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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 i≠j.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(1−λ)=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+∑i≠j(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 x∈ker⁡(A)x \in \ker(A) so it suffices to prove the same for x∈im⁡(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 i≠ji \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 vi∈Viv_i \in V_i and w.l.o.g. v1≠0v_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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