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Data Interpolation
| Content Provider | Scilit |
|---|---|
| Author | Bashier, Eihab B. M. |
| Copyright Year | 2020 |
| Description | Data interpolation means to use a given set of n + 1 data points to approximate a function f(x) by a polynomial P$ _{ n }$(x) = a$ _{ n }$ x$ ^{ n }$ + a$ _{ n−1}$ x$ ^{ n−1}$ + … + a$ _{1}$ x + a$ _{0}$ (of degree not exceeding n), such that P$ _{ n }$(x$ _{ i }$) = f(x$ _{ i }$), i = 0, …, n, where a$ _{0}$, …, a$ _{ n }$ are constants. The data points are given by the table: x x$ _{0}$ x$ _{1}$ … x$ _{ n }$ f(x) f(x$ _{0}$) f(x$ _{1}$) … f(x$ _{ n }$) where x$ _{ i }$ ≠ x$ _{ j }$ for i ≠ j and x$ _{0}$ < x$ _{1}$ … < x$ _{n}$. This chapter discusses some of the interpolation methods and their implementation in MATLAB® and Python. It is divided into four sections. Section 1 discusses Lagrange interpolation and its implementation in MATLAB and Python. Section 2 discusses Newton's interpolation and the divided difference technique for finding the coefficients of Newton's interpolation. One-dimensional interpolations with MATLAB and Python are discussed in Sections 3 and 4. Book Name: Practical Numerical and Scientific Computing with MATLAB® and Python |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2018-0-91821-9&isbn=9780429021985&doi=10.1201/9780429021985-5&format=pdf |
| Ending Page | 124 |
| Page Count | 20 |
| Starting Page | 105 |
| DOI | 10.1201/9780429021985-5 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-03-18 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Practical Numerical and Scientific Computing with Matlab® and Python Function Implementation Lagrange Polynomial Newton Interpolation Data Exceeding |
| Content Type | Text |
| Resource Type | Chapter |
