Re: Proof For Cov. Matrix equation?

*To*: mathgroup at smc.vnet.net*Subject*: [mg30005] Re: [mg29904] Proof For Cov. Matrix equation?*From*: Daniel Lichtblau <danl at wolfram.com>*Date*: Sat, 21 Jul 2001 00:49:12 -0400 (EDT)*References*: <200107180608.CAA18955@smc.vnet.net>*Sender*: owner-wri-mathgroup at wolfram.com

Robert Parry wrote: > > I know I've already posted this message to alt.math.recreational (5 days > ago) but to date I have had no reply. Could anyone help me with the > following derivation/proof? > > I'm looking for a short proof for the nxn version of the following problem. > I managed to prove it for the 2x2 case but ended up with a 3 page proof so > I'd hate to attempt the proof for larger matrices. > > Here's the 2x2 case: (For the nxn case x,y becomes x1,x2,...,xn etc.) > > Let x, y & r be 3 random variables (in this case timeseries) > now r = x*Vx + y*Vy is a weighted mean (where Vx + Vy = 1 - Vx & Vy are > weights) > Let the Covariance Matrices H and T be defined as > > H = [ var[x] covar[x,y]; covar[x,y] var[y] ] > (Historic Covariance) > > and T = [ var[x-r] covar[x-r,y-r]; covar[x-r,y-r] var[y-r] ] > (Tracking Error Covariance) > > Prove that > > [ Wx Wy ].T.[ Wx; Wy ] = [ Wx-Vx Wy-Vy ].H.[ Wx-Vx; Wy-Vy ] > where Wx + Wy = 1 > > (remember that: > covar[x,y] = covar[y,x] > covar[a*x1+b*x2,y] = a*covar[x1,y] + b*covar[x2,y] > > - i.e. symmetric and bilinear) > > Thanks in advance, > Robert This being a Mathematica-related news group, we'll warm up with some Mathematica code to automate the proofs for low dimension. First some notation. I use avec = {a[1],a[2],...} where you have {Vx, Vy}, bvec={b[1],b[2],...} where you have {Wx,Wy}, xvars={x[1],x[2],...} where you have {x,y} (or {x1,x2,...}. Thus your 'r' becomes avec.xvars. I use for the covariance matrix elements of the form v[x[i],x[j]] which in turn is abbreviated v[i,j]. Now we put in place rules that make the covariance matrix symmetric and furthermore make the function v[a,b] bilinear. v[i_Integer, j_Integer] /; j < i := v[j, i] v[x[i_Integer], x[j_Integer]] := v[i, j] v[s_*x[i_Integer], y_] /; Head[s] =!= x := s*v[x[i], y] v[y_, s_*x[i_Integer]] /; Head[s] =!= x := s*v[y, x[i]] v[a_ + b_, y_] := v[a, y] + v[b, y] v[y_, a_ + b_] := v[y, a] + v[y, b] With this we now form a function to demonstrate the identity given a specific dimension. We define local variables of the form noted above, form the "translated" variables transvars=x[j]-avec.xvars, form the matrix tmat by applying v to all pairs {transvars[i],transvars[j], and take the difference of the two dot products in question. At this point it suffices to simplify the expression subject to the relation b[1]+...+b[dim]==1. Note that this is the only restriction we need enforce on the vectors (so in particular we do not care about the sum of the a values). But we can make this much more efficient at higher dimensions if we first rewrite the difference as a polynomial with monomial variables consisting of the a[i], x[j], and v[m,n], and coefficients from the b[k]. We can then simplify each coefficient individually (much more efficient) and check that they all simplify to zero. Clearly this suffices; the relation between b[k]'s will not simplify the whole thing to zero unless it simplifies each such term to zero. Finally we output the leaf count of the difference and also whether the difference simplified to zero: True means our identity is proven. identityProof[dim_Integer] /; dim > 0 := Module[ {vmat, avec, bvec, xvars, transvars, tmat, diff, coeffs, rel}, vmat = Array[v, {dim, dim}]; avec = Array[a, {dim}]; bvec = Array[b, {dim}]; xvars = Array[x, {dim}]; transvars = xvars - avec.xvars; tmat = Outer[v, transvars, transvars]; diff = bvec.tmat.bvec - (bvec - avec).vmat.(bvec - avec); coeffs = Map[#[[2]] &, First[Internal`DistributedTermsList[diff, Join[avec, xvars, Flatten[vmat]]]]]; rel = Apply[Plus, bvec] == 1; {LeafCount[diff], Apply[And, Map[(Simplify[#, rel] == 0) &, coeffs]]} ] In[11]:= Table[Timing[identityProof[j]], {j,1,15}] Out[11]= {{0.04 Second, {46, True}}, {0.15 Second, {333, True}}, {0.23 Second, {1177, True}}, {0.81 Second, {3097, True}}, {1.61 Second, {6771, True}}, {2.9 Second, {13045, True}}, {4.74 Second, {22933, True}}, {7.54 Second, {37617, True}}, {11.32 Second, {58447, True}}, {16.82 Second, {86941, True}}, {24.37 Second, {124785, True}}, {33.59 Second, {173833, True}}, {46.19 Second, {236107, True}}, {61.27 Second, {313797, True}}, {82.49 Second, {409261, True}}} So a couple dozen lines of code and we can show the identity automatically given not-too-large dimension. Now for a general proof. Again, some notation. Your matrix H (I'll refer to it below as hmat) has entries H[[i,j]] equal to what I have called v[i,j]. Your T (what I called tmat in identityProof) has entries T[[i,j]] equal to v[x[i]-avec.xvars, x[j]-avec.xvars] This expands (using, more or less, Mathematica notation) as tmat[[i,j]] = v[i,j] - Sum[a[k]*(v[i,k]+v[k,j]),{k,dim}] + Sum[a[k]*a[m]*v[k,m],{k,dim},{m,dim}] Thus tmat may be written as hmat + mat2 + mat3 with mat2 and mat3 elements given by the second and third sums respectively. Now we look at the equation we are to show. We have on the left side bvec.tmat.bvec and on the right side (bvec-avec).hmat.(bvec-avec). The left side expands symbolically as bvec.hmat.bvec + bvec.mat2.bvec + bvec.mat3.bvec Note that I am using a Mathematica convention which, for purposes of dot products with matrices, treats vectors as row or column according to whichever makes sense (so on the left of a matrix it's a row vector, on the right it's a column vector). The right side expands as bvec.hmat.bvec - 2*avec.hmat.bvec + avec.hmat.avec. We will show that (i) bvec.mat3.bvec == avec.hmat.avec and (ii) bvec.mat2.bvec == -2*avec.hmat.bvec under the assumption that Sum[b[i],{i,dim}]==1 and this suffices to prove the identity First, avec.hmat.avec expands as Sum[a[k]*a[m]*v[k,m],{k,dim},{m,dim}] which is the same as each element of mat3 as defined above. We will call that sum s. Since all elements of mat3 are the same, mat3.bvec is simply a vector will all components given by s*Sum[b[j],{j,dim}]. Under our assumption this in turn is just s so the matrix times vector product gives svec=Table[s,{dim}]. Similarly bvec.svec = Sum[b[j],{j,dim}]*s = s again using our assumption. This finishes (i). We now split mat2 into mat2a and mat2b where mat2a[[i,j]] = -Sum[a[k]*v[i,k],{k,dim}] and mat2b[[i,j]] = -Sum[a[k]*v[k,j],{k,dim}] We see that mat2b=Transpose[mat2a] and hence bvec.mat2.bvec=2*bvec.mat2a.bvec. Moreover each row of mat2a is constant and we have (mat2a.bvec)[[i]] given by the product -Sum[a[k]*v[i,k],{k,dim}]*Sum[b[j],{j,dim}]. This just reduces to -Sum[a[k]*v[i,k],{k,dim}] under the operatin assumption about bvec. So we obtain bvec.mat2.bvec = -2*bvec.Table[Sum[a[k]*v[i,k],{k,dim}],{i,dim}] This Table is Mathematica notation for the vector described above as mat2a.bvec; if it helps for readability, a more conventional ascii notation might be {Sum[a[k]*v[1,k],{k,dim}], Sum[a[k]*v[2,k],{k,dim}], ... , Sum[a[k]*v[dim,k],{k,dim}]} Now just notice that this is exactly the same as -2*bvec.hmat.avec. This demonstrates (ii) and thus finishes the proof of the identity. Daniel Lichtblau Wolfram Research

**References**:**Proof For Cov. Matrix equation?***From:*"Robert Parry" <rparry@ct.bbd.co.za>

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