Oskar Laverny

Correctness issues + annoying behavior

2

Hey, The following behaviors are really annoying: ```julia julia> t = TaylorScalar{Float64, 3}((Inf, 1.0, 0.0)) TaylorScalar{Float64, 3}((Inf, 1.0, 0.0)) julia> t+1 TaylorScalar{Float64, 3}((Inf, 1.0, 0.0)) ##### OK julia> t*1 TaylorScalar{Float64,...

Performance of `(+,-,*,/)(::TaylorScalar,::Number)`: easy short path ?

11

Hey, The current implementation of `*(::TaylorScalar,::Number)` in your package promotes the scalar to a TaylorScalar as so : ````julia for op in (:+, :-, :*, :/) @eval @inline $op(a::TaylorScalar, b::Number)...

Hi, I do want to solve the SDP problem from example (4.1) in [this paper](https://arxiv.org/pdf/1105.0410.pdf). This problem seem quite easy to solve, but i do not manage to make it...

Interface compatibility with Distributions.jl ?

5

Hey, Thanks for this great addition to the ecosystem ! From Distribution.jl, it seems like the first argument to the fit_mle function should be the distributions type and not an...

`0 <= cdf(truncated(Normal(2.5, 0.2), lower=0.0),x)` not always true.

2

Followup to #1853 Consider the following: ```julia using Distributions D = truncated(Normal(2.5, 0.2), lower=0.0) cdf(D, 3.741058503233821e-17) > 0 # false cdf(D, 1.4354474178676617e-18) > 0 # false cdf(D, 8.834854780587132e-18) > 0...