Experiments with iterative methods for linear systems using GeoGebra and TI-Nspire

1970 ◽  
Vol 11 ◽  
pp. 89-102
Author(s):  
Gregor Milicic ◽  
Simon Plangg
Keyword(s):  
Linear Systems ◽  
Iterative Methods ◽  
Stem Education ◽  
Approximate Solutions ◽  
New Methods ◽  
Dynamical Aspect ◽  
Key Topics ◽  
Algorithmic Thinking ◽  
Unsolvable Problems

Algorithms and algorithmic thinking are key topics in STEM Education. By using algorithms approximate solutions can be obtained for analytical unsolvable problems. Before new methods can be safely applied they have to be thoroughly tested in experiments. In this article we present a series of exercise where students can experiment with algorithms and test them using GeoGebra or the TI-Nspire. Based on the results of such experiments the students can compare algorithms, showing them a heuristic and dynamical aspect of Mathematics.

scholarly journals Novel higher order iterative schemes based on the $ q- $Calculus for solving nonlinear equations

2021 ◽  
Vol 7 (3) ◽  
pp. 3524-3553
Author(s):  
Gul Sana ◽  
◽  
Muhmmad Aslam Noor ◽  
Dumitru Baleanu ◽  
◽  
...  
Keyword(s):  
Iterative Methods ◽  
Nonlinear Equations ◽  
Approximate Solutions ◽  
Auxiliary Function ◽  
Quantum Calculus ◽  
New Methods ◽  
Classical Calculus ◽  
Mathematical Sciences ◽  
Modern Era ◽  
Taylor’S Series

<abstract><p>The conventional infinitesimal calculus that concentrates on the idea of navigating the $ q- $symmetrical outcomes free from the limits is known as Quantum calculus (or $ q- $calculus). It focuses on the logical rationalization of differentiation and integration operations. Quantum calculus arouses interest in the modern era due to its broad range of applications in diversified disciplines of the mathematical sciences. In this paper, we instigate the analysis of Quantum calculus on the iterative methods for solving one-variable nonlinear equations. We introduce the new iterative methods called $ q- $iterative methods by employing the $ q- $analogue of Taylor's series together with the inclusion of an auxiliary function. We also investigate the convergence order of our newly suggested methods. Multiple numerical examples are utilized to demonstrate the performance of new methods with an acceptable accuracy. In addition, approximate solutions obtained are comparable to the analogous solutions in the classical calculus when the quantum parameter $ q $ tends to one. Furthermore, a potential correlation is established by uniting the $ q- $iterative methods and traditional iterative methods.</p></abstract>

scholarly journals Multipreconditioned GMRES for simulating stochastic automata networks

2018 ◽  
Vol 16 (1) ◽  
pp. 986-998
Author(s):  
Chun Wen ◽  
Ting-Zhu Huang ◽  
Xian-Ming Gu ◽  
Zhao-Li Shen ◽  
Hong-Fan Zhang ◽  
...  
Keyword(s):  
Steady State ◽  
Probability Distribution ◽  
Linear Systems ◽  
Iterative Methods ◽  
Communication Systems ◽  
Queueing Systems ◽  
Steady State Probability ◽  
Stochastic Automata ◽  
Automata Networks ◽  
Generator Matrices

AbstractStochastic Automata Networks (SANs) have a large amount of applications in modelling queueing systems and communication systems. To find the steady state probability distribution of the SANs, it often needs to solve linear systems which involve their generator matrices. However, some classical iterative methods such as the Jacobi and the Gauss-Seidel are inefficient due to the huge size of the generator matrices. In this paper, the multipreconditioned GMRES (MPGMRES) is considered by using two or more preconditioners simultaneously. Meanwhile, a selective version of the MPGMRES is presented to overcome the rapid increase of the storage requirements and make it practical. Numerical results on two models of SANs are reported to illustrate the effectiveness of these proposed methods.

scholarly journals Amesos2 and Belos: Direct and Iterative Solvers for Large Sparse Linear Systems

2012 ◽  
Vol 20 (3) ◽  
pp. 241-255 ◽  
Author(s):  
Eric Bavier ◽  
Mark Hoemmen ◽  
Sivasankaran Rajamanickam ◽  
Heidi Thornquist
Keyword(s):  
Linear Systems ◽  
Iterative Methods ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Direct Methods ◽  
Iterative Solvers ◽  
Software Project ◽  
Sparse Linear Systems ◽  
Scalar Type ◽  
Mixed Precision

Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples the algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers.

Introduction to Iterative Methods for the Solution of Linear Systems

2017 ◽  
pp. 407-439
Author(s):  
Larisa Beilina ◽  
Evgenii Karchevskii ◽  
Mikhail Karchevskii
Keyword(s):  
Linear Systems ◽  
Iterative Methods

SOR-like Methods with Optimization Model for Augmented Linear Systems

2017 ◽  
Vol 7 (1) ◽  
pp. 101-115 ◽  
Author(s):  
Rui-Ping Wen ◽  
Su-Dan Li ◽  
Guo-Yan Meng
Keyword(s):  
Linear Systems ◽  
Linear Equations ◽  
Optimization Technique ◽  
Relaxation Parameter ◽  
Convergence Theory ◽  
Optimization Models ◽  
System Of Linear Equations ◽  
Augmented System ◽  
New Methods ◽  
Outstanding Work

AbstractThere has been a lot of study on the SOR-like methods for solving the augmented system of linear equations since the outstanding work of Golub, Wu and Yuan (BIT 41(2001)71-85) was presented fifteen years ago. Based on the SOR-like methods, we establish a class of accelerated SOR-like methods for large sparse augmented linear systems by making use of optimization technique, which will find the optimal relaxation parameter ω by optimization models. We demonstrate the convergence theory of the new methods under suitable restrictions. The numerical examples show these methods are effective.

DMRG Approach to Fast Linear Algebra in the TT-Format

2011 ◽  
Vol 11 (3) ◽  
pp. 382-393 ◽  
Author(s):  
Ivan Oseledets
Keyword(s):  
Linear Systems ◽  
Linear Algebra ◽  
Direct Method ◽  
Fast Algorithms ◽  
Approximate Solutions ◽  
Vector Product ◽  
The Matrix ◽  
Minimization Scheme

AbstractIn this paper, the concept of the DMRG minimization scheme is extended to several important operations in the TT-format, like the matrix-by-vector product and the conversion from the canonical format to the TT-format. Fast algorithms are implemented and a stabilization scheme based on randomization is proposed. The comparison with the direct method is performed on a sequence of matrices and vectors coming as approximate solutions of linear systems in the TT-format. A generated example is provided to show that randomization is really needed in some cases. The matrices and vectors used are available from the author or at http://spring.inm.ras.ru/osel

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