Eigen() warntype

New to Julia
1

calling LinearAlgebra.eigen() gives warntype. Is it a bug?

import Statistics
import LinearAlgebra
function test(x::Matrix{Float64})
	sigma = Statistics.cov(x)
	eig = LinearAlgebra.eigen(sigma)
	return eig
end

julia> @code_warntype test(randn(20, 5) )
Body::Union{Eigen{Complex{Float64},Complex{Float64},Array{Complex{Float64},2},Array{Complex{Float64},1}}, Eigen{Float64,Float64,Array{Float64,2},Array{Float64,1}}}
1 ─ %1 = Statistics.cov::Core.Compiler.Const(Statistics.cov, false)
│   %2 = invoke Statistics.:(#cov#37)(1::Int64, true::Bool, %1::Function, _2::Array{Float64,2})::Array{Float64,2}
│   %3 = LinearAlgebra.eigen::Core.Compiler.Const(LinearAlgebra.eigen, false)
│   %4 = invoke LinearAlgebra.:(#eigen#58)(true::Bool, true::Bool, %3::Function, %2::Array{Float64,2})::Union{Eigen{Complex{Float64},Complex{Float64},Array{Complex{Float64},2},Array{Complex{Float64},1}}, Eigen{Float64,Float64,Array{Float64,2},Array{Float64,1}}}
└──      return %4
2

The eigenvalues and eigenvectors can be real or complex. This is deliberate behaviour (not a bug).

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3

okay, even it’s not a bug, it’s a design issue: it gives a warntype, which would slows down runtime.

or, is it a special case that has no runtime impact?

moreover, as far as I know, for positive-semifinite matrix like the covariance, the Eigen values and vectors would always be real. Could we do better?

4

The amount of time spent on the warning will be tiny compared to the time taken to calculate the eigen-decomposition.

There is no type instability if the matrix is e.g. of type Symmetric.

5

thanks! so, the function should better be written as:

function test2(x::Matrix{Float64})
	sigma = Statistics.cov(x)
	eig = LinearAlgebra.eigen(LinearAlgebra.Symmetric(sigma) )
	return eig
end

x = randn(20, 5)

julia> test(x) == test2(x)
true

@code_warntype test2(x)    # fine
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6

Maybe Statistics.cov should automatically return a Symmetric matrix?

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7

But probably that would lead to type instability somewhere down the line.

8

Note that you can test that the result type of the calculation is the same as the type that Julia infers using

using Test
@inferred test2(x)
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9

covariance is not only symmetric, it’s also positive-semi-finite (I think). It may be a big change to LinearAlgebra and Statistics to incorporate a special matrix type for it.

10

Yes, a covariance matrix is at least positive semi-definite.
If the number of linearly independent samples exceeds the dimensionality, it will be positive definite.

If you’re taking the eigendecomposition anyway, you can easily check whether all the eigenvalues are positive.

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