Projection Operators on Vector Spaces: Clarifying Mistakes

In summary, in this conversation, it is discussed how two different direct sum decompositions of a vector space V can lead to a seemingly incorrect result when using linear projection operators. It is pointed out that for v and w not in the subspace V_1, it is not always true that P_2(v) = P_2(w) and a mistake is identified in this understanding.
  • #1
HenryGomes
7
0
Supposing we have a vector space [tex]V[/tex] and a subspace [tex]V_1\subset V[/tex].
Suppose further that we have two different direct sum decompositions of the total space [tex]V=V_1\oplus V_2[/tex] and [tex]V_1\oplus V_2'[/tex]. Given the linear projection operators [tex]P_1, P_2, P_1', P_2'[/tex] onto these decompositions, we have that [tex]P_2\circ P_1=P_2\circ P_1'=0[/tex]. But then we have that [tex]P_2(v)=P_2(P_1'+P_2')(v)=P_2 (P_2'(v))[/tex]. Now, for [tex]v\notin V_1[/tex], given any [tex]w\notin V_1[/tex], we can find a decomposition such that [tex]P_2'(v)=w[/tex].
This gives the apparently wrong result that for any [tex]v,w\notin V_1, ~P_2(v)=P_2(w)[/tex]. Can anyone clarify the mistake?
 
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  • #2
HenryGomes said:
Now, for [tex]v\notin V_1[/tex], given any [tex]w\notin V_1[/tex], we can find a decomposition such that [tex]P_2'(v)=w[/tex].
Can you elaborate on this?
 
  • #3
Of course, I can't, because it's not true. Only if [tex]v-w\in V_1[/tex]. I don't know from where I got that idea...
Thanks
 

1. What is a projection operator?

A projection operator is a linear transformation that maps a vector space onto itself, such that the image of the projection is equal to its original space. In simpler terms, it is a mathematical tool used to project a vector onto a subspace within a larger vector space.

2. How do projection operators work?

Projection operators work by taking a vector and breaking it down into its component parts along a given subspace. This is done by finding the orthogonal complement of the subspace and projecting the vector onto it, resulting in a vector that lies entirely within the subspace.

3. What mistakes are commonly made when using projection operators?

One common mistake when using projection operators is forgetting to check for linear independence within the subspace. This can lead to inaccurate projections and incorrect results. Another mistake is not considering the dimensionality of the subspace, as projection operators only work for subspaces of equal or lower dimension than the original space.

4. What are some practical applications of projection operators?

Projection operators have many practical applications in fields such as physics, engineering, and computer science. They are commonly used in signal processing to filter out unwanted noise, in image and video compression to reduce file size, and in machine learning for dimensionality reduction.

5. Are there different types of projection operators?

Yes, there are two main types of projection operators: orthogonal and oblique. Orthogonal projection operators preserve the angles and lengths of vectors, while oblique projection operators alter them. In addition, there are also idempotent projection operators, which when applied multiple times, result in the same projection as the first time.

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