Transpose of Matrix as Linear Map

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SUMMARY

The discussion focuses on the relationships between a matrix H and its transpose H^T as linear maps, specifically in the context of their kernels and images. It establishes that the row rank equals the column rank, indicating a fundamental property of linear transformations. The conversation highlights that the images and kernels of H and H^T are intrinsically linked to the rows and columns of H, emphasizing the transpose's role as a pullback map on linear functions. Additionally, it explores the implications of the compositions HH^T and H^TH in relation to their respective dimensions.

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  • Understanding of linear algebra concepts, particularly linear maps
  • Familiarity with matrix operations and properties
  • Knowledge of kernel and image in the context of linear transformations
  • Basic comprehension of the transpose operation in matrices
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chingkui
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What are the relations between a matrix H and its transpose H^T? I am not asking about the relations between the coefficients, I am asking the relations as linear maps (H: F^m->F^n; H^T: F^n->F^m). I am not sure exactly how I should pose the question actually, but I am thinking there is some deeper relations than between their coefficients, like for example, can we say something about the kernel and image of H^T if we know something about the kernel and image of H? What can we say about HH^T: F^n->F^n and H^TH: F^m->F^m?
(F is an arbitrary field)
 
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row rank=column rank, that pretty much tells you everything, and the fact that the images and kernels are related to the rows and cols of H's implies soemthing obvious since the row/cols of the transpose are the cols/rows of the original. (i haven't said which way round since it depends on what side you make your matrices act)
 
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the transpose represents the pullback map on linear functions.
 

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