Figure Properties - SERC

gallery
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rosser—Classic symmetric eigenvalue test matrix
A = rosser returns the Rosser matrix. This matrix was a challenge for many
matrix eigenvalue algorithms. But the QR algorithm, as perfected by
Wilkinson and implemented in MATLAB, has no trouble with it. The matrix
is 8-by-8 with integer elements. It has:
A double eigenvalue
Three nearly equal eigenvalues
Dominant eigenvalues of opposite sign
A zero eigenvalue
A small, nonzero eigenvalue
smoke—Complex matrix with a 'smoke ring' pseudospectrum
A = gallery('smoke',n) returns an n-by-n matrix with 1’s on the
superdiagonal, 1 in the (n,1) position, and powers of roots of unity along the
diagonal.
A = gallery('smoke',n,1) returns the same except that element A(n,1) is
zero.
The eigenvalues of gallery('smoke',n,1) are the nth roots of unity; those of
gallery('smoke',n) are the nth roots of unity times 2^(1/n).
toeppd—Symmetric positive definite Toeplitz matrix
A = gallery('toeppd',n,m,w,theta) returns an n-by-n symmetric, positive
semi-definite (SPD) Toeplitz matrix composed of the sum of m rank 2 (or, for
certain theta, rank 1) SPD Toeplitz matrices. Specifically,
T = w(1)*T(theta(1)) + ... + w(m)*T(theta(m))
where T(theta(k)) has (i,j) element cos(2*pi*theta(k)*(i-j)).
By default: m = n, w = rand(m,1), and theta = rand(m,1).