Orthogonal Projections vs Non-orthogonal projections?

In summary, the conversation covered the topic of orthogonal projections in Linear Algebra. An example was given using the vectors y, v1, and v2, and it was shown that the projection of y can be found by using the dot product of y with v1 and v2. It was also mentioned that a set is orthogonal if the dot product of its vectors is equal to 0. The conversation ended with a request for help and a side note about using brackets and symbols.
  • #1
rayzhu52
1
0
Hi everyone,

My Linear Algebra Professor recently had a lecture on Orthogonal projections.

Say for example, we are given the vectors:

y = [3, -1, 1, 13], v1 = [1, -2, -1, 2] and v2 = [-4, 1, 0, 3]

To find the projection of y, we first check is the set v1 and v2 are orthogonal:

v1 • v2 = -4 -2 + 0 + 6 = 0

So we know the set is orthogonal and we can now find the projection of y, or [itex]\hat{y}[/itex]:

[itex]\hat{y}[/itex] =[(y • v1)/(v1 • v1) * v1)
+ [(y • v2)/(v2 • v2) * v2)]
= some value

Now, we covered what it means when a set is non-orthogonal v1 • vn≠ 0,
but what if we are asked to find [itex]\hat{y}[/itex]?

Any form of help would be greatly appreciated!
 
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  • #2
hi rayzhu52! :smile:

(btw, where did you get • from? use . or · (on a mac, it's alt-shift-9))

(and you've been using too many brackets :wink:)


the easiest way is probably to start by finding the unit normal, a multiple of v1 x v2 :smile:
 

1. What is the difference between orthogonal and non-orthogonal projections?

Orthogonal projections involve projecting a vector onto a subspace in such a way that the projected vector is perpendicular to the subspace. Non-orthogonal projections, on the other hand, do not require the projected vector to be perpendicular to the subspace.

2. When should I use orthogonal projections and when should I use non-orthogonal projections?

Orthogonal projections are useful when working with geometric shapes or when preserving distances is important. Non-orthogonal projections are more versatile and can be used in a wider range of applications, such as data analysis and signal processing.

3. How do I calculate an orthogonal projection?

The formula for calculating an orthogonal projection involves finding the dot product between the vector to be projected and the basis vectors of the subspace, and then multiplying these dot products by the corresponding basis vectors.

4. Can non-orthogonal projections be converted to orthogonal projections?

Yes, non-orthogonal projections can be converted to orthogonal projections by using a process called Gram-Schmidt orthogonalization. This involves finding an orthogonal basis for the subspace and then projecting the vector onto this basis.

5. In what real-life situations are orthogonal and non-orthogonal projections used?

Orthogonal projections are commonly used in engineering and physics, such as in 3D computer graphics and structural analysis. Non-orthogonal projections have applications in machine learning, data compression, and image processing.

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