Why do we need to normalize vectors for?Is it just to cut down on

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Normalizing vectors is essential for simplifying calculations in various mathematical contexts, such as projections and angle measurements between vectors. It ensures that formulas, like those involving Fourier series and orthonormal bases, hold true by maintaining unit length for basis vectors. Normalization also facilitates easier application of techniques like the Gram-Schmidt procedure, which benefits from orthonormal sequences. Overall, normalizing vectors streamlines computations and enhances clarity in mathematical expressions. This practice is crucial in fields involving integral transforms, such as Fourier and wavelet transforms.
matqkks
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Why do we need to normalize vectors for?
Is it just to cut down on the arithmetic when finding other quantities of the vector?
Does it make life simpler to normalize vectors?
 
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Hi matqkks! :smile:

It is sometimes essential. For example, the Fourier series w.r.t. a orthogonal basis is given by

\sum_{n=0}^{+\infty}{<x,e_i>\frac{e_i}{\|e_i\|}}

This formula wouldn't be true if we didn't normalize the ei.

But most of the time, I guess it's just easier. You can perform a Gramm-Schmidt procedure where you just obtain an orthogonal sequence, but it's nicer if you also know it's orthonormal. It makes a lot of formula's easier if you normalize.
 


matqkks said:
Why do we need to normalize vectors for?
Is it just to cut down on the arithmetic when finding other quantities of the vector?
Does it make life simpler to normalize vectors?

There are a variety of reasons why you might want to normalize a vector.

One reason includes projections. Another reason might include the need to find angles between vectors: in order to do this you need to normalize vectors so the a . b = |a| |b| cos(a,b) and since |a| = |b| = 1, you can get cos(a,b) directly.

Also things like decomposition require this. As micromass has pointed out above, decompositions require that you normalize elements.

When you are doing projections in integral transforms, you are dealing with orthonormal basis and one property of orthonormal basis is that the the length of a basis vector is unit length (i.e. 1). If you ever look at integral transforms like Fourier transforms and wavelet transforms, you will see what I am talking about.
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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