Proving Matrix X rank Decomposition

In summary, matrices can be written as the sum of rank-1 matrices, which can be helpful in proving that a matrix with rank n can be decomposed into matrices with ranks n-1 and 1. This can be done by finding any decomposition of the matrix and writing it as a sum of rank-1 matrices, such as using the SVD.
  • #1
rhuelu
17
0
How can you prove that matrix X with rank n can be written as the sum of matrices Y and Z where Y has rank n-1 and Z has rank of 1. Thanks!
 
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  • #2
It may be helpful to think of matrix products as sums of rank-1 matrices. For example, consider matrices A and B and their product AB. If the columns of A are a1, a2, ..., and the rows of B are b1*, b2*, ..., then the product is

[tex]AB = \left[\begin{array} & a_1 \vline a_2 \vline ... \vline a_n\end{array}\right]\left[\begin{array} & b_1^* & \hline & b_2^* & \hline & \vdots & \hline & b_n^*\end{array}\right] = \sum_{i=1}^n a_i b_i^*[/tex]

Where [itex]a_i b_i^*[/itex] are all rank-1 matrices.

Now if you have a matrix M, all you have to do is find any decomposition of it (M = AB), and you can write it as the sum of rank-1 matrices. M = MI works just fine (can you see what this is this in summation form?), or you could use any other factorization you like. The SVD is particularly enlightening in this regard.
 

1. What is the purpose of proving matrix X rank decomposition?

The purpose of proving matrix X rank decomposition is to determine the rank of a matrix, which is the maximum number of linearly independent rows or columns in the matrix. This is an important concept in linear algebra and has many applications in fields such as engineering, computer science, and data analysis.

2. How do you prove matrix X rank decomposition?

To prove matrix X rank decomposition, you can use various methods such as Gaussian elimination, the singular value decomposition (SVD) method, or the rank-nullity theorem. These methods involve manipulating the matrix and its associated operations to determine the rank.

3. What is the significance of the rank of a matrix?

The rank of a matrix is significant because it tells us about the linear independence of the rows or columns of the matrix. A higher rank indicates a greater number of linearly independent rows or columns, which can be useful in solving systems of linear equations and understanding the properties of the matrix.

4. Can a matrix have a rank greater than its dimensions?

No, a matrix cannot have a rank greater than its dimensions. The rank of a matrix is always less than or equal to the smaller of its number of rows or columns. This is because the rank is determined by the linear independence of the rows or columns, and there cannot be more linearly independent rows or columns than the number of rows or columns in the matrix.

5. What are some real-world applications of matrix X rank decomposition?

Matrix X rank decomposition has various real-world applications such as image compression, data compression, data analysis, and solving systems of linear equations. It is also used in machine learning algorithms, signal processing, and network analysis. Additionally, rank decomposition is important in understanding the properties of matrices and their applications in various fields of science and engineering.

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