Unofficial archive — problems, solutions & results © IMC, reproduced with permission.

IMC / 2009 / Problems / Day 2, P10

IMC 2009 · Day 2 · P10

killer

Let MM be the vector space of m×pm \times p real matrices. For a vector subspace SMS \subseteq M, denote by δ(S)\delta(S) the dimension of the vector space generated by all columns of all matrices in SS.

Say that a vector subspace TMT \subseteq M is a covering matrix space if AT,A0kerA=Rp.\bigcup_{A \in T, A \ne 0} \ker A = \mathbb{R}^p. Such a TT is minimal if it does not contain a proper vector subspace STS \subset T which is also a covering matrix space.

(a) (8 points) Let TT be a minimal covering matrix space and let n=dimTn = \dim T. Prove that δ(T)(n2).\delta(T) \le \binom{n}{2}.

(b) (2 points) Prove that for every positive integer nn we can find mm and pp, and a minimal covering matrix space TT as above such that dimT=n\dim T = n and δ(T)=(n2)\delta(T) = \binom{n}{2}.

Solution 1 of 2 (official)

(a) We will prove the claim by constructing a suitable decomposition T=Z0Z1T = Z_0 \oplus Z_1 \oplus \cdots and a corresponding decomposition of the space spanned by all columns of TT as W0W1W_0 \oplus W_1 \oplus \cdots, such that dimW0n1\dim W_0 \leqslant n - 1, dimW1n2\dim W_1 \leqslant n - 2, etc., from which the bound follows.

We first claim that, in every covering matrix space SS, we can find an ASA \in S with rkAdimS1\operatorname{rk} A \leqslant \dim S - 1. Indeed, let S0SS_0 \subseteq S be some minimal covering matrix space. Let s=dimS0s = \dim S_0 and fix some subspace SS0S' \subset S_0 of dimension s1s - 1. SS' is not covering by minimality of S0S_0, so that we can find an uRpu \in \mathbb{R}^p with uBS,B0KerBu \notin \bigcup_{B \in S', B \ne 0} \operatorname{Ker} B. Let V=S(u)V = S'(u); by the rank-nullity theorem, dimV=s1\dim V = s - 1. On the other hand, as S0S_0 is covering, we have that Au=0Au = 0 for some AS0SA \in S_0 \setminus S'. We claim that ImAV\operatorname{Im} A \subset V (and therefore rk(A)s1\operatorname{rk}(A) \leqslant s - 1).

For suppose that AvVAv \notin V for some vRpv \in \mathbb{R}^p. For every αR\alpha \in \mathbb{R}, consider the map fα:S0Rmf_\alpha : S_0 \to \mathbb{R}^m defined by fα:(τ+βA)τ(u+αv)+βAvf_\alpha : (\tau + \beta A) \mapsto \tau(u + \alpha v) + \beta A v, τS\tau \in S', βR\beta \in \mathbb{R}. Note that f0f_0 is of rank s=dimS0s = \dim S_0 by our assumption, so that some s×ss \times s minor of the matrix of f0f_0 is non-zero. The corresponding minor of fαf_\alpha is thus a nonzero polynomial of α\alpha, so that it follows that rkfα=s\operatorname{rk} f_\alpha = s for all but finitely many α\alpha. For such an α0\alpha \ne 0, we have that Kerfα={0}\operatorname{Ker} f_\alpha = \{0\} and thus 0τ(u+αv)+βAv=(τ+α1βA)(u+αv)0 \ne \tau(u + \alpha v) + \beta A v = (\tau + \alpha^{-1} \beta A)(u + \alpha v) for all τS\tau \in S', βR\beta \in \mathbb{R} not both zero, so that B(u+αv)0B(u + \alpha v) \ne 0 for all nonzero BS0B \in S_0, a contradiction.

Let now TT be a minimal covering matrix space, and write dimT=n\dim T = n. We have shown that we can find an ATA \in T such that W0=ImAW_0 = \operatorname{Im} A satisfies w0=dimW0n1w_0 = \dim W_0 \leqslant n - 1. Denote Z0={BT:ImBW0}Z_0 = \{ B \in T : \operatorname{Im} B \subset W_0 \}; we know that t0=dimZ01t_0 = \dim Z_0 \geqslant 1. If T=Z0T = Z_0, then δ(T)n1\delta(T) \leqslant n - 1 and we are done. Else, write T=Z0T1T = Z_0 \oplus T_1, also write Rm=W0V1\mathbb{R}^m = W_0 \oplus V_1 and let π1:RmRm\pi_1 : \mathbb{R}^m \to \mathbb{R}^m be the projection onto the V1V_1-component. We claim that T1={π1τ1:τ1T1}T_1^{\sharp} = \{ \pi_1 \tau_1 : \tau_1 \in T_1 \} is also a covering matrix space. Note here that π1:T1T1\pi_1^{\sharp} : T_1 \to T_1^{\sharp}, τ1(π1τ1)\tau_1 \mapsto (\pi_1 \tau_1) is an isomorphism. In particular we note that δ(T)=w0+δ(T1)\delta(T) = w_0 + \delta(T_1^{\sharp}).

Suppose that T1T_1^{\sharp} is not a covering matrix space, so we can find a v1Rpv_1 \in \mathbb{R}^p with v1τ1T1,τ10Ker(π1τ1)v_1 \notin \bigcup_{\tau_1 \in T_1, \tau_1 \ne 0} \operatorname{Ker}(\pi_1 \tau_1). On the other hand, by minimality of TT we can find a u1Rpu_1 \in \mathbb{R}^p with u1τ0Z0,τ00Kerτ0u_1 \notin \bigcup_{\tau_0 \in Z_0, \tau_0 \ne 0} \operatorname{Ker} \tau_0. The maps gα:Z0V0g_\alpha : Z_0 \to V_0, τ0τ0(u1+αv1)\tau_0 \mapsto \tau_0 (u_1 + \alpha v_1) and hβ:T1V1h_\beta : T_1 \to V_1, τ1π1(τ1(v1+βu1))\tau_1 \mapsto \pi_1 (\tau_1 (v_1 + \beta u_1)) have rkg0=t0\operatorname{rk} g_0 = t_0 and rkh0=nt0\operatorname{rk} h_0 = n - t_0 and thus both rkgα=t0\operatorname{rk} g_\alpha = t_0 and rkhα1=nt0\operatorname{rk} h_{\alpha^{-1}} = n - t_0 for all but finitely many α0\alpha \ne 0 by the same argument as above. Pick such an α\alpha and suppose that (τ0+τ1)(u1+αv1)=0(\tau_0 + \tau_1)(u_1 + \alpha v_1) = 0 for some τ0Z0\tau_0 \in Z_0, τ1T1\tau_1 \in T_1. Applying π1\pi_1 to both sides we see that we can only have τ1=0\tau_1 = 0, and then τ0=0\tau_0 = 0 as well, a contradiction given that TT is a covering matrix space.

In fact, the exact same proof shows that, in general, if TT is a minimal covering matrix space, Rm=V0V1\mathbb{R}^m = V_0 \oplus V_1, T0={τT:ImτV0}T_0 = \{ \tau \in T : \operatorname{Im} \tau \subset V_0 \}, T=T0T1T = T_0 \oplus T_1, π1:RmRm\pi_1 : \mathbb{R}^m \to \mathbb{R}^m is the projection onto the V1V_1-component, and T1={π1τ1:τ1T1}T_1^{\sharp} = \{ \pi_1 \tau_1 : \tau_1 \in T_1 \}, then T1T_1^{\sharp} is a covering matrix space.

We can now repeat the process. We choose a π1A1T1\pi_1 A_1 \in T_1^{\sharp} such that W1=(π1A1)(Rp)W_1 = (\pi_1 A_1)(\mathbb{R}^p) has w1=dimW1nt01n2w_1 = \dim W_1 \leqslant n - t_0 - 1 \leqslant n - 2. We write Z1={τ1T1:Im(π1τ1)W1}Z_1 = \{ \tau_1 \in T_1 : \operatorname{Im}(\pi_1 \tau_1) \subset W_1 \}, T1=Z1T2T_1 = Z_1 \oplus T_2 (and so T=(Z0Z1)T2T = (Z_0 \oplus Z_1) \oplus T_2), t1=dimZ11t_1 = \dim Z_1 \geqslant 1, V1=W1V2V_1 = W_1 \oplus V_2 (and so Rm=(W0W1)V2\mathbb{R}^m = (W_0 \oplus W_1) \oplus V_2), π2:RmRm\pi_2 : \mathbb{R}^m \to \mathbb{R}^m is the projection onto the V2V_2-component, and T2={π2τ2:τ2T2}T_2^{\sharp} = \{ \pi_2 \tau_2 : \tau_2 \in T_2 \}, so that T2T_2^{\sharp} is also a covering matrix space, etc.

We conclude that δ(T)=w0+δ(T1)=w0+w1+δ(T2)=(n1)+(n2)+(n2).\delta(T) = w_0 + \delta(T_1^{\sharp}) = w_0 + w_1 + \delta(T_2^{\sharp}) = \cdots \leqslant (n-1) + (n-2) + \cdots \leqslant \binom{n}{2}.

(b) We consider (n2)×n\binom{n}{2} \times n matrices whose rows are indexed by (n2)\binom{n}{2} pairs (i,j)(i, j) of integers 1i<jn1 \leqslant i < j \leqslant n. For every u=(u1,u2,,un)Rnu = (u_1, u_2, \dots, u_n) \in \mathbb{R}^n, consider the matrix A(u)A(u) whose entries (A(u))(i,j),k(A(u))_{(i,j),k} with 1i<jn1 \leqslant i < j \leqslant n and 1kn1 \leqslant k \leqslant n are given by (A(u))(i,j),k={ui,k=j,uj,k=i,0,otherwise.(A(u))_{(i,j),k} = \begin{cases} u_i, & k = j, \\ -u_j, & k = i, \\ 0, & \text{otherwise.} \end{cases} It is immediate that KerA(u)=Ru\operatorname{Ker} A(u) = \mathbb{R} \cdot u for every u0u \ne 0, so that S={A(u):uRn}S = \{ A(u) : u \in \mathbb{R}^n \} is a covering matrix space, and in fact a minimal one.

On the other hand, for any 1i<jn1 \leqslant i < j \leqslant n, we have that A(ei)(i,j),jA(e_i)_{(i,j),j} is the (i,j)(i,j)-th vector in the standard basis of R(n2)\mathbb{R}^{\binom{n}{2}}, where eie_i denotes the ii-th vector in the standard basis of Rn\mathbb{R}^n. This means that δ(S)=(n2)\delta(S) = \binom{n}{2}, as required.

Solution 2 of 2 (official)

(for part a)

Let us denote X=RpX = \mathbb{R}^p, Y=RmY = \mathbb{R}^m. For each xXx \in X, denote by μx:TY\mu_x : T \to Y the evaluation map ττ(x)\tau \mapsto \tau(x). As TT is a covering matrix space, kerμx>0\ker \mu_x > 0 for every xXx \in X. Let U={xX:dimkerμx=1}U = \{ x \in X : \dim \ker \mu_x = 1 \}.

Let T1T_1 be the span of the family of subspaces {kerμx:xU}\{ \ker \mu_x : x \in U \}. We claim that T1=TT_1 = T. For suppose the contrary, and let TTT' \subset T be a subspace of TT of dimension n1n - 1 such that T1TT_1 \subseteq T'. This implies that TT' is a covering matrix space. Indeed, for xUx \in U, (kerμx)T=kerμx0(\ker \mu_x) \cap T' = \ker \mu_x \ne 0, while for xUx \notin U we have dimμx2\dim \mu_x \ge 2, so that (kerμx)T0(\ker \mu_x) \cap T' \ne 0 by computing dimensions. However, this is a contradiction as TT is minimal.

Now we may choose x1,x2,,xnUx_1, x_2, \dots, x_n \in U and τ1,τ2,,τnT\tau_1, \tau_2, \dots, \tau_n \in T in such a way that kerμxi=Rτi\ker \mu_{x_i} = \mathbb{R} \tau_i and τi\tau_i form a basis of TT. Let us complete x1,,xnx_1, \dots, x_n to a sequence x1,,xdx_1, \dots, x_d which spans XX. Put yij=τi(xj)y_{ij} = \tau_i(x_j). It is clear that yijy_{ij} span the vector space generated by the columns of all matrices in TT. We claim that the subset {yij:i>j}\{ y_{ij} : i > j \} is enough to span this space, which clearly implies that δ(T)(n2)\delta(T) \leqslant \binom{n}{2}.

We have yii=0y_{ii} = 0. So it is enough to show that every yijy_{ij} with i<ji < j can be expressed as a linear combination of ykiy_{ki}, k=1,,nk = 1, \dots, n. This follows from the following lemma:

Lemma. For every x0Ux_0 \in U, 0τ0kerμx00 \ne \tau_0 \in \ker \mu_{x_0} and xXx \in X, there exists a τT\tau \in T such that τ0(x)=τ(x0)\tau_0(x) = \tau(x_0).

Proof. The operator μx0\mu_{x_0} has rank n1n - 1, which implies that for small ε\varepsilon the operator μx0+εx\mu_{x_0 + \varepsilon x} also has rank n1n - 1. Therefore one can produce a rational function τ(ε)\tau(\varepsilon) with values in TT such that mx0+εx(τ(ε))=0m_{x_0 + \varepsilon x}(\tau(\varepsilon)) = 0.

Taking the derivative at ε=0\varepsilon = 0 gives μx0(τ0)+μx(τ(0))=0\mu_{x_0}(\tau_0) + \mu_x(\tau'(0)) = 0.

Therefore τ=τ(0)\tau = -\tau'(0) satisfies the desired property.

Remark. Lemma in solution 2 is the same as the claim ImAV\operatorname{Im} A \subset V at the beginning of solution 1, but the proof given here is different. It can be shown that all minimal covering spaces TT with dimT=(n2)\dim T = \binom{n}{2}

are essentially the ones described in our example.

How the field did

contestants scored
336
average (of 10)
0.04
solved (≥ 80%)
0.0%
near-0 (≤ 10%)
98.8%
discrimination
0.19

Score distribution (field cohort)

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

Similar problems

IMC 2021 · Day 2 · P8killeravg 0.0/10 · solved 0% · near-0 99% · disc 0.08
IMC 2006 · Day 2 · P12killeravg 0.0/10 · solved 0% · near-0 99% · disc 0.16
IMC 2004 · Day 2 · P12killeravg 0.2/10 · solved 1% · near-0 97% · disc 0.13
IMC 2016 · Day 2 · P10killeravg 0.1/10 · solved 1% · near-0 99% · disc 0.16