Hi everybody, using LinearAlgebra eigvals([0. 1im;1im 0.]) gives: 2-element Vector{ComplexF64}: 0.0 + 0.9999999999999997im 2.7755575615628914e-17 - 1.0im which is correct but with approximation. I am new to Julia and wondering if anything I’m missing or not. The question ac…

Also, if you need more precision, you can use BigFloats. julia> eigvals([big(0) 1im; 1im 0]) 2-element Vector{Complex{BigFloat}}: 0.0 - 1.0im 0.0 + 1.0im

eigvals is an iterative method, even for problems this small. So it seesm that you got something reasonable.

When dealing with floats, you should in general expect answers that are “correct but with approximation”, because of the way floating numbers are stored internally. As another illustration of this, consider julia> .1 + .2 0.30000000000000004

[image] PSA: floating-point arithmetic Offtopic Sometimes people are surprised by the results of floating-point calculations such as julia> 5/6 0.8333333333333334 # shouldn't the last digit be 3? julia> 2.6 - 0.7 - 1.9 2.2204460492503…

Thank you all for the answers. Sorry my question was kind of naive as my background is not CS. Just another question to me, maybe you put a comment on it, is: I am doing a matrix computation in which I am using FLoops.jl to parallel the computation. Here what I get from @floop and without @floop ar…

Floating point round-off depends on the order of operations. Doing calculations in parallel vs sequentially will change the order of operations.

Eigenvalues are approximately correct, not exactly

New to Julia

Sukera April 3, 2022, 10:01pm 5

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Eigenvalues are approximately correct, not exactly

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