Results 81 to 90 of about 1,753,142 (356)

Minimal residual Hermitian and skew-Hermitian splitting iteration method for the continuous Sylvester equation [PDF]

arXiv, 2020
By applying the minimal residual technique to the Hermitian and skew-Hermitian (HSS) iteration scheme, we introduce a non-stationary iteration method named minimal residual Hermitian and skew-Hermitian (MRHSS) iteration method to solve the continuous Sylvester equation.
arxiv  

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Data‐driven performance metrics for neural network learning

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +1 more source

An iterative method with error estimators

Journal of Computational and Applied Mathematics, 2001
AbstractIterative methods for the solution of linear systems of equations produce a sequence of approximate solutions. In many applications it is desirable to be able to compute estimates of the norm of the error in the approximate solutions generated and terminate the iterations when the estimates are sufficiently small.
Calvetti D., Morigi S., Reichel L., Sgallari F.   +3 more
openaire   +3 more sources

Targeted protein degradation in oncology: novel therapeutic opportunity for solid tumours?

Molecular Oncology, EarlyView.
Current anticancer therapies are limited by the occurrence of resistance and undruggability of most proteins. Targeted protein degraders are novel, promising agents that trigger the selective degradation of previously undruggable proteins through the recruitment of the ubiquitin–proteasome machinery. Their mechanism of action raises exciting challenges,
Noé Herbel, Sophie Postel‐Vinay
wiley   +1 more source

Weak convergence theorem for a class of split variational inequality problems and applications in a Hilbert space

Journal of Inequalities and Applications, 2017
In this paper, we consider the algorithm proposed in recent years by Censor, Gibali and Reich, which solves split variational inequality problem, and Korpelevich’s extragradient method, which solves variational inequality problems. As our main result, we
Ming Tian, Bing-Nan Jiang
doaj   +1 more source

q-Iterative Methods [PDF]

IOSR Journal of Mathematics, 2013
This paper is devoted to derivation of q-analogues of Iterative Methods for solution of algebraic and transcendental equations and comparing accuracy of results with classical methods.
Prashant Singh, Pramod Kumar Mishra
openaire   +1 more source

Bold Diagrammatic Monte Carlo in the Lens of Stochastic Iterative Methods [PDF]

, 2017
This work aims at understanding of bold diagrammatic Monte Carlo (BDMC) methods for stochastic summation of Feynman diagrams from the angle of stochastic iterative methods. The convergence enhancement trick of the BDMC is investigated from the analysis of condition number and convergence of the stochastic iterative methods.
arxiv   +1 more source

Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data

Molecular Oncology, EarlyView.
This study develops a semi‐supervised classifier integrating multi‐genomic data (1404 training/5893 validation samples) to improve homologous recombination deficiency (HRD) detection in breast cancer. Our method demonstrates prognostic value and predicts chemotherapy/PARP inhibitor sensitivity in HRD+ tumours.
Rong Zhu, Katherine Eason, Suet‐Feung Chin, Paul A. W. Edwards, Raquel Manzano Garcia, Richard Moulange, Jia Wern Pan, Soo Hwang Teo, Sach Mukherjee, Maurizio Callari, Carlos Caldas, Stephen‐John Sammut, Oscar M. Rueda   +12 more
wiley   +1 more source

An Optimal Order Method for Multiple Roots in Case of Unknown Multiplicity

Algorithms, 2016
In the literature, recently, some three-step schemes involving four function evaluations for the solution of multiple roots of nonlinear equations, whose multiplicity is not known in advance, are considered, but they do not agree with Kung–Traub’s ...
Jai Prakash Jaiswal
doaj   +1 more source