Results 41 to 50 of about 214 (158)
Geometric approaches to matrix normalization and graph balancing
Normal matrices, or matrices which commute with their adjoints, are of fundamental importance in pure and applied mathematics. In this paper, we study a natural functional on the space of square complex matrices whose global minimizers are normal ...
Tom Needham, Clayton Shonkwiler
doaj +1 more source
Due to the massive and extensive number of variables within the dataset, correctly classifying educational data into a particular category remains challenging.
Suantai Suthep +4 more
doaj +1 more source
combinatorial optimization, branch and bound, artificial intelligence, 65K05, 65K10,
Albert Corominas, Rafael Pastor
core +1 more source
Acceleration of Convergence in Dontchev’s Iterative Method for Solving Variational Inclusions [PDF]
2000 Mathematics Subject Classification: 47H04, 65K10.In this paper we investigate the existence of a sequence (xk ) satisfying 0 ∈ f (xk )+ ∇f (xk )(xk+1 − xk )+ 1/2 ∇2 f (xk )(xk+1 − xk )^2 + G(xk+1 ) and converging to a solution x∗ of the generalized
Geoffroy, M., Hilout, S., Pietrus, A.
core
Predicting COVID-19 outbreak in India using modified SIRD model
In this paper, the existing Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiologic process model is modified for forecasting the coronavirus effect in India.
Sakshi Shringi +5 more
doaj +1 more source
Steffensen Methods for Solving Generalized Equations [PDF]
2000 Mathematics Subject Classification: 65G99, 65K10, 47H04.We provide a local convergence analysis for Steffensen's method in order to solve a generalized equation in a Banach space setting. Using well known fixed point theorems for set-valued maps [13]
Argyros, Ioannis K., Hilout, Saïd
core
A high-accuracy differential quadrature method with optimized spline basis for Burgers’ equation
In this study, a new numerical approach is presented by combining the differential quadrature method with particle swarm optimization for an efficient solution of nonlinear partial differential equations. The proposed methodology is illustrated using the
Simran Sahlot +4 more
doaj +1 more source
In deep learning, interval neural networks are used to quantify the uncertainty of a pre-trained neural network. Suppose we are given a computational problem $P$ and a pre-trained neural network $\Phi _P$ that aims to solve $P$ .
Luca Eva Gazdag +2 more
doaj +1 more source
Pseudomonotone operators and the Bregman Proximal Point Algorithm
Pseudomonotone operators, Variational inequalities, Bregman distances, Proximal Point algorithm, Interior-point-effect, 47J20, 65J20, 65K10, 90C26, 90C30,
Nils Langenberg
core +1 more source
GENERALIZED NONLINEAR VARIATIONAL INEQUALITIES [PDF]
. In this paper, we consider a generalized nonlinear variational inequality problem involving single valued and multivalued nonlinear operators. We also study criteria of its solvability. Iterative methods for approximate solution are also proposed and a
Suja Varghese, Balwant Singh Thakur
core

