Convergence problem inab initio crystal orbital calculations

1981 ◽  
Vol 19 (6) ◽  
pp. 1207-1214 ◽  
Author(s):  
Alfred Karpfen
Keyword(s):  
Convergence Problem ◽  
Crystal Orbital

Intra-protocol Convergence Problem in BBRv2’s Bandwidth Probing

2020 ◽  
Author(s):  
Yeong-Jun Song ◽  
Won-Ju Eom ◽  
Jeong-Keun Kim ◽  
Chang-Hoon Park ◽  
Geon-Hwan Kim ◽  
...  
Keyword(s):  
Convergence Problem

scholarly journals Viscosity Approximation Methods for a Family of Nonexpansive Mappings in CAT(0) Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jinfang Tang
Keyword(s):  
Strong Convergence ◽  
Nonexpansive Mappings ◽  
Approximation Methods ◽  
Viscosity Approximation Method ◽  
Viscosity Approximation ◽  
Convergence Problem ◽  
Iterative Schemes ◽  
The Family ◽  
Viscosity Approximation Methods ◽  
Implicit And Explicit

The purpose of this paper is using the viscosity approximation method to study the strong convergence problem for a family of nonexpansive mappings in CAT(0) spaces. Under suitable conditions, some strong convergence theorems for the proposed implicit and explicit iterative schemes to converge to a common fixed point of the family of nonexpansive mappings are proved which is also a unique solution of some kind of variational inequalities. The results presented in this paper extend and improve the corresponding results of some others.

A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

2012 ◽  
Vol 605-607 ◽  
pp. 2442-2446
Author(s):  
Xin Ran Li ◽  
Yan Xia Jin
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Convergence Problem ◽  
Early Maturity ◽  
Classical Function ◽  
Function Test ◽  
Quantum Behavior ◽  
Swarm Algorithm

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

On the convergence problem for lattice sums

1990 ◽  
Vol 23 (12) ◽  
pp. 2523-2528 ◽  
Author(s):  
J P Buhler ◽  
R E Crandall
Keyword(s):  
Convergence Problem ◽  
Lattice Sums

Comments on a comparison of the photoelectron spectrum and crystal orbital calculations of polyethylene

1973 ◽  
Vol 23 (2) ◽  
pp. 206-210 ◽  
Author(s):  
J.M. André ◽  
J. Delhalle ◽  
S. Delhalle ◽  
R. Caudano ◽  
J.J. Pireaux ◽  
...  
Keyword(s):  
Photoelectron Spectrum ◽  
Crystal Orbital

scholarly journals Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fayadh Alenezi ◽  
K. C. Santosh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Network Stability ◽  
Signal To Noise ◽  
Convergence Problem ◽  
Information Fidelity ◽  
Medical Image Enhancement

One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

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