# Column Space and SVD

I was reading Gilbert Strang's book and he says that if $A=USV'$ be the SVD of A ( assume square for the moment) then the nullspace of A is given by the last $n-r$ columns of V and the column space by the first $r$ columns of $U$.

I understand the null space part. If \begin{align} A&=USV'\\ AV&=US\\ [Av_1 \;Av_2\cdots Av_r\cdots Av_n]&=[Us_1 \;Us_2\cdots Us_r\cdots Us_n] \end{align} The columns $Us_{r+1}\cdots Us_n$ are $0$ vectors and correspond to (applicatoin of A on) linearly independent vectors $v_{r+1}\cdots v_n$. Thus, vectors $v_{r+1}\cdots v_n$ form the null space of A.

How do I prove the statement on column space? I am not even able to start. This is not homework by the way. Just wanted to prove everything in Strang myself for fun.

I feel it has to be inclusion proof where I show that any vector in the column space of A is in the column space of $\hat{U}$ and vice versa. ($\hat{U}$ is first r columns of $U$)

Since $A = USV'$, then its column space must be the same as the column space of $US$, since $V$ is invertible. And $S$ is a diagonal matrix and only the first $r$ diagonal entries of $S$ are nonzero, so check that only the first $r$ columns of $U$ "survive" being multiplied by $S$.
• If $A = BC$, then the column space of $A$ must be a subset of the column space of $B$, since the matrix $BC$ is literally a matrix whose columns are some linear combinations of the columns of $B$. If $C$ is also invertible, then also $AC^{-1} = B$, which implies the column space of $B$ is a subset of the column space of $A$. Thus both inclusions hold so they have equal column space. Now set $B = US$ and $C = V'$. – Christopher A. Wong Mar 14 '15 at 2:25
in fact you can write as the sum of $r$ rank one matrices $$A = s_1u_1v_1^T + s_2u_2v_2^T + \cdots+s_ru_rv_r^T$$ where $u_1,u_2,\ldots, u_r$ and $v_1,v_2,\ldots, v_r$ make up the columns of the orthogonal matrices $U,V$ and $r$ is the rank of $A.$ to show that $u_1,\ldots, u_r$ spans the column space of $A,$ look at $Ax.$ $$Ax = s_1(v_1^T x) u_1 + s_2(v_2^Tx)u_2 + \cdots+s_r(v_r^Tx)u_r$$
you can look at the row space of $A$ by computing $y^TA$ in the same way.
• If we see the matrix $\mathbf{A}_{n\times m}(\mathbb{R})$ as a linear operator $T(\mathbf{x})=\mathbf{Ax}$, then $C(\mathbf{A})= \{ T(\mathbf{x})|\mathbf{x}\in \mathbb{R}^n \}$. Is it your point that for each $\mathbf{x}\in\mathbb{R}^n$ $T(\mathbf{x})$ can be written a combination of the $r$ columns of $\mathbf{U}$ and thus belong to $C(\mathbf{U})$? Thanks. – darkmoor Nov 13 '17 at 20:04
Note that $Us_i = \sigma_i u_i$ because $S$ is diagonal where $\sigma_i$ is the $i$th singular value and $u_i$ is the $i$th column of $U$. So $A v_i = \sigma_i u_i$, $i=1,2,\dots,r$ spans the column space of $A$.