Proving that cond(ATA) = cond(A)^2

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SUMMARY

The discussion centers on proving the relationship between the condition number of a matrix \(A\) and its transpose, specifically that \(\kappa(A^TA) = \kappa(A)^2\). The condition number is defined as \(\kappa(A) = \|A\|\|A^{-1}\|\) or \(\kappa(A) = \|A\|\|A^+\|\) where \(A^+\) is the pseudoinverse of \(A\). The solution involves utilizing the properties of singular values, demonstrating that the singular values of \(A^TA\) are the squares of the singular values of \(A\). This leads to the conclusion that the condition number of \(A^TA\) is indeed the square of the condition number of \(A\).

PREREQUISITES
  • Understanding of condition numbers, specifically \(\kappa(A) = \|A\|\|A^{-1}\|\)
  • Familiarity with matrix norms and their properties
  • Knowledge of pseudoinverses, particularly \(A^+\)
  • Basic concepts of singular value decomposition (SVD)
NEXT STEPS
  • Study the properties of singular values and their relationship to matrix condition numbers
  • Learn about the implications of condition numbers in numerical analysis
  • Explore the concept of pseudoinverses and their applications in solving linear systems
  • Investigate the Cauchy-Schwarz inequality as it applies to matrix norms
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Mathematicians, data scientists, and engineers involved in numerical linear algebra, particularly those working with matrix computations and condition number analysis.

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Homework Statement


Prove that [tex]\kappa(A^TA) = \kappa(A)^2[/tex] where [tex]\kappa(A) = \left\|A\right\|\left\|A^{-1}\right\|[/tex], or in a more general case, [tex]\kappa(A) = \left\|A\right\|\left\|A^+\right\|[/tex], where [tex]A^+[/tex] is the pseudoinverse of [tex]A\in\mathbb{R}^{m\times n}[/tex]

The Attempt at a Solution



My initial guess is to use Cauchy-Schwarz to separate the products of the norms when replacing [tex]A[/tex] with [tex]A^TA[/tex], giving me

[tex]\kappa(A^TA) = \left\|A^TA\right\|\left\|(A^TA)^+\right\| \leq \left\|A^T\right\|\left\|A\right\|\left\|A^+\right\|\left\|\left(A^T\right)^+\right\| = \underbrace{\left\|A\right\|\left\|A^+\right\|}_{\kappa(A)}\underbrace{\left\|A^T\right\|\left\|\left(A^T\right)^+\right\|}_{\kappa(A^T)}[/tex]

which gives [tex]\kappa(A)^2[/tex] as [tex]\kappa(A) = \kappa(A^T)[/tex].

But I'm not sure if I can do it this simple; any inputs?

Thanks in advance
 
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Figured it out - used the definition of the singular values of A, and then prooved that the singular values of ATA are the squared singular values of A.
 

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