# How do I find a D matrix that satisfies [K]*inv(M)​*[D]==[D]*​inv(M)*[K] ?

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ikram ros salamat on 25 Jan 2021
Commented: David Goodmanson on 26 Jan 2021
M and K are respectively a mass and stiffness matrices 12x12, I need to find a matrix D 12x12 that satisfies this equality.
David Goodmanson on 26 Jan 2021
Hi ikram,
I agree that the idea of D being the damping matrix on the basis of satisfying
K*inv(M)*D = D* inv(M)*K
does not make sense physically. For one thing I was remiss in not mentioning that if D satisfies the equation above, so does constant*D, which means if D were damping, then its overall size could be anything. Bruno pointed out what I missed, that w*X*inv(V) is also a solution for any diagonal X.
Could you provide a reference or expanation of what is meant by the first theorem of Rayleigh?

David Goodmanson on 25 Jan 2021
Edited: David Goodmanson on 25 Jan 2021
Hi Ikram,
Using N in place of inv(M) for simplicity, you are looking for a D such that
K*N*D = D*N*K
Let K*N and N*K have eigenvalue expansions
(K*N)*W = W*lambda
(N*K)*V = V*lambda
for matrices W and V and diagonal eigenvalue matrix lambda (although the code outputs lambda as a vector for convenience). It's the same lambda in both cases since the eigenvalues of K*N and N*K are identical. Matlab does not put eigenvalues in any particular order, so the code sorts the lambdas to make sure they are in the same order in each case. For simplicity I also assume no repeated eigenvalues because things are more complicated in that situation. It's easy to show that
D = W*inv(V)
is the solution.
n = 7;
K = rand(n,n);
N = rand(n,n);
[W lam] = eig(K*N,'vector');
[~,ind] = sort(lam);
W = W(:,ind);
[V lam] = eig(N*K,'vector');
[~,ind] = sort(lam);
V = V(:,ind);
D = real(W/V); % remove tiny imaginary part down in the numerical noise
K*N*D - D*N*K % check, should be small
##### 2 CommentsShowHide 1 older comment
Bruno Luong on 25 Jan 2021
More generally
D = W*X*inv(V)
with X any diagonal matrix, is also a solution

Bruno Luong on 25 Jan 2021
Edited: Bruno Luong on 25 Jan 2021
You have homogeneous linear equation, the entire null space of operator (D considered as input)
K*inv(M)*D-D*inv(M)*K
(dimension n) contain all solutions:
K = rand(7);
M = rand(7);
I = eye(size(K));
A = K*inv(M);
B = inv(M)*K;
N = null(kron(I,A)-kron(B.',I)); % size(N,2) >= n
r = randn(size(N,2),1); % here we randomize the solution
D = reshape(N*r,size(K));
% Check, it should be small
norm(K*inv(M)*D-D*inv(M)*K)
Note that usually stiffnes and mass K and M are symmetric (definite positive). Thus B = A.'. Your equation is homegenuous Lyapunov equation