Linear transformation of an orthonormal basis

In summary, the problem asks to show that there exists an orthonormal basis for a linear transformation from Rm to Rn such that the transformed vectors are orthogonal. The hint suggests considering an orthonormal basis for the symmetric matrix ATA. This is because if v1 and v2 are eigenvectors of a symmetric matrix with distinct eigenvalues, then they are orthogonal. The matrix A^T A is diagonalizable by orthogonal matrices, so there exists an orthonormal basis for it. Using this, it can be shown that the transformed vectors are orthogonal.
  • #1
zwingtip
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Homework Statement


Consider a linear transformation L from Rm to Rn. Show that there is an orthonormal basis {v1,...,vm} of Rm to Rn such that the vectors {L(v1),...,L(vm)} are orthogonal. Note that some of the vectors L(vi) may be zero. HINT: Consider an orthonormal basis {v1,...,vm} for the symmetric matrix ATA.


Homework Equations


if v1 and v2 are eigenvectors of a symmetric matrix with distinct eigenvalues [tex]\lambda_1[/tex] and [tex]\lambda_2[/tex], then v1 and v2 are orthogonal


The Attempt at a Solution


I have no idea how to even start this problem and I've been trying for a couple of days. Can anybody give me a tip as to how attack it? Thanks.
 
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  • #2
The matrix A^T A is diagonalizable by orthogonal matrices since it is symmetric. Therefore there exists an orthonormal basis v_1,..., v_m such that A^T A v_i = c_i v_i for some constant c_i. Now can you show that the Av_i are all orthogonal to each other?
 

1. What is an orthonormal basis?

An orthonormal basis is a set of vectors in a vector space that are all mutually perpendicular (orthogonal) to each other and have a length of 1 (normalized). This means that the basis vectors are independent and can be used to represent any vector in the vector space.

2. What is a linear transformation?

A linear transformation is a function that maps one vector space to another vector space while preserving the vector operations of addition and scalar multiplication. In other words, the output of a linear transformation is a linear combination of the input vectors.

3. How does a linear transformation affect an orthonormal basis?

A linear transformation preserves the orthonormality and length of the basis vectors. This means that the transformed basis vectors will still be mutually perpendicular and have a length of 1, but they may be in a different direction than the original basis vectors.

4. What is the importance of an orthonormal basis in linear algebra?

An orthonormal basis is important because it simplifies vector operations and calculations. It also allows for a unique representation of vectors and makes it easier to find the coordinates of a vector in a given basis.

5. How is an orthonormal basis used in practical applications?

An orthonormal basis is commonly used in computer graphics, signal processing, and data analysis. It allows for the decomposition of complex data into simpler components and aids in the visualization and manipulation of data. It is also used in solving systems of linear equations and in other areas of mathematics and physics.

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