Singular value decomposition of adjoint

In summary, the conversation discusses the singular value decomposition of a linear transformation T over a finite-dimensional complex inner product space. The goal is to show that the adjoint of T, denoted T*, can be expressed as a sum involving the singular values of T and orthonormal bases of the space. The conversation goes through a series of calculations to show this relation, and it is eventually discovered that the singular values of T are equal to their complex conjugates, leading to the desired expression.
  • #1
fluxions
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0

Homework Statement


Let T be a linear transformation over V, a finite-dimensional complex inner product space (with inner product < , >). Suppose T has the singular value decomposition
[tex] Tv = \sum_j^n s_j \left\langle v,e_j \right\rangle f_j [/tex]
where s_1, ... s_n are the singular values of T and (e_1, ..., e_n) and (f_1, ..., f_n) are orthonormal bases of V.

I'm supposed to show that (* denotes adjoint)
[tex] T^*v = \sum_j^n s_j \left\langle v, f_j\right\rangle e_j. [/tex]

The Attempt at a Solution


(I'll suppress the upper limit on the sums. Overline denotes complex conjugation.)
[tex] T^*v = \sum_j \left\langle T^*v, e_j \right\rangle e_j [/tex]
Require <Te_i, v> = <e_i, T*v> for i = 1, ..., n.
Calculating these, I get:
[tex]
\left\langle Te_i,v \right\rangle = \left\langle \sum_j s_j \left\langle e_i, e_j \right\rangle f_j, v \right\rangle = \sum_j s_j \left\langle e_i, e_j \right\rangle \left\langle f_j, v \right\rangle = \sum_j s_j \delta_{ij} \left\langle f_j, v \right\rangle = s_i \left\langle f_i, v \right\rangle
[/tex]
And:
[tex]
\left\langle e_i, T^*v \right\rangle = \left\langle e_i, \sum_j \left\langle T^*v, e_j \right\rangle e_j \right\rangle = \sum_j \overline{\left\langle T^*v, e_j \right\rangle} \left\langle e_i, e_j \right\rangle = \sum_j \overline{\left\langle T^*v, e_j \right\rangle} \delta_{ij} = \overline{\left\langle T^*v, e_i \right\rangle}. [/tex]

Hence for i = 1, ..., n, I obtain:
[tex]
\left\langle T^*v, e_i \right\rangle = \overline{s_i}\left\langle v, f_i \right\rangle
[/tex]

This *almost* gives the desired expression, except for I'm off by the complex conjugate of the s_i's. Where did I go wrong?
 
Last edited:
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  • #2
I figured it out while sleeping. The singular values of T are the eigenvalues of [itex] \sqrt{T^*T} [/itex] which is a self-adjoint operator, and therefore has real eigenvalues. So [itex] s_i = \overline{s_i} [/itex] for each i. The desired relation follows.
 

1. What is singular value decomposition (SVD)?

SVD is a mathematical procedure used to decompose a matrix into three simpler matrices. The resulting matrices can then be used for various mathematical operations, such as solving linear systems of equations and identifying the most important features in a dataset.

2. What is the purpose of SVD?

The purpose of SVD is to simplify complex matrices and make them easier to work with. It can also be used for data compression and feature selection in machine learning and data analysis.

3. What is the difference between SVD and adjoint SVD?

SVD and adjoint SVD are two different methods of decomposing a matrix. The main difference is that SVD decomposes a matrix into three matrices, while adjoint SVD decomposes a matrix into two matrices and a diagonal matrix.

4. How is SVD used in image processing?

In image processing, SVD can be used for image compression and noise reduction. By decomposing an image into its singular values and vectors, the most important features can be identified and the image can be reconstructed with a lower dimensionality, reducing storage space and improving image quality.

5. What are the applications of SVD in data analysis?

SVD has many applications in data analysis, including dimensionality reduction, feature selection, and data compression. It is also used in collaborative filtering and recommender systems, as well as in natural language processing for topic modeling and semantic analysis.

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