Is it ever the case that exp(A) == exp.(A) apart from when A is 1x1?
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Diagonal 
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Umm no: exp(::Diagonal) is diagonal but exp(0)= 1:
julia> exp.(I(2))
2×2 Matrix{Float64}:
2.71828 1.0
1.0 2.71828
julia> exp(I(2))
2×2 Diagonal{Float64, Vector{Float64}}:
2.71828 ⋅
⋅ 2.71828
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… maybe I shouldn’t post before coffee …
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You can get arbitrarily close by just making A a matrix of larger and larger negative numbers.
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You can get arbitrarily close by just making A a matrix of larger and larger negative numbers.
You need to be more precise about how you want to approach the negative infinity. With e.g. equal entries this is nowhere on the road to zeros:
julia> exp([-10000 -10000;-10000 -10000])
2×2 Matrix{Float64}:
0.5 -0.5
-0.5 0.5
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Good point.
julia> using Optim
julia> sol = optimize(ones(2,2), BFGS()) do A
norm(exp(A) - exp.(A))
end
* Status: success
* Candidate solution
Final objective value: 0.000000e+00
* Found with
Algorithm: BFGS
* Convergence measures
|x - x'| = 5.47e+04 ≰ 0.0e+00
|x - x'|/|x'| = 1.00e+00 ≰ 0.0e+00
|f(x) - f(x')| = 2.55e-02 ≰ 0.0e+00
|f(x) - f(x')|/|f(x')| = Inf ≰ 0.0e+00
|g(x)| = 0.00e+00 ≤ 1.0e-08
* Work counters
Seconds run: 1 (vs limit Inf)
Iterations: 5
f(x) calls: 23
∇f(x) calls: 23
julia> sol.minimizer
2×2 Matrix{Float64}:
-54762.4 -53084.3
-53084.3 -54762.4
julia> exp.(sol.minimizer)
2×2 Matrix{Float64}:
0.0 0.0
0.0 0.0
julia> exp(sol.minimizer)
2×2 Matrix{Float64}:
0.0 -0.0
-0.0 0.0
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Stefan gutel tweeted me
Which had an example
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Is there a way to get logarithms of negative numbers without having to write + 0im all the time:
A = [log(-4/3+0im) log(-2+0im) log(-2+0im);
log(-2+0im) log(-4/3+0im) log(-2+0im);
log(-2+0im) log(-2+0im) log(-4/3+0im)]
julia> norm(exp(A) - exp.(A)) < 1e-12
true
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How about
julia> A = log.(Complex[-4//3 -2 -2
-2 -4//3 -2
-2 -2 -4//3])
3×3 Matrix{ComplexF64}:
0.287682+3.14159im 0.693147+3.14159im 0.693147+3.14159im
0.693147+3.14159im 0.287682+3.14159im 0.693147+3.14159im
0.693147+3.14159im 0.693147+3.14159im 0.287682+3.14159im
julia> exp(A) ≈ exp.(A)
true
?
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Great, thanks 
NB: Is there any point in this case in defining rational numbers (4//3) given that the logarithms and exponentiation should convert then to floats?
Not really, no.
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My first thought was also Diagonal, and I also haven’t had my coffee yet 
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With a matrix A that is strictly positive definite e^{tA} \rightarrow 0 as t \rightarrow -\infty. So as you see, tA is negative definite with negative entries
Nilpotent matrices should provide plenty of examples; the entries solving equations of the messy type e^{a_{ij}} = polynomial(a_{11},a_{12},\dots,a_{nn}).
Not every positive definite matrix has all positive entries though.
Examples of what?
Which is why I specified, negative definite (not semi-definite) matrix with negative entries.
Examples of matrices satisfying exp.(A) == exp(A).
Very unlikely nilpotent helps since zero is not an example
Theorem 1 of the following paper gave a complete characterization of the solutions.
ADDED: But the problem in the paper is not really what we are discussing here, notice that the zero-th term is missing, in the following definitions from the paper.
p(A)=c_{m} A^{m}+c_{m-1} A^{m-1}+\cdots+c_{1} A
p^{H}(A)=c_{m} A^{(m)}+c_{m-1} A^{(m-1)}+\cdots+c_{1} A
It will be nice if someone can provided a large family of solutions to our problem here.
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At the moment there is only one example which is complex… I’m curious if there’s a real example
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If matrix A has invertible eigenvector matrix M with corresponding eigenvalues \lambda_1,\ldots,\lambda_n, then
\exp(A)\cdot M = M\cdot \mathrm{diag}(e^\lambda_1,\ldots,e^\lambda_n)
Here, \mathrm{diag}(e^{\lambda_1},\ldots,e^{\lambda_n}) \neq \exp.(\Lambda) where \Lambda = \mathrm{diag}(\lambda_1,\ldots,\lambda_n) — \mathrm{diag} (e^{\lambda_1},\ldots,e^{\lambda_n}) has zeros outside of the diagonal, while \exp.(\Lambda) has unity outside of the diagonal.
More generally,
\exp(A) = \sum_{i=0}^\infty\frac{1}{i!}A^i