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Interpolation Techniques In Numerical Computation
Subject area: Science,Engineering and Technology · Area of research: Electrical Engineering
Abstract
In this paper we will come across introduction to interpolation and calculus of finite differences. It further includes various polynomial interpolation methods like that of Lagrange?s, Newton?s forward, and backward & central difference method. These help us to calculate any number of numerical integrations with minimal error. The main idea lies in increasing the coefficients rather than an interval .In order to reduce the numerical computations a formula has been derived from Newton?s interpolation method. Application of this formula can be seen and is formulated below.
References
[1] John G Proakis and Dimitris G Manolakis “Digital Processing System” Forth Edition Chapter 2, Page no. 43 Sept 2012
[2] Meijering Erikson, “A Chronology of Interpolation from Astronomy to Modern Signal and Image Processing” vol. 91 no.3. (Sam Chui)
[3] Steven Chapra, “Applied Numerical Methods with Interpolation”, Forth Edition, Chapter 18 Page no. 474 IEEE 2017
[4] Jianping Xiao, Xuecheng Zou, “Adaptive Interpolation Algorithm for time Image Resizing” vol. 2 , February 2018
[5] Changbum Chun, “Iterative method improving Newton’s method by interpolation method”, March 2009.
[6] Sumita Arora, “Computational methods for Lagrange’s Interpolation method” (Dhanpat Rai and corporation)
How to cite this paper
@article{1701792,
author = {VISHAL. V. MEHTRE, ASHUTOSH MISHRA},
title = {Interpolation Techniques In Numerical Computation},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {6},
pages = {27-29},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1701792.pdf},
abstract = {In this paper we will come across introduction to interpolation and calculus of finite differences. It further includes various polynomial interpolation methods like that of Lagrange?s, Newton?s forward, and backward & central difference method. These help us to calculate any number of numerical integrations with minimal error. The main idea lies in increasing the coefficients rather than an interval .In order to reduce the numerical computations a formula has been derived from Newton?s interpolation method. Application of this formula can be seen and is formulated below.},
month = {December},
}