Results 71 to 80 of about 71,800 (235)

Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images

Cybernetics and Information Technologies, 2018
This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local ...
Akl Adib
doaj   +1 more source

Preserving positivity for rank-constrained matrices

, 2017
Entrywise functions preserving the cone of positive semidefinite matrices have been studied by many authors, most notably by Schoenberg [Duke Math. J. 9, 1942] and Rudin [Duke Math. J. 26, 1959].
Guillot, Dominique, Khare, Apoorva, Rajaratnam, Bala   +2 more
core   +1 more source

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao, Qintao Guo, Jin Zhou, Cheng Yi   +3 more
wiley   +1 more source

Completely positive and completely positive semidefinite tensor relaxations for polynomial optimization

, 2016
Completely positive (CP) tensors, which correspond to a generalization of CP matrices, allow to reformulate or approximate a general polynomial optimization problem (POP) with a conic optimization problem over the cone of CP tensors.
Kuang, Xiaolong, Zuluaga, Luis F.
core   +1 more source

Simulating Quantum State Transfer Between Distributed Devices Using Noisy Interconnects

Advanced Quantum Technologies, EarlyView.
Noisy connections challenge future networked quantum computers. This work presents a practical method to address this by simulating an ideal state transfer over noisy interconnects. The approach reduces the high sampling cost of previous methods, an advantage that improves as interconnect quality gets better.
Marvin Bechtold, Johanna Barzen, Frank Leymann, Alexander Mandl   +3 more
wiley   +1 more source

Regression on fixed-rank positive semidefinite matrices: a Riemannian approach [PDF]

, 2011
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems.
Bonnabel, Silvere, Meyer, Gilles, Sepulchre, Rodolphe   +2 more
core  

Data‐Based Refinement of Parametric Uncertainty Descriptions

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT We consider dynamical systems with a linear fractional representation involving parametric uncertainties which are either constant or varying with time. Given a finite‐horizon input‐state or input‐output trajectory of such a system, we propose a numerical scheme which iteratively improves the available knowledge about the involved constant ...
Tobias Holicki, Carsten W. Scherer
wiley   +1 more source

A generalization and an application of the arithmetic–geometric mean inequality for the Frobenius norm

Journal of Inequalities and Applications, 2018
Recently, Kittaneh and Manasrah (J. Math. Anal. Appl. 361:262–269, 2010) showed a refinement of the arithmetic–geometric mean inequality for the Frobenius norm. In this paper, we shall present a generalization of Kittaneh and Manasrah’s result. Meanwhile,
Xuesha Wu
doaj   +1 more source

Separability of symmetric states and vandermonde decomposition

New Journal of Physics, 2020
Symmetry is one of the central mysteries of quantum mechanics and plays an essential role in multipartite entanglement. In this paper, we consider the separability problem of quantum states in the symmetric space.
Lilong Qian, Lin Chen, Delin Chu
doaj   +1 more source

Block Factor-width-two Matrices and Their Applications to Semidefinite and Sum-of-squares Optimization

, 2020
Semidefinite and sum-of-squares (SOS) optimization are fundamental computational tools in many areas, including linear and nonlinear systems theory. However, the scale of problems that can be addressed reliably and efficiently is still limited.
Papachristodoulou, Antonis, Sootla, Aivar, Zheng, Yang   +2 more
core