definition
A scalar, \lambda, such that there exists a non-zero vector x (a corresponding eigenvector) for which the image of x under a given linear operator \mathrm{A} is equal to the image of x under multiplication by \lambda; i.e. \mathrm{A} x = \lambda x.
example
The eigenvalues \lambda of a square transformation matrix \mathrm{M} may be found by solving \det(\mathrm{M} - \lambda\mathrm{I}) = 0.
We have also shown that the eigenvalue classification scheme of [1] cannot be used to directly classify the codimension-two bifurcations.
The corresponding eigenvalue is the same in both cases.
The goal is to find visual characteristics of the domain which determine or at least estimate the lowest eigenvalue.
From section 3.1 we seek the eigenvector with smallest eigenvalue of.
For large problems round- off errors may accumulate during the solution of the matrix eigenvalue equation.
Hessian eigenmodes contribute with a weighting according to their eigenvalue.
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