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Register a new userThe Minc-type bound and the eigenvalue inclusion sets of the general product of tensors
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In this paper, we give the Minc-type bound for spectral radius of nonnegative tensors. We also present the bounds for the spectral radius and the eigenvalue inclusion sets of the general product of tensors.
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We consider the single eigenvalue fluctuations of random matrices of general Wigner-type, under a one-cut assumption on the density of states. For eigenvalues in the bulk, we prove that the asymptotic fluctuations of a single eigenvalue around its classical location are Gaussian with a universal variance which agrees with the GOE and GUE cases. Our method is based on a dynamical approach to mesoscopic linear spectral statistics which reduces their behavior on short scales to that on larger scales. We prove a central limit theorem for linear spectral statistics on larger scales via resolvent techniques and show that for certain classes of test functions, the leading order contribution to the variance is universal, agreeing with the GOE/GUE cases.
It is proved that the Fibonacci and the Frolov point sets, which are known to be very good for numerical integration, have optimal rate of decay of dispersion with respect to the cardinality of sets. This implies that the Fibonacci and the Frolov point sets provide universal discretization of the uniform norm for natural collections of subspaces of the multivariate trigonometric polynomials. It is shown how the optimal upper bounds for dispersion can be derived from the upper bounds for a new characteristic -- the smooth fixed volume discrepancy. It is proved that the Fibonacci point sets provide the universal discretization of all integral norms.
The M-matrix is an important concept in matrix theory, and has many applications. Recently, this concept has been extended to higher order tensors [18]. In this paper, we establish some important properties of M-tensors and nonsingular M-tensors. An M-tensor is a Z-tensor. We show that a Z-tensor is a nonsingular M-tensor if and only if it is semi-positive. Thus, a nonsingular M-tensor has all positive diagonal entries; and an M-tensor, regarding as the limitation of a series of nonsingular M-tensors, has all nonnegative diagonal entries. We introduce even-order monotone tensors and present their spectral properties. In matrix theory, a Z-matrix is a nonsingular M-matrix if and only if it is monotone. This is no longer true in the case of higher order tensors. We show that an even-order monotone Z-tensor is an even-order nonsingular M-tensor but not vice versa. An example of an even-order nontrivial monotone Z-tensor is also given.
A new emph{S}-type eigenvalue localization set for tensors is derived by breaking $N={1,2,cdots,n}$ into disjoint subsets $S$ and its complement. It is proved that this new set is tighter than those presented by Qi (Journal of Symbolic Computation 40 (2005) 1302-1324), Li et al. (Numer. Linear Algebra Appl. 21 (2014) 39-50) and Li et al. (Linear Algebra Appl. 493 (2016) 469-483). As applications, checkable sufficient conditions for the positive definiteness and the positive semi-definiteness of tensors are proposed. Moreover, based on this new set, we establish a new upper bound for the spectral radius of nonnegative tensors and a lower bound for the minimum emph{H}-eigenvalue of weakly irreducible strong emph{M}-tensors in this paper. We demonstrate that these bounds are sharper than those obtained by Li et al. (Numer. Linear Algebra Appl. 21 (2014) 39-50) and He and Huang (J. Inequal. Appl. 114 (2014) 2014). Numerical examples are also given to illustrate this fact.
In this work, we describe a Bayesian framework for the X-ray computed tomography (CT) problem in an infinite-dimensional setting. We consider reconstructing piecewise smooth fields with discontinuities where the interface between regions is not known. Furthermore, we quantify the uncertainty in the prediction. Directly detecting the discontinuities, instead of reconstructing the entire image, drastically reduces the dimension of the problem. Therefore, the posterior distribution can be approximated with a relatively small number of samples. We show that our method provides an excellent platform for challenging X-ray CT scenarios (e.g. in case of noisy data, limited angle, or sparse angle imaging). We investigate the accuracy and the efficiency of our method on synthetic data. Furthermore, we apply the method to the real-world data, tomographic X-ray data of a lotus root filled with attenuating objects. The numerical results indicate that our method provides an accurate method in detecting boundaries between piecewise smooth regions and quantifies the uncertainty in the prediction, in the context of X-ray CT.
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