Andrew Thangaraj
Aug-Nov 2020
\(A\): \(m\times n\) matrix over \(F\)
represents a linear map \(T:F^n\to F^m\)
column space is range \(T\), subspace of \(F^m\)
what about the rows?
rowspace \(A=\) span\(\{\)rows of \(A\}\) is a subspace of \(F^n\).
rank or column rank \(A=\) dim colspace \(A\)
what about dim rowspace \(A\)?
Let \(A\) be an \(m\times n\) matrix. Then, dim rowspace \(A\) \(=\) dim colspace \(A\). Popularly known as: row rank equals column rank
Proof
Suppose column rank is \(r\). Let colspace \(A=\) span\(\{v_1,\ldots,v_r\}\)
Write \(j\)-th column of \(A\) as \(c_{1j}v_1+\cdots+c_{rj}v_r\)
\[A=\begin{bmatrix} \vdots&\cdots&\vdots\\ v_1&\cdots&v_r\\ \vdots&\cdots&\vdots \end{bmatrix}\begin{bmatrix} c_{11}&\cdots&c_{1n}\\ \vdots&\cdots&\vdots\\ c_{r1}&\cdots&c_{rn} \end{bmatrix}\]
Each row of \(A\) is a linear combination of \(r\) vectors; so, row rank \(A\le r=\) col rank \(A\)
Use above result for \(A^T\): row rank \(A^T\le\) col rank \(A^T\), or col rank \(A\le\) row rank \(A\)
Square, \(m=n\)
Non-square, \(m\ne n\)
\(T:V\to W\) linear map with rank \(T=\) dim range \(T\). Suppose \(S:V\to V\) and \(U:W\to W\) are invertible operators. Then,
rank \(T=\) rank \(TS=\) rank \(UT=\) rank \(UTS\)
\(A:m\times n\) matrix, \(B:n\times n\) invertible matrix, \(C:m\times m\) invertible matrix.
rank \(A=\) rank \(AB=\) rank \(CA=\) rank \(CAB\)
Elementary row operations do not change the row space
Elementary row operations do not change the rank
Elementary row operations can change the column space
For any matrix \(A\), there are elementary row operations \(E_i\) such that \[\left(\prod_i E_i\right)A=\begin{bmatrix} 0&\cdots&0&1&\ast&\cdots&\ast&\ast&\ast&\cdots&\ast&\ast&\cdots&\cdots\\ 0&\cdots&0&0& 0 &\cdots& 0 & 1 &\ast&\cdots&\ast&\ast&\cdots&\cdots\\ 0&\cdots&0&0& 0 &\cdots& 0 & 0 & 0 &\cdots& 0 & 1 &\cdots&\cdots\\ 0&\cdots&0&0& 0 &\cdots& 0 & 0 & 0 &\cdots& 0 & 0 &\cdots&\cdots\\ \vdots&\vdots&\vdots&\vdots& \vdots &\vdots& \vdots & \vdots & \vdots &\vdots& \vdots & \vdots &\vdots &\vdots\\ \end{bmatrix}\]
rank \(A\) \(=\) number of nonzero pivots
dim null \(A=n-\)rank \(A\)
Basis of null \(A\): found by solving \(Ax=0\) using above form
Let \(A\) be an \(m\times n\) matrix. There are elementary row operations \(E_i\) and column operations \(F_j\) such that \[\left(\prod_i E_i\right)A\left(\prod_j F_j\right)=\begin{bmatrix} 1&0&\cdots&0&0&\cdots&0\\ 0&1&\cdots&0&0&\cdots&0\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&1&0&\cdots&0\\ 0&0&\cdots&0&0&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&0&0&\cdots&0\\ \end{bmatrix},\] where the number of 1s equals the rank of \(A\).
Elementary row operations: get upper triangular form
Do column swaps to get nonzero pivots together
Do column operations to make values above and right of pivots zero
Let \(A\) be an \(m\times n\) matrix. Then, left null \(A=\{x\in F^m: xA=0\}\).
left null \(A=\) null \(A^T\)
Fundamental theorem on \(A\) \[n=\text{col rank }A+\text{nullity }A\]
Fundamental theorem on \(A^T\) \[m=\text{row rank }A+\text{left nullity }A\]
Since row rank equals column rank… \[n=\text{row rank }A+\text{nullity }A\]
row space \(A\), null \(A\) are subspaces of \(F^n\)
If \(F=\mathbb{R}\), the intersection of row space \(A\) and null \(A\) is equal to \(\{0\}\).
Proof
Suppose \(v=(v_1,\ldots,v_n)\in\) row space \(A\). Then, there exists \(x=(x_1,\ldots,x_m)\) such that \[v=xA.\]
Multiply both sides by \(v^T\). \[vv^T=xAv^T=0\]
What about \(F\) being the field of complex numbers? What is going on with \(A^T\)? Answers later…