Interpolation of Functions

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The first line shows concatenation; the second shows the expansion trick:.

Function Interpolation

The first technique builds the final string by concatenating smaller strings, avoiding interpolation but achieving the same end. When you absolutely must have interpolation, you need the punctuation-riddled interpolation from the Solution. You can do more than simply assign to a variable after interpolation.

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For instance, this example will build a string with an interpolated expression and pass the result to a function:. Although these techniques work, simply breaking your work up into several steps or storing everything in temporary variables is almost always clearer to the reader. In version 5. This bug is fixed in version 5. Polynomial Interpolation is useful in many ways; however, one should be careful to its limitation of usage.

As how different methods are born, Piece-wise Interpolation solves these complications.

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Piece-wise interpolation answers these by fitting a large number of data points with low-degree polynomials. Since we only use low-degree polynomials, we eliminate excessive oscillations and non-convergence. General Concept: Given a set of data points, a different polynomial is used in each interval such that we interpolate several interpolants at successive points. GOAL: Laying all those concepts and primary concerns, we aim to find an interpolating function that is smooth and does not change too much between node points.

We now discuss the method below. Why is it called Natural Cubic Spline?

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Now presenting the main points:. We specify how it originated using the derivation. This one is quite long but is understandable for those who know derivatives and integrals. Since I view math as modeling, we start with an illustration. This will serve as to concretely tell what we want to explain and also our map while we do derivation.

Derivation Proper. The Derivation Proper is consists of two parts — each part arriving at an equation — which we will use and play an important part in computations.

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Having the needed formulas, we show how to use them. How to Use it for interpolation? We illustrate its usage through an example then later generalize the process. Data points are as follows: 2, 1 , 1, 0 , 5, 0 , 3, 0 , 4,1.


We first understand what it wants. We use Gaussian Elimination here:.

We find which knots does x is in: 1. General Process:. Must be seen as a suggestion only. There are many ways to do this. Given: data points. We will be needing these formulas, so it would be nice to get all your formulas settled before you start your calculations:. We can say that Natural Cubic Spline is a pretty interesting method for interpolation.

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Having known interpolation as fitting a function to all given data points, we knew Polynomial Interpolation can serve us at some point using only a single polynomial to do the job. One saving grace is Piece-wise Interpolation which does things using different low-degree polynomials which is especially useful with large data sets.

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