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use power iteration for opnorm2 #49487

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use power iteration for opnorm2 #49487

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use power iteration for opnorm2
oscardssmith Apr 24, 2023
0a8b9e2
minor type improvement
oscardssmith Apr 24, 2023
c798902
Nonsquare typo and use `randn`
oscardssmith Apr 25, 2023
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oscardssmith May 4, 2023
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22 changes: 21 additions & 1 deletion stdlib/LinearAlgebra/src/generic.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -670,13 +670,33 @@ function opnorm1(A::AbstractMatrix{T}) where T
return convert(Tnorm, nrm)
end

# Uses power iteration to compute the maximal singular value.
# falls back to svdvals if it runs into numerics issues.
function opnorm2(A::AbstractMatrix{T}) where T
require_one_based_indexing(A)
m,n = size(A)
Tnorm = typeof(float(real(zero(T))))
if m == 0 || n == 0 return zero(Tnorm) end
if m == 1 || n == 1 return norm2(A) end
return svdvals(A)[1]
At = A'
# to minimize the chance of x being orthogonal to the largest eigenvector
x = randn(T, n)
tmp = A * x
# this will converge quickly as long as the top two eigenvalues are distinct
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Couldn't we use a Lanczos iteration to be more robust and converge quickly even if a few eigenvalues are similar in magnitude?

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@stevengj stevengj Apr 29, 2023 •
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This paper seems to be a popular algorithm: https://doi.org/10.1137/04060593X and is even fancier (restarting, bidiagonalization).

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good point. There's a python implementation of that paper with a good license here: https://github.com/bwlewis/irlbpy/blob/master/irlb/irlb.py. I'll see how it does.

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good news and bad news. Good news is I have it working. Bad news is it takes about 84 lines of code. Performance testing to come.

for i in 1:n
mul!(tmp, A, x)
v1 = norm(tmp)
!isfinite(v1) && return v1
tmp .*= inv(v1)
mul!(x, At, tmp)
v2 = norm(x)
!isfinite(v2) && return v2
# tune this better
isapprox(v1, v2) && return v2
x .*= inv(v2)
end
# iteration failed to converge
return first(svdvals(A))
end

function opnormInf(A::AbstractMatrix{T}) where T
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