The following code gives expected output in Julia 4.+, but errors in Julia
5.0
using LinearMaps
n=10
A=rand(n,n)
function f(x::Vector)
A*x
end
B=LinearMap(f,n)
eigs(B)
ERROR: LoadError: MethodError: no method matching
issymmetric(::LinearMaps.FunctionMap{Float64})
Closest candidates are:
Thanks! One reference for tracking eigenvalues with Jacobi is
http://www.sciencedirect.com/science/article/pii/S00243795046X
Proper implementation of Jacobi requires blocking and some preconditioning
(see LAPACK's dgesvj.f).
I will take your suggestion seriuosly and see what to do about
I'm will be lecturing the course Modern Applications of Numerical Linear
Algebra at IIT Indore, June 24- July 5.
The three modules are Short Julia Course, Eigenvalue and Singular Value
Decompositions, and Applications.
The lecture notes are all Jupyter notebooks, see
In [1]:
Z=givens(1.0,2.0,1,3,3)
Z, Z', transpose(Z)
Out[1]:
(
3x3 Givens{Float64}:
0.447214 0.0 0.894427
0.0 1.0 0.0
-0.894427 0.0 0.447214,
3x3 Givens{Float64}:
0.447214 0.0 0.894427
0.0 1.0 0.0
-0.894427 0.0 0.447214,
3x3 Givens{Float64}:
