Results 81 to 90 of about 71,699 (272)

Completely positive and completely positive semidefinite tensor relaxations for polynomial optimization

open access: yes, 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

Binary Positive Semidefinite Matrices and Associated Integer Polytopes [PDF]

open access: yesMathematical Programming, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Letchford, Adam N.   +1 more
openaire   +3 more sources

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

open access: yesInternational 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   +3 more
wiley   +1 more source

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

open access: yesCybernetics 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

A Comparative Study of MINLP and MPVC Formulations for Solving Complex Nonlinear Decision‐Making Problems in Aerospace Applications

open access: yesOptimal Control Applications and Methods, EarlyView.
ABSTRACT High‐level decision‐making for dynamical systems often involves performance and safety specifications that are activated or deactivated depending on conditions related to the system state and commands. Such decision‐making problems can be naturally formulated as optimization problems where these conditional activations are regulated by ...
Andrea Ghezzi   +4 more
wiley   +1 more source

Preserving positivity for rank-constrained matrices

open access: yes, 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   +2 more
core   +1 more source

Functions Operating on Positive Semidefinite Quaternionic Matrices

open access: yesMonatshefte f�r Mathematik, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Simulating Quantum State Transfer Between Distributed Devices Using Noisy Interconnects

open access: yesAdvanced 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   +3 more
wiley   +1 more source

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

open access: yes, 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   +2 more
core  

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

open access: yes, 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   +2 more
core  

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