pkofod

using Optim, Calculus
# Let's try with a polynomial. It has very simple Hessian, as there are no cross
# products, only quadratic terms. Hence the Hessian is a diagonal matrix where
# diag(H) = [2, 2, ..., 2]. Let's try to see if that's what we get! 
function large_polynomial(x::Vector)
    res = zero(x[1])
    for i in 1:250
        res += (i - x[i])^2
    end
    return res

pkofod

intersect at /home/pkm/julia/julia/src/subtype.c:1628
intersect_ufirst at /home/pkm/julia/julia/src/subtype.c:1032 [inlined]
intersect_var at /home/pkm/julia/julia/src/subtype.c:1100
intersect at /home/pkm/julia/julia/src/subtype.c:1628
intersect_ufirst at /home/pkm/julia/julia/src/subtype.c:1032 [inlined]
intersect_var at /home/pkm/julia/julia/src/subtype.c:1100
intersect at /home/pkm/julia/julia/src/subtype.c:1628
intersect_ufirst at /home/pkm/julia/julia/src/subtype.c:1032 [inlined]
intersect_var at /home/pkm/julia/julia/src/subtype.c:1100
intersect at /home/pkm/julia/julia/src/subtype.c:1628

pkofod

julia> Profile.print()
11 ./event.jl:68; (::Base.REPL.##3#4{Base.REPL.REPLBackend})()
 11 ./REPL.jl:95; macro expansion
  11 ./REPL.jl:64; eval_user_input(::Any, ::Base.REPL.REPLBackend)
   11 ./boot.jl:234; eval(::Module, ::Any)
    11 ./<missing>:?; anonymous
     11 ./profile.jl:16; macro expansion;
      11 /home/pkm/.julia/v0.5/MDPTools/src/solution/solve.jl:39; solve!;
       11 /home/pkm/.julia/v0.5/MDPTools/src/solution/solve.jl:40; #solve!#58;
        11 ./<missing>:0; (::MDPTools.#kw##solve!)(::Array{Any,1}, ::MDPTools.#solve!, ::MDPTools.LinearUtility{Float64}, ::MDP...

pkofod

pkm@pkm:~/.julia/v0.6/RemPiO2$ julia6 -O3 test/runtests.jl 

Testing speed and accuracy of rempio2 in [-pi*9/4, pi*9/4]
----------------------------------------------------------
Numbers below are elapsed time returned from @belapsed

Every number is checked between the two implementations
using a @test such that any difference results in termi-
nation of the program.

pkofod

Benchmark Report

Job Properties

Commit(s): pkofod/julia@a4daf3b21f1a98dd208f50bc1b785d4d92f24d3b vs JuliaLang/julia@83007fb8c4c748fb23d38759daafd77fa1d56c2b

Triggered By: link

Tag Predicate: ALL

pkofod

Pkg.add("BenchmarkTools")
using BenchmarkTools
function inplace_sq_mat(m::Array{Float64, 2}, n::Array{Float64, 2})
    s = size(m, 1)
    t = s+1
    v = zeros(t)

    @inbounds for i=1:s
        for j=1:s
            v[j] = m[j, i]

pkofod

function tauchen(ρ, σₛ, m, N)
    const Φ = normcdf # CDF of standard normal
    s̃₁, s̃ₙ = -m*σₛ, m*σₛ # end points
    s̃ = linspace(s̃₁, s̃ₙ, N) # grid
    w = (s̃[2]-s̃[1])/2 # half distance between grid points
    F = zeros(N, N) # empty transition matrix
    F[:, 1] = Φ.((s̃[1]-ρ.*s̃+w)/sqrt(σₛ))
    F[:, N] = 1-Φ.((s̃[end]-ρ.*s̃-w)/sqrt(σₛ))
    for j = 2:N-1
        for i = 1:N

pkofod

pkm@pkm:~/.julia$ mkdir fakepkg
pkm@pkm:~/.julia$ julia
               _
   _       _ _(_)_     |  A fresh approach to technical computing
  (_)     | (_) (_)    |  Documentation: https://docs.julialang.org
   _ _   _| |_  __ _   |  Type "?help" for help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 0.6.0 (2017-06-19 13:05 UTC)
 _/ |\__'_|_|_|\__'_|  |  Official http://julialang.org/ release
|__/                   |  x86_64-pc-linux-gnu

pkofod

# Adapted from https://tpapp.github.io/post/log1p/
# consistent random numbers
T = Float32
srand(UInt32[0xfd909253, 0x7859c364, 0x7cd42419, 0x4c06a3b6])

"""
    err(x, [prec])

Return two values, which are the log2 relative errors for calculating
`log(x)`, using `Base.log` and `Base.Math.JuliaLibm.log`.

pkofod

Benchmarking 0.0
BenchmarkTools.Trial: 
  memory estimate:  0 bytes
  allocs estimate:  0
  --------------
  minimum time:     3.992 ns (0.00% GC)
  median time:      4.586 ns (0.00% GC)
  mean time:        4.647 ns (0.00% GC)
  maximum time:     18.766 ns (0.00% GC)
  --------------