Proving Matrix X rank Decomposition

Click For Summary
SUMMARY

The discussion focuses on proving that a matrix X with rank n can be expressed as the sum of two matrices Y and Z, where Y has rank n-1 and Z has rank 1. It emphasizes the utility of matrix products as sums of rank-1 matrices, specifically using the example of matrices A and B, where their product AB can be decomposed into a summation of rank-1 matrices. The discussion highlights the importance of matrix factorization techniques, particularly Singular Value Decomposition (SVD), in achieving this decomposition.

PREREQUISITES
  • Understanding of matrix rank and properties
  • Familiarity with matrix multiplication and decomposition
  • Knowledge of Singular Value Decomposition (SVD)
  • Basic concepts of linear algebra
NEXT STEPS
  • Study the properties of matrix rank and its implications in linear algebra
  • Learn about matrix decomposition techniques, focusing on SVD
  • Explore applications of rank-1 matrices in data science and machine learning
  • Investigate other matrix factorization methods, such as QR decomposition
USEFUL FOR

Mathematicians, data scientists, and anyone involved in linear algebra or matrix theory who seeks to deepen their understanding of matrix decomposition and its applications.

rhuelu
Messages
17
Reaction score
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!
 
Physics news on Phys.org
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.
 

Similar threads

  • · Replies 14 ·
Replies
14
Views
5K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 9 ·
Replies
9
Views
5K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 2 ·
Replies
2
Views
4K