Linear algebra Orthogonal Projections

In summary, the conversation discusses the proof that a projection operator T, satisfying the condition ||Tx||≤||x||, is also an orthogonal projection. The suggested approach is to prove that the range of T is equal to the orthogonal complement of its null space, and to show that T is equal to its adjoint. However, there is some confusion about how to incorporate the norm inequality into the proof. The asker welcomes any advice on how to approach the proof.
  • #1
zcd
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Homework Statement


Given T is a projection such that ||Tx||≤||x||, prove T is an orthogonal projection.

Homework Equations


[tex]T:V\to V[/tex] (V finite dimensional)
[tex]<Tx,y>=<x,T^* y>[/tex]

general projection/idempotent operator:
[tex]V=R(T)\oplus N(T)[/tex]
[tex]T^2=T[/tex]


orthogonal projection:
[tex]R(T)=N(T)^{\perp}[/tex]
[tex]T^2=T=T^*[/tex]

The Attempt at a Solution


I think the most straightforward approach would be to prove [tex]R(T)=N(T)^{\perp}[/tex], most likely by showing they contain each other. I'm having trouble seeing how the inequality of norms comes in though.
 
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  • #2
I think it may be related to showing T=T^*. Any advice on how to approach this would be appreciated, thank you.
 

FAQ: Linear algebra Orthogonal Projections

What is a linear algebra orthogonal projection?

A linear algebra orthogonal projection is a mathematical operation that projects a vector onto a subspace in a way that preserves orthogonality. It is commonly used in linear algebra to find the closest approximation of a vector in a subspace.

How is a linear algebra orthogonal projection calculated?

A linear algebra orthogonal projection is calculated using the formula P = A(ATA)-1AT, where A is the matrix representing the subspace onto which the vector is being projected. This formula can also be represented as P = A(ATA)-1u, where u is the vector being projected.

What is the purpose of a linear algebra orthogonal projection?

The purpose of a linear algebra orthogonal projection is to find the best approximation of a vector in a subspace. This is useful in many applications, such as data analysis, machine learning, and image processing.

What are some properties of linear algebra orthogonal projections?

Some properties of linear algebra orthogonal projections include:

  • They are idempotent, meaning that when applied repeatedly, the result will not change.
  • They preserve vector lengths and angles, meaning that the projected vector will have the same length and angle with other vectors in the subspace as the original vector.
  • They are self-adjoint, meaning that the transpose of the projection matrix is equal to the projection matrix itself.

How is a linear algebra orthogonal projection used in real-world applications?

Linear algebra orthogonal projections have many real-world applications, including:

  • In data analysis, they can be used to reduce the dimensionality of a dataset while preserving important features.
  • In machine learning, they can be used for dimensionality reduction, feature extraction, and classification tasks.
  • In image processing, they can be used to enhance images or to remove noise.
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