Sparse matrix computations with application to solve system of nonlinear equations

2013 ◽  
Vol 5 (5) ◽  
pp. 372-386
Author(s):  
Shahadat Hossain ◽  
Trond Steihaug
Keyword(s):  
Nonlinear Equations ◽  
Sparse Matrix ◽  
System Of Nonlinear Equations ◽  
Matrix Computations

Solving System of Nonlinear Equations using Genetic Algorithm

2019 ◽  
Vol 10 (4) ◽  
pp. 877-886 ◽  
Author(s):  
Chhavi Mangla ◽  
Musheer Ahmad ◽  
Moin Uddin
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equations ◽  
System Of Nonlinear Equations

scholarly journals Typical curve with G1 constraints for curve completion

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Chuan He ◽  
Gang Zhao ◽  
Aizeng Wang ◽  
Fei Hou ◽  
Zhanchuan Cai ◽  
...  
Keyword(s):  
Nonlinear Equations ◽  
Interpolation Problem ◽  
Sufficient Conditions ◽  
System Of Nonlinear Equations ◽  
Simple Construction ◽  
Completion Task ◽  
Curve Completion ◽  
Novel Algorithm

AbstractThis paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic curvature. The G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a solution. The proposed algorithm was applied to a curve completion task. The main advantages of the proposed method are its simple construction, compatibility with NURBS, and monotonic curvature.

Quantum Newton’s Method for Solving the System of Nonlinear Equations

SPIN ◽  
2021 ◽  
pp. 2140004
Author(s):  
Cheng Xue ◽  
Yuchun Wu ◽  
Guoping Guo
Keyword(s):  
Quantum Computing ◽  
Nonlinear Equations ◽  
Newton’S Method ◽  
Newton's Method ◽  
Linear Equations ◽  
Data Conversion ◽  
System Of Nonlinear Equations ◽  
Dimensional System ◽  
System Of Linear Equations ◽  
Whole Process

While quantum computing provides an exponential advantage in solving the system of linear equations, there is little work to solve the system of nonlinear equations with quantum computing. We propose quantum Newton’s method (QNM) for solving [Formula: see text]-dimensional system of nonlinear equations based on Newton’s method. In QNM, we solve the system of linear equations in each iteration of Newton’s method with quantum linear system solver. We use a specific quantum data structure and [Formula: see text] tomography with sample error [Formula: see text] to implement the classical-quantum data conversion process between the two iterations of QNM, thereby constructing the whole process of QNM. The complexity of QNM in each iteration is [Formula: see text]. Through numerical simulation, we find that when [Formula: see text], QNM is still effective, so the complexity of QNM is sublinear with [Formula: see text], which provides quantum advantage compared with the optimal classical algorithm.

Optimal Design With a Monomial-Based Version of Newton’s Method

1991 ◽  
Author(s):  
Scott A. Burns
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Engineering Design ◽  
Nonlinear Equations ◽  
Newton’S Method ◽  
Newton's Method ◽  
Geometric Mean ◽  
System Of Nonlinear Equations ◽  
Engineering Design Optimization

Abstract A monomial-based method for solving systems of algebraic nonlinear equations is presented. The method uses the arithmetic-geometric mean inequality to construct a system of monomial equations that approximates the system of nonlinear equations. This “monomial method” is closely related to Newton’s method, yet exhibits many special properties not shared by Newton’s method that enhance performance. These special properties are discussed in relation to engineering design optimization.

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