The approximation or approximate solution of mathematical puzzles is the focus of numerical mathematics. We set off numerical mathematics, numerical linear algebra, numerical nonlinear equations solutions, approximation and interpolation techniques, etc. Knowing and analyzing the error estimate is important in order to use numerical mathematics techniques.
In general, the issue we resolve is referred to as input information, and the solution is referred to as output information. An algorithm is a method for converting input data into output data. An approximation or approximate match, as well as interpolation or precise matches, are discussed in this work. We employ interpolation for a small quantity of input data because it allows us to precisely create functions that pass through all of the provided points. While approximation admits some degree of inaccuracy, interpolation entails the passage of an interpolation function overall provided locations. The resultant function is then smoothed.
In this “Interpolation and Approximation - Numerical Methods” you will learn about the following topics:
- Lagrangian’s polynomials
- Newton’s interpolation using difference and divided difference
- Cubic spline interpolation
- Curve fitting: least-squares lines for linear and nonlinear data
==== Point to Note ====
This article Interpolation and Approximation - Numerical Methods is contributed by Namrata Chaudhary, a student of Lumbini Engineering College (LEC).
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