Proof of/Reason for SVT Decomposition

In summary, for a flat FRW perturbed universe with metric ds^2=a^2(\tau)[(1+2A)dt^2-B_idtdx^i-(\delta_{ij}+h_{ij})dx^idx^j], the vector B_i can be decomposed into two parts: B_i^{\perp} and B_i^{\parallel}, where \nabla \cdot B^{\perp}=\nabla \times B^{\parallel}=0. In Fourier space, this means the vector is decomposed into a parallel and perpendicular component to the wavevector k. This same decomposition can also be applied to the tensor hij (eq. 4.2.
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AuraCrystal
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Using the conventions of http://www.damtp.cam.ac.uk/user/db275/Cosmology/Chapter4.pdf (not mine).

For a flat FRW perturbed universe, the metric is can be written in general as:
[tex]ds^2=a^2(\tau)[(1+2A)dt^2-B_idtdx^i-(\delta_{ij}+h_{ij})dx^idx^j][/tex]
I understand intuitively that we can decompose Bi into two parts:
[tex]B_i=B_i^{\perp}+B_i^{\parallel}[/tex]
with
[tex]\nabla \cdot B^{\perp}=\nabla \times B^{\parallel}=0[/tex]
In Fourier space, this means that we decompose the vector into two parts: one parallel to the wavevector k, and one perpendicular.

(And of course, we can write a curl-less vector as a gradient of a scalar.)

He then writes down a similar decomposition for the tensor hij (eq. 4.2.35-4.2.37). What's the reason/justification for the form of that?
 
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FAQ: Proof of/Reason for SVT Decomposition

What is SVT decomposition?

SVT decomposition, also known as singular value thresholding decomposition, is a mathematical method used to decompose a matrix into three smaller matrices. It is commonly used in signal processing and data analysis to reduce the noise and extract important information from a given dataset.

How does SVT decomposition work?

SVT decomposition works by taking a matrix and decomposing it into three matrices: U, S, and V. U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values of the original matrix. The decomposition process involves thresholding the singular values to remove noise and retain the important information in the dataset.

What are the applications of SVT decomposition?

SVT decomposition has various applications in different fields, such as image processing, machine learning, and data analysis. It is commonly used to reduce noise in images and extract features from a dataset. It is also used in recommendation systems, where it helps in identifying important features in a dataset.

How is SVT decomposition different from other matrix decompositions?

SVT decomposition is different from other matrix decompositions, such as SVD (singular value decomposition) and PCA (principal component analysis), in that it introduces a thresholding step. This step allows for better noise reduction and feature extraction, making it more suitable for datasets with high levels of noise.

What are the advantages of using SVT decomposition?

One of the main advantages of SVT decomposition is its ability to reduce noise and extract important features from a dataset. It is also computationally efficient and can handle large datasets. Additionally, it is a flexible method that can be applied to various types of data, making it a popular choice in many scientific applications.

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