TITLE: Relation between linear transformations and traces
QUESTION [5 upvotes]: a) Let $A \in M_n (K)$. We denote $f_A$ the linear form defined, for every $X \in M_n (K)$, by $f_A(X)=Tr(AX)$. Show that the function $f$ which maps $A \in M_n (K)$ to $f_A$ is an isomorphism between $M_n (K)$ and its dual.
b) Let $f: M_n (K) \rightarrow K$ be a linear form such that, for every $(X,Y)$ in $M_n (K)^2$, $f(XY)=f(YX)$. Show that there exists $\lambda \in K$ such that for every $X \in M_n (K)$, $f(X)=\lambda Tr(X)$
That is what I have:
a) Let $(E_{ij})_{1\leq i,j \leq n}$ be the standard basis for $M_n (K)$. Let us start by showing that for every $1 \leq i,j,k,l \leq n$, we have $E_{ij} E_{kl}= \delta_{jk} E_{il}$
We have $E_{ij}=(\delta_{pi} \delta_{qj})_{1 \leq p,q \leq n}$ and $E_{kl}=(\delta_{pk} \delta_{ql})_{1 \leq p,q \leq n}$
$A= E_{ij} E_{kl}= (a_{p,q})$ such that:
$a_{p,q}= \sum \limits_{r=1}^n (\delta_{pi} \delta_{rj})(\delta_{rk} \delta_{ql})=( \sum \limits_{r=1}^n \delta_{rj} \delta_{rk})\delta_{pi} \delta_{ql}= \delta_{jk} \delta_{pi} \delta_{ql}$
Hence: $E_{ij} E_{kl}= \delta_{jk} E_{il}$
$$\begin{array}{c}\\\\\end{array}$$
It is obvious that $f$ is linear. Therefore, for dimensional reasons, we must only show that $f$ is injective. Let $A= (a_{ij})_{1\leq i,j \leq n}$ such that $f_A=0$. We therefore have, for $1 \leq i_0, j_0 \leq n$
$0= Tr(AE_{i_0 j_0})= Tr(\sum \limits_{1 \leq i,j \leq n} a_{ij}E_{ij}E{i_0 j_0})= Tr(\sum \limits_{i=1}^n a_{i i_{0}}E_{i i_0} E_{i_0 j_0})= \sum \limits_{i=1}^n a_{i i_0} Tr(E_{i j_0})=a_{j_{0} i_{0}}$
Hence $A$ equals zero.
Therefore, $f_A$ is an isomorphism.
$$\begin{array}{c}\\\\\end{array}$$
Can someone help me with b?

REPLY [3 votes]: b) By assumption, $f$ is a linear form so by a) we have $A$ such that $f=f_A$.
Then for every $i\neq j$:
$$
a_{ji}=\mbox{Tr}(AE_{ij})=\mbox{Tr}(AE_{ii}E_{ij})=f_A(E_{ii}E_{ij})=f_A(E_{ij}E_{ii})=f_A(0)=0
$$
and 
$$
a_{ii}=\mbox{Tr}(AE_{ii})=f_A(E_{ii})=f_A(E_{ij}E_{ji})=f_A(E_{ji}E_{ij})=f_A(E_{jj})=\mbox{Tr}(AE_{jj})=a_{jj}.
$$
Hence $A$ is a scalar matrix, which yields the result.