generalized newton
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2021 ◽  
Vol 112 ◽  
pp. 103009
Author(s):  
Jinling Liu ◽  
Junzheng Jiang ◽  
Jiming Lin ◽  
Junyi Wang

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
M. A. Rehman ◽  
Amir Naseem ◽  
Thabet Abdeljawad

In this paper, we propose two novel iteration schemes for computing zeros of nonlinear equations in one dimension. We develop these iteration schemes with the help of Taylor’s series expansion, generalized Newton-Raphson’s method, and interpolation technique. The convergence analysis of the proposed iteration schemes is discussed. It is established that the newly developed iteration schemes have sixth order of convergence. Several numerical examples have been solved to illustrate the applicability and validity of the suggested schemes. These problems also include some real-life applications associated with the chemical and civil engineering such as adiabatic flame temperature equation, conversion of nitrogen-hydrogen feed to ammonia, the van der Wall’s equation, and the open channel flow problem whose numerical results prove the better efficiency of these methods as compared to other well-known existing iterative methods of the same kind.


2021 ◽  
Vol 14 (3) ◽  
pp. 339-350
Author(s):  
Yueyong Shi ◽  
Jian Huang ◽  
Yuling Jiao ◽  
Yicheng Kang ◽  
Hu Zhang

2021 ◽  
Vol 31 (2) ◽  
pp. 1184-1214
Author(s):  
Boris S. Mordukhovich ◽  
M. Ebrahim Sarabi

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Lei Shi ◽  
Javed Iqbal ◽  
Muhammad Arif ◽  
Alamgir Khan

In this paper, we suggest a Newton-type method for solving the system of absolute value equations. This new method is a two-step method with the generalized Newton method as predictor. Convergence of the proposed method is proved under some suitable conditions. At the end, we take several numerical examples to show that the new method is very effective.


Author(s):  
ali ashrafi ◽  
Arezu Zare

This paper examines a complex fractional quadratic optimization problem subject to two quadratic constraints. The original problem is transformed into a parametric quadratic programming problem by the well-known classical Dinkelbach method. Then a semidefinite and Lagrangian dual optimization approaches are presented to solve the nonconvex parametric problem at each iteration of the bisection and generalized Newton algorithms. Finally, the numerical results demonstrate the effectiveness of the proposed approaches.


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