Interpolation vs Least Squares Approximation
Thomas J. Kennedy
1 Least Squares Approximation
We spent quite a bit of time discussing Least Squares Approximation. In total we discussed the following methods…
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$$[X^TX|X^TY]$$
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$$[A|\vec{b}]$$
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$$\frac{\partial}{\partial c_k} \int\limits_{\mathbb{R}} \left(f - \hat{\varphi}\right)^2$$
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Presolved $$[A|\vec{b}]$$ for a line
While we discussed four methods for deriving $\hat{\varphi}$… all four methods were really different notations for the same techniques. Note that our focus here is on Least Squares Approximation as applied to discrete data.
Least Squares Approximation is a single method.
2 Why Interpolation?
Least Squares Approximation captures the behavior of a set of data. Interpolation also captures the behavior of a set of data. However,
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Least Squares Approximation focuses on global behavior (the entire collection of data)
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Interpolation focuses on local behavior (around and at individual points)
Interpolation refers to a collection of techniques… with a common invariant.
The error at every input point must be zero. Given a polynomial $p(x)$, the polynomial must be equal to $f(x)$ at every input point ($x_0, x_1, \ldots, x_n$). This invariant is often written as
$$ \forall_{x_k} p(x_k) = f(x_k) $$
This notation is read as for all $x_k$ $p(x_k)$ must be equal to $f(x_k)$.
3 Let Us Try that Again… In Plain English
Least Squares Approximation can be thought of as…
We have a memory of a pieces of a picture and want to capture the idea as inspired by the pieces
Interpolation can be though of as…
We have pieces of a picture and want to fill in the blanks… while preserving the orignal pieces and trying to figure out should be around each piece.