Low-rank matrix approximations over canonical subspaces
Journal of Numerical Analysis and Approximation Theory, 2020 In this paper we derive closed form expressions for the nearest rank-\(k\) matrix on canonical subspaces. We start by studying three kinds of subspaces. Let \(X\) and \(Y\) be a pair of given matrices. The first subspace contains all the \(m\times
Achiya Dax
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New properties for certain positive semidefinite matrices [PDF]
, 2016 Minghua Lin
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On Some Matrix Trace Inequalities
Journal of Inequalities and Applications, 2010 We first present an inequality for the Frobenius norm of the Hadamard product of two any square matrices and positive semidefinite matrices. Then, we obtain a trace inequality for products of two positive semidefinite block matrices by using 2×2 ...
Ramazan Türkmen +1 more
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A trace bound for integer-diagonal positive semidefinite matrices
Special Matrices, 2020 We prove that an n-by-n complex positive semidefinite matrix of rank r whose graph is connected, whose diagonal entries are integers, and whose non-zero off-diagonal entries have modulus at least one, has trace at least n + r − 1.
Mitchell Lon
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Separability for mixed states with operator Schmidt rank two [PDF]
Quantum, 2019 The operator Schmidt rank is the minimum number of terms required to express a state as a sum of elementary tensor factors. Here we provide a new proof of the fact that any bipartite mixed state with operator Schmidt rank two is separable, and can be ...
Gemma De las Cuevas +2 more
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On the closure of the completely positive semidefinite cone and linear approximations to quantum colorings [PDF]
, 2015 We investigate structural properties of the completely positive semidefinite cone $\mathcal{CS}_+^n$, consisting of all the $n \times n$ symmetric matrices that admit a Gram representation by positive semidefinite matrices of any size. This cone has been
Burgdorf, Sabine +2 more
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Decomposition of arrow type positive semidefinite matrices with application to topology optimization [PDF]
Mathematical programming, 2019 Decomposition of large matrix inequalities for matrices with chordal sparsity graph has been recently used by Kojima et al. (Math Program 129(1):33–68, 2011) to reduce problem size of large scale semidefinite optimization (SDO) problems and thus increase
M. Kočvara
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Conic approach to quantum graph parameters using linear optimization over the completely positive semidefinite cone [PDF]
, 2015 We investigate the completely positive semidefinite cone $\mathcal{CS}_+^n$, a new matrix cone consisting of all $n\times n$ matrices that admit a Gram representation by positive semidefinite matrices (of any size).
Laurent, Monique, Piovesan, Teresa
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Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices [PDF]
IEEE Annual Symposium on Foundations of Computer Science, 2017 We show how to compute a relative-error low-rank approximation to any positive semidefinite (PSD) matrix in sublinear time, i.e., for any n x n PSD matrix A, in Õ(n ⋅ poly(k/ε)) time we output a rank-k matrix B, in factored form, for ...
Cameron Musco, David P. Woodruff
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Fischer Type Log-Majorization of Singular Values on Partitioned Positive Semidefinite Matrices
Journal of Function Spaces, 2021 In this paper, we establish a Fischer type log-majorization of singular values on partitioned positive semidefinite matrices, which generalizes the classical Fischer's inequality. Meanwhile, some related and new inequalities are also obtained.
Benju Wang, Yun Zhang
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