POTW Comparing Rank and Trace of a Matrix

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The discussion focuses on proving the inequality that relates the rank of a nonzero complex n x n matrix M to its trace and the trace of its conjugate transpose. Specifically, it establishes that the rank of M is at least the square of the absolute value of the trace of M divided by the trace of M's conjugate product. Participants explore the implications of this inequality and discuss conditions under which equality holds. The conversation emphasizes the mathematical foundations and potential applications of this relationship in linear algebra. Understanding these concepts is crucial for deeper insights into matrix theory and its applications.
Euge
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Let ##M## be a nonzero complex ##n\times n##-matrix. Prove $$\operatorname{rank}M \ge |\operatorname{trace} M|^2/\operatorname{trace}(M^\dagger M)$$ What is a necessary and sufficient condition for equality?
 
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Consider the inner product ##\langle A,B\rangle=\text{tr}(B^*A)## on the space of ##n\times n## complex matrices.

Let ##P## be the projection matrix onto the column space of ##M.## Note that ##P^*=P## and ##PM=M.##

Then, from Cauchy-Schwarz,

##|\text{tr}(M)|^2=|\text{tr}(PM)|^2=|\langle M,P\rangle|^2 \leq \langle M,M\rangle \langle P,P\rangle=\text{tr}(M^*M) \text{rank}(M).##

Dividing both sides by ##\text{tr}(M^*M)## proves the inequality.

Equality in Cauchy Schwarz occurs when ##M## and ##P## are dependent, i.e. ##M## is a multiple of a projection matrix (which I think should be equivalent to saying that it is diagonalizable, all its nonzero eigenvalues are equal, and its nullspace is orthogonal to its columnspace).
 
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