Accompanying code for "Nearest \Omega-stable matrix via Riemannian optimization", V. Noferini and F. Poloni.
This code has been tested with Manopt 5.0.
Example:
>> importmanopt
Manopt was added to Matlab's path.
>> rng(0)
>> A = randn(5);
>> B = nearest_real_eigenvalues(A);
>> C = nearest_stable_real(A, 'hurwitz'); # nearest Hurwitz stable in R^(nxn)
>> D = nearest_stable_complex(A, 'schur'); # nearest Schur stable in C^(nxn)
It may seem that the computed solutions do not respect the constraints, e.g.,
>> eig(B)
ans =
-1.3732e+00 + 0.0000e+00i
4.8213e-01 + 0.0000e+00i
4.8200e-01 + 1.2719e-04i
4.8200e-01 - 1.2719e-04i
4.8187e-01 + 0.0000e+00i
does not have computed real eigenvalues. However, this is only due to round-off errors: the matrix B is at a distance comparable with machine precision from one with real eigenvalues. The fourth and fifth output value provide a Schur decomposition of the solution:
>> [B, ~, ~, Q, T] = nearest_real_eigenvalues(A)
B =
9.7238e-01 8.7634e-01 1.4553e+00 -2.8240e-01 -2.6962e-01
8.0089e-01 -1.0966e+00 2.6768e-01 -3.8187e-01 -1.1662e-01
-3.9443e-01 -1.0929e+00 -7.2378e-01 1.1848e-01 3.1793e-01
3.0884e-01 -7.9942e-01 1.3594e+00 2.8792e-01 1.1094e+00
-8.2340e-01 -2.9726e+00 -1.6790e+00 -7.5791e-01 1.1149e+00
Q =
-3.8670e-01 6.5349e-01 1.9233e-01 -1.0284e-01 -6.1307e-01
4.8406e-01 1.7892e-01 6.0460e-02 -8.5309e-01 4.7463e-02
3.6523e-01 -4.9559e-01 -5.1955e-02 5.5984e-02 -7.8432e-01
-3.8176e-01 -4.5324e-01 7.6290e-01 -2.5539e-01 3.9851e-02
5.8053e-01 2.9984e-01 6.1208e-01 4.3966e-01 7.1710e-02
T =
-1.3732e+00 5.0898e-01 -3.9577e-01 2.5657e+00 2.0421e+00
0 4.8200e-01 -7.0671e-01 -4.3808e-01 -5.9247e-01
0 0 4.8200e-01 2.6800e+00 -2.7807e-01
0 0 0 4.8200e-01 1.9519e+00
0 0 0 0 4.8200e-01
>> norm(B - Q*T*Q')
ans =
0
One can see in this example that T does indeed have real eigenvalues (including, in particular, one with multiplicity 4). It is consistent with eigenvalue perturbation theory that when one forms the product QTQ' these eigenvalues are affected by perturbations of magnitude eps^(1/4) \approx 10^(-4).
The second and third output of the functions contain diagnostics in the same format as those produced by the code on http://bit.ly/NearestStableMatrix2 by N. Gillis (objective function and time after each iteration).
(Note that their corresponding code for the Schur case returns squared norms rather than norms in e.)
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