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IMC / 2022 / Problems / Day 2, P7

IMC 2022 · Day 2 · P7

hard

Let A1,A2,,AkA_1, A_2, \dots, A_k be n×nn \times n idempotent complex matrices such that AiAj=AjAifor all ij.A_i A_j = -A_j A_i \quad \text{for all } i \ne j. Prove that at least one of the given matrices has rank nk\le \dfrac{n}{k}.

(A matrix AA is called idempotent if A2=AA^2 = A.)

(proposed by Danila Belousov, Novosibirsk)

Solution 1 of 2 (official)

Hint: Consider the trace and the rank of AA.

Lemma. For any idempotent matrix BB tr(B)=rank(B)\operatorname{tr}(B) = \operatorname{rank}(B) Proof. Observe that an idempotent matrix satisfies the equation λ(1λ)=0\lambda(1 - \lambda) = 0. Hence the minimal polynomial is a product of linear factors and the matrix is diagonalizable. Therefore, the rank of the matrix equals the number of non-zero eigenvalues. Since the matrix has eigenvalues 0 or 1, this provides that the trace is equal to the number of unity eigenvalues, or non-zero eigenvalues.

It can be shown that i=1kAi\sum\limits_{i=1}^{k} A_i is also an idempotent. Indeed, (i=1kAi)2=i=1kAi2+ij(AiAj+AjAi)=i=1kAi\left( \sum_{i=1}^{k} A_i \right)^2 = \sum_{i=1}^{k} A_i^2 + \sum_{i \ne j} (A_i A_j + A_j A_i) = \sum_{i=1}^{k} A_i Applying the lemma one can obtain i=1krank(Ai)=i=1ktr(Ai)=tr(i=1kAi)=rank(i=1kAi)n\sum_{i=1}^{k} \operatorname{rank}(A_i) = \sum_{i=1}^{k} \operatorname{tr}(A_i) = \operatorname{tr} \left( \sum_{i=1}^{k} A_i \right) = \operatorname{rank} \left( \sum_{i=1}^{k} A_i \right) \leqslant n The required inequality follows.

Solution 2 of 2 (official)

We first prove that for idempotents A,BA, B with AB=BAAB = -BA we already must have AB=BA=0AB = BA = 0. Indeed, it is clear that ABx=BAx=0ABx = BAx = 0 for xker(A)x \in \ker(A) so it suffices to prove the same for xim(A)x \in \operatorname{im}(A), i.e. when Ax=xAx = x. But then writing Bx=yBx = y we have Ay=yAy = -y i.e. y=Ay=A2y=Ay=yy = -Ay = -A^2 y = Ay = -y and hence y=0y = 0 so that again ABx=BAx=0ABx = BAx = 0.

Henceforth, we can assume the stronger condition AiAj=0A_i A_j = 0 for all iji \ne j. We next claim that all the image spaces ViV_i of AiA_i are linearly independent. This will imply the claim, since then the sum of their dimensions can be at most nn, and so one of them has to be nk\le \frac{n}{k}. Now, for the sake of contradiction, suppose that vi=0\sum v_i = 0 with viViv_i \in V_i and w.l.o.g. v10v_1 \ne 0. But then 0=A1(v1++vk)=v1+A1v2++A1vk=v1+A1A2v2++A1Akvk=v10 = A_1 (v_1 + \dots + v_k) = v_1 + A_1 v_2 + \dots + A_1 v_k = v_1 + A_1 A_2 v_2 + \dots + A_1 A_k v_k = v_1 since A1Ai=0A_1 A_i = 0 for all ii.

Remark. Here is a different argument for AB=BA=0AB = BA = 0, without eigenvectors: multiplying by AA and using its idempotence and the super-commutativity, we have BA=AB=A2B=AAB=ABA=BAA=BA2=BA-BA = AB = A^2 B = AAB = -ABA = BAA = BA^2 = BA thus BA=0BA = 0.

How the field did

contestants scored
589
average (of 10)
3.88
solved (≥ 80%)
28.4%
near-0 (≤ 10%)
42.4%
discrimination
0.53

Score distribution (field cohort)

Computed on contestants with a meaningful total (field cohort); discrimination is the corrected item–total correlation.

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