I was reading Gilbert Strang's book for Linear Algebra (along with his lectures) and I feel that he is emphasizing that the 4 fundamental subspaces (Column Space, Row Space, Null Space and Null Space of $A^T$) form the "crux" or "heart" of Linear Algebra and that the understanding of it is crucial for further study.

But what I don't understand is WHY? Why are they so important?

I understand that :

  • Row Space and Null Space are orthogonal. (Same for other two)
  • Their dimensions are governed by rank of the matrix.

but beyond that I didn't find anything that I found profound or interesting. Am I missing something or is Prof. Strang over-rating the 4 fundamental subspaces?

  • 8
    $\begingroup$ The short answer is that it becomes clearer the more linear algebra you learn, especially when you start applying it to do other things. I am not convinced a one-paragraph or even one-page summary of various ideas that build upon these ideas will be useful until you start tackling them yourself. Perhaps it's a good idea to simply have faith that Gilbert Strang is a very smart man who knows what he's talking about. $\endgroup$ May 29, 2012 at 20:48
  • 2
    $\begingroup$ The first bullet point is not correct. $\endgroup$
    – timur
    May 29, 2012 at 22:11
  • $\begingroup$ @QiaochuYuan. I will. Thanks ! $\endgroup$ May 30, 2012 at 15:03

1 Answer 1


The core of studying matrices is to study linear transformations between vector spaces. These can be realized as matrix multiplication on the left (or right) of column (or row) vectors.

If we are in this setup: $x\mapsto Ax$ for a column vector $x$ and appropriate matrix $A$, then the image of the linear transformation will be spanned by the columns of $A$.

The kernel of the transformation (nullspace) is the set of all $x$ such that $Ax=0$ is important for understanding the solutions to some matrix equations. You probably have already learned that if $x_0$ is a solution to $Ax=b$, then every other solution is given by $x_0+k$ where $k$ is in the nullspace.

This all has analogous explanation on the other side. If we are in this setup: $x\mapsto xA$ for a row vector $x$, then the image of the linear transformation is now spanned by the rows of $A$.

Talking about the nullspace of $A^T$ is just a fancy way of dressing up the "left nullspace" of $A$, since $xA=0$ iff $A^T x^T=0$. The nullspace is now the set of all $x$ such that $xA=0$, and you can draw the same conclusions about solutions to $xA=b$.

In short, these four spaces (really just two spaces, with a left and a right version of the pair) carry all the information about the image and kernel of the linear transformation that $A$ is affecting, whether you are using it on the right or on the left.


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