Metropolis Monte Carlo deconvolution technique compared to iterative methods for noisy data

2001 ◽  
Author(s):  
Abolfazl M. Amini ◽  
George E. Ioup ◽  
Juliette W. Ioup
Keyword(s):  
Monte Carlo ◽  
Iterative Methods ◽  
Noisy Data ◽  
Deconvolution Technique ◽  
Metropolis Monte Carlo

scholarly journals Analysis of Monte Carlo accelerated iterative methods for sparse linear systems

2017 ◽  
Vol 24 (3) ◽  
pp. e2088 ◽  
Author(s):  
Michele Benzi ◽  
Thomas M. Evans ◽  
Steven P. Hamilton ◽  
Massimiliano Lupo Pasini ◽  
Stuart R. Slattery
Keyword(s):  
Monte Carlo ◽  
Linear Systems ◽  
Iterative Methods ◽  
Sparse Linear Systems

scholarly journals High order iterative methods for matrix inversion and regularized solution of fredholm integral equation of first kind with noisy data

2018 ◽  
Vol 22 ◽  
pp. 01002
Author(s):  
Suzan Cival Buranay ◽  
Ovgu Cidar Iyikal
Keyword(s):  
Integral Equation ◽  
Iterative Methods ◽  
Fredholm Integral Equation ◽  
Recurrence Formula ◽  
Noisy Data ◽  
Matrix Inversion ◽  
High Order ◽  
The Given ◽  
High Order Iterative Methods ◽  
Regularized Solution

The motivation of the present work is to propose high order iterative methods with a recurrence formula for approximate matrix inversion and provide regularized solution of Fredholm integral equation of first kind with noisy data by an algorithm using the proposed methods. From the given family of methods of orders p = 7,11,15,19 are applied to solve problems of Fredholm integral equation of first kind. From the literature, iterative methods of same orders are used to solve the considered problems and numerical comparisons are shown through tables and figures.

scholarly journals Growth and arrest of topological cycles in small physical networks

2020 ◽  
Vol 117 (27) ◽  
pp. 15394-15396
Author(s):  
Timothy W. Sirk
Keyword(s):  
Monte Carlo ◽  
Random Graphs ◽  
Kinetic Monte Carlo ◽  
Polymer Networks ◽  
Finite Size ◽  
Characteristic Size ◽  
Network Size ◽  
Embedded Networks ◽  
Metropolis Monte Carlo ◽  
Chordless Cycle

The chordless cycle sizes of spatially embedded networks are demonstrated to follow an exponential growth law similar to random graphs if the number of nodesNxis below a critical valueN*. For covalent polymer networks, increasing the network size, as measured by the number of cross-link nodes, beyondN*results in a crossover to a new regime in which the characteristic size of the chordless cyclesh*no longer increases. From this result, the onset and intensity of finite-size effects can be predicted from measurement ofh*in large networks. Although such information is largely inaccessible with experiments, the agreement of simulation results from molecular dynamics, Metropolis Monte Carlo, and kinetic Monte Carlo suggests the crossover is a fundamental physical feature which is insensitive to the details of the network generation. These results show random graphs as a promising model to capture structural differences in confined physical networks.

Magnetic properties of a bi-fullerene-like structure, X60-Y60, with RKKY interactions in the Blume–Capel model

2018 ◽  
Vol 32 (04) ◽  
pp. 1850031 ◽  
Author(s):  
T. Sahdane ◽  
A. Mhirech ◽  
L. Bahmad ◽  
B. Kabouchi
Keyword(s):  
Monte Carlo ◽  
Magnetic Properties ◽  
Physical Parameters ◽  
Nano Particle ◽  
Interaction Type ◽  
The Core ◽  
Spherical Surfaces ◽  
Metropolis Monte Carlo

In this paper, we study the influence of the physical parameters on the thermal and magnetic properties of a nano-particle of bi-fullerene-like structure X[Formula: see text]-Y[Formula: see text] separated by nonmagnetic spherical surfaces, using the Metropolis Monte Carlo (MC) simulations. The coupling between the two spheres containing the spins [Formula: see text] = 3/2 and the spins S = 7/2 belongs to the core and shell, respectively. This study is based on the RKKY (Ruderman–Kittel–Kasuya–Yoshida) interaction type. To complete this work, we carry out the hysteresis cycles of the studied system for different nonmagnetic surface (NMS) numbers.

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