Matrix scaling for improved conditioning
Equilibrate a matrix with a large condition number to improve the efficiency and stability of a linear system solution with the iterative solver
west0479 matrix, which is a real-valued 479-by-479 sparse matrix. Use
condest to calculate the estimated condition number of the matrix.
load west0479 A = west0479; c1 = condest(A)
c1 = 1.4244e+12
Try to solve the linear system using
gmres with 450 iterations and a tolerance of
1e-11. Specify five outputs so that
gmres returns the residual norms of the solution at each iteration (using
~ to suppress unneeded outputs). Plot the residual norms in a semilog plot. The plot shows that
gmres is not able to achieve a reasonable residual norm, and so the calculated solution for is not reliable.
b = ones(size(A,1),1); tol = 1e-11; maxit = 450; [x,flx,~,~,rvx] = gmres(A,b,,tol,maxit); semilogy(rvx) title('Residual Norm at Each Iteration')
equilibrate to permute and rescale
A. Create a new matrix
B = R*P*A*C, which has a better condition number and diagonal entries of only 1 and -1.
[P,R,C] = equilibrate(A); B = R*P*A*C; c2 = condest(B)
c2 = 5.1036e+04
Using the outputs returned by
equilibrate, you can reformulate the problem into , where and . In this form you can recover the solution to the original system with .
gmres to solve for , and then replot the residual norms at each iteration. The plot shows that equilibrating the matrix improves the stability of the problem, with
gmres converging to the desired tolerance of
1e-11 in fewer than 200 iterations.
d = R*P*b; [y,fly,~,~,rvy] = gmres(B,d,,tol,maxit); hold on semilogy(rvy) legend('Original', 'Equilibrated', 'Location', 'southeast') title('Relative Residual Norms (No Preconditioner)') hold off
Improve Solution with Preconditioner
After you obtain the matrix
B, you can improve the stability of the problem even further by calculating a preconditioner for use with
gmres. The numerical properties of
B are better than those of the original matrix
A, so you should use the equilibrated matrix to calculate the preconditioner.
Calculate two different preconditioners with
ilu, and use these as inputs to
gmres to solve the problem again. Plot the residual norms at each iteration on the same plot as the equilibrated norms for comparison. The plot shows that calculating preconditioners with the equilibrated matrix greatly increases the stability of the problem, with
gmres achieving the desired tolerance in fewer than 30 iterations.
semilogy(rvy) hold on [L1,U1] = ilu(B,struct('type','ilutp','droptol',1e-1,'thresh',0)); [yp1,flyp1,~,~,rvyp1] = gmres(B,d,,tol,maxit,L1,U1); semilogy(rvyp1) [L2,U2] = ilu(B,struct('type','ilutp','droptol',1e-2,'thresh',0)); [yp2,flyp2,~,~,rvyp2] = gmres(B,d,,tol,maxit,L2,U2); semilogy(rvyp2) legend('No preconditioner', 'ILUTP(1e-1)', 'ILUTP(1e-2)') title('Relative Residual Norms with ILU Preconditioner (Equilibrated)') hold off
P— Permutation matrix
Permutation matrix, returned as a sparse matrix.
P*A is the
A that maximizes the absolute value of the product of
its diagonal elements.
R— Row scaling
Row scaling, returned as a sparse diagonal matrix. The diagonal entries in
C are real and positive.
C— Column scaling
Column scaling, returned as a sparse diagonal matrix. The diagonal entries in
C are real and positive.