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add lenstra eliptic curve factoring#104

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oscardssmith wants to merge 1 commit intoJuliaMath:mainfrom
oscardssmith:Lenstra-elliptic-curve
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add lenstra eliptic curve factoring#104
oscardssmith wants to merge 1 commit intoJuliaMath:mainfrom
oscardssmith:Lenstra-elliptic-curve

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heavily inspired by @trizen, but refactored a little to make it more reliable.

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some benchmarks:

x = prod(nextprime(big(2)^30, i) for i in 1:2)
@time Primes.pollardfactors!(x, Dict{BigInt,Int}())
  0.028354 seconds (1.13 M allocations: 22.163 MiB)
@time lenstrafactors!(x, Dict{BigInt,Int}())
  0.110741 seconds (1.65 M allocations: 30.821 MiB, 26.82% gc time)

x = prod(nextprime(big(2)^30, i) for i in 1:20)
@time Primes.pollardfactors!(x, Dict{BigInt,Int}());
  0.830832 seconds (14.86 M allocations: 346.059 MiB, 21.69% gc time)
@time lenstrafactors!(x, Dict{BigInt,Int}());
  0.375128 seconds (3.62 M allocations: 114.038 MiB, 11.86% gc time)

x = prod(nextprime(big(2)^50, i) for i in 1:2)
@time lenstrafactors!(x, Dict{BigInt,Int}())
 18.921172 seconds (296.05 M allocations: 6.041 GiB, 14.56% gc time)
@time Primes.pollardfactors!(x, Dict{BigInt,Int}())
  56.697309 seconds (1.12 G allocations: 21.651 GiB, 20.29% gc time)

These are the bad cases for ecm (when all the factors are of similar size). That said, ecm tends to do a bunch better for larger inputs, and for numbers with a few smaller factors. This implementation is not especially optimized, but I think it is a good baseline for future improvements.

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