-overhead optimizer for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. Architectural diversity among different processors together with structural diversity among different sparse matrices lead to bottleneck ...
Elafrou, Athena +2 more
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Recovery of Low-Rank Plus Compressed Sparse Matrices with Application to Unveiling Traffic Anomalies
, 2012 Given the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, the goal of this paper is to establish deterministic conditions under which exact recovery of the low-rank and sparse components ...
Giannakis, Georgios B. +2 more
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Sparse kronecker pascal measurement matrices for compressive imaging
Journal of the European Optical Society-Rapid Publications, 2017 Background The construction of measurement matrix becomes a focus in compressed sensing (CS) theory. Although random matrices have been theoretically and practically shown to reconstruct signals, it is still necessary to study the more promising ...
Yilin Jiang +3 more
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