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Bias-Reduced Localization for Drone Swarm Based on Sensor Selection. [PDF]

Sensors (Basel)
Wu B, Shen B, Zhang Y, Yang L, Wang Z.
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Cholesky decomposition of a positive semidefinite matrix with known kernel

Applied Mathematics and Computation, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zdenek Dostál, Tomáš Kozubek, Alexandros Markopoulos   +2 more
exaly   +2 more sources

On the uniqueness of positive semidefinite matrix solution under compressed observations

2010 IEEE International Symposium on Information Theory, 2010
In this paper, we investigate the uniqueness of positive semidefinite matrix solution to compressed linear observations. We show that under a necessary and sufficient condition for the linear compressed observations operator, there will be a unique positive semidefinite matrix solution to the compressed linear observations. It is further shown, through
Ao Tang
exaly   +2 more sources

Approximation by a Hermitian Positive Semidefinite Toeplitz Matrix

SIAM Journal on Matrix Analysis and Applications, 1993
The authors study the problem of finding the closest Hermitian positive semidefinite Toeplitz matrix of a given rank to an arbitrary given matrix (in the Frobenius norm = Hilbert-Schmidt norm). They introduce two methods, one is based on using a special orthonormal basis in the space of Hermitian Toeplitz matrices and the second is a modified ...
T. J. Suffridge, Tom L. Hayden
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On the Positive Semidefinite Nature of a Certain Matrix Expression

Canadian Journal of Mathematics, 1964
By "positive definite matrices" or, briefly, definite matrices, we mean in this note self-adjoint matrices all the characteristic values of which are positive. Alternatively, they can be defined as matrices, all the hermitian quadratic forms of which are real and positive.
Wigner, Eugene P., Yanase, M. M.
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The Probability that a (partial) matrix is positive semidefinite

1998
Assuming that a ij is distributed uniformly in [—1,1] and a ii = 1, we compute the probability that a symmetric matrix A = [a ij ] 171-1 j=1 is positive semidefinite. The probability is also computed if A is a Toeplitz matrix. Finally, some results for partial matrices are presented.
C. R. Johnson, G. Nævdal
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A note on Hermitian positive semidefinite matrix polynomials

Linear Algebra and its Applications, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
S. Friedland, A. Melman
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The unique square root of a positive semidefinite matrix

International Journal of Mathematical Education in Science and Technology, 2006
An easy way to present the uniqueness of the square root of a positive semidefinite matrix is given.
Koeber, Martin, Schäfer, Uwe
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