Dimension of the null space of A transpose

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SUMMARY

The discussion centers on finding the dimension of the null space of the transpose of a matrix A that is already in Reduced Row Echelon Form (RREF). It is established that one does not need to RREF the transpose to determine the null space dimension, as it can be derived from the rank of the original matrix. The rank of matrix A, denoted as r, directly informs the rank of its transpose, which is also r. Consequently, the dimension of the null space of the transpose is calculated as n - r, where n is the number of columns in the original matrix.

PREREQUISITES
  • Understanding of matrix rank and its implications
  • Familiarity with Reduced Row Echelon Form (RREF)
  • Knowledge of null space and its relationship to rank
  • Concept of orthogonal complements in linear algebra
NEXT STEPS
  • Study the relationship between rank and nullity in linear algebra
  • Learn about orthogonal complements and their applications
  • Explore the properties of transposes of matrices in RREF
  • Investigate the implications of the Rank-Nullity Theorem
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Students and professionals in mathematics, particularly those studying linear algebra, as well as educators teaching concepts related to matrix theory and its applications.

leroyjenkens
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So I'm given a matrix A that is already in RREF and I'm supposed to find the null space of its transpose.
So I transpose it. Do I RREF the transpose of it? Because if I transpose a matrix that's already in RREF, it's no longer in RREF. But if I RREF the transpose, it gives me a matrix with 2 leading entries that are both equal to zero. I don't think I'm doing it right.

Thanks.
 
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you don't need to do anything to find the dimension of the nullspace of the transpose if you already understand the rank of the matrix, since the nullspace of the transpose is the orthogonal complement of the range of the matrix. so if an nbym matrix represents a map R^m-->R^n of rank r, then the range has dimension r, so its orthocomplement has dimension n-r.

Equivalently, the rank of the transpose is also r. I.e. RREF shows you that both the row rank and the column rank are equal. The rank of a matrix is by definition the column rank, but the columns of the transpose equal the rows of the original matrix.

I.e. just knowing the rank of the original matrix, gives you also the rank of the transpose and hence also its nullity.
 

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