Are Non-Singular Column Vectors of X Linearly Independent?

  • Context: Undergrad 
  • Thread starter Thread starter IniquiTrance
  • Start date Start date
Click For Summary
SUMMARY

If the matrix \(X^T X\) is non-singular, then the column vectors of matrix \(X\) are linearly independent. This conclusion is established through a proof by contradiction, where the existence of a non-zero vector \(y\) satisfying \(X^T X y = 0\) indicates linear dependence among the columns of \(X\). The discussion also highlights the utility of QR decomposition for simplifying the analysis of non-square matrices. The terms "contradiction" and "contraposition" are discussed, emphasizing the importance of understanding concepts over terminology.

PREREQUISITES
  • Understanding of linear algebra concepts, specifically linear independence and dependence.
  • Familiarity with matrix operations, particularly \(X^T X\) and determinants.
  • Knowledge of QR decomposition and its application to matrices.
  • Basic proof techniques, including proof by contradiction and contraposition.
NEXT STEPS
  • Study the properties of non-singular matrices and their implications in linear algebra.
  • Learn about QR decomposition and its applications in solving linear systems.
  • Explore proof techniques in mathematics, focusing on contradiction and contraposition.
  • Investigate the implications of linear independence in the context of machine learning and data analysis.
USEFUL FOR

Students of linear algebra, mathematicians, data scientists, and anyone involved in theoretical aspects of matrix analysis and linear independence.

IniquiTrance
Messages
185
Reaction score
0
Is it true to say that if X^T X is non-singular, then the column vectors of X must be linearly independent? I know how to prove that if the columns of X are linearly independent, then X^T X is non-singular. Just not sure about the other way around. Thanks!
 
Physics news on Phys.org
Is X a square matrix? If so use
det(X^TX) = det(X)^2=0

If not X is not square, but is real, then QR decomposition should reduce the problem to that of square matrices (something simpler may suffice, but this is the simplest approach I can think of right now).
 
Prove it by contradition. If the columns of X are linearly dependent, there is a non-zero vector ##y## such that ##X^TXy = 0##.
 
Thank you. I suspected it was true, but couldn't prove it to myself.
 
AlephZero said:
Prove it by contradition. If the columns of X are linearly dependent, there is a non-zero vector ##y## such that ##X^TXy = 0##.

That's a very nice proof, especially because you established necessary and sufficient conditions. The OP claimed that he could prove the converse, but, if he knew this proof he should have had no trouble.

Just one remark, instead of "contradiction", maybe you should have used "contraposition".
 
Dickfore said:
Just one remark, instead of "contradiction", maybe you should have used "contraposition".

Well, I used to know what "contrapositive" meant when I was a student, but these days I find understanding the concepts is more useful than remembering their names.
 

Similar threads

  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 27 ·
Replies
27
Views
2K
  • · Replies 23 ·
Replies
23
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 1 ·
Replies
1
Views
4K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 12 ·
Replies
12
Views
5K
  • · Replies 8 ·
Replies
8
Views
3K