# RcppDE 0.1.0

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A new package RcppDE has been uploaded in a first version 0.1.0 to

CRAN.

It provides differential evolution optimisation—a variant of stochastic

optimisation that is similar to genetic algorithms but particularly suitable

for the floating-point representations common in numerical optimisation.

It builds of on the nice

DEoptim package by

Ardia et al, but reimplements the algorithm in C++ (rather than C) using a

large serving of

Rcpp and

RcppArmadillo.

I worked on this on for a few evenings and weekends in October and November

and then spent a few more evenings writing a

paper / vignette

(which is finished as a very first draft now)

about it. This was an interesting and captivating problem as I had worked on

genetic algorithms going back quite some time to the beginning and then again

the end of graduate school (and traces of that early work are near the bottom of my

presentations page).

So what got me started?

DEoptim is a really

nice package, but it is implemented in old-school

C.

There is nothing wrong with that per se, but at the same time that I was

wrestling with GAs, I also taught myself

C++ which, to put it

simply, offers a few more choices to the programmer. I like having those choices.

And with all the work that

Romain and I have put into

Rcpp, I was curious

how far I could push this cart if I were to move it along.

I made a bet with myself starting from the old saw *shorter, easier,
faster: pick any two*. Would it be possible to achieve all three of

these goals?

DEoptim, and I take

version 2.0-7 as my reference point here, is pretty efficiently yet

verbosely coded. Copying a vector takes a loop with an assignment for each

element, copying a matrix does the same using two loops. Replacing that with

a single statement in C++ is pretty easy.

We also have a few little optimisations behind the scenes here and there in

Rcpp: would all

that be enough to move the needle in terms of performance?

And the same time,

DEoptim is also full

of the uses of the old R API which we often point to in the

Rcpp documentation

so fixing readibility should be a relatively low-hanging fruit.

To cut a long story short, I was able to reduce code *size* quite

easily by using a combination of

C++ and

Rcpp idioms. I was

also able to get to *faster*: the

paper / vignette

demostrates consistent speed improvements on all setups that I tested (three

standard functions on three small and three larger parameter vectors). More

important speed gains were achieved by allowing use of objective functions

that are written in C++

which again is both possible and easy thanks to

Rcpp.

That leaves *easier* to prove: adding compiled objective functions is

one indication; further proof could be provided by, say, moving the inner

loop to parallel execution thanks to Open MP

which I may attempt over the next few months. So far I’d like to give myself about

half a point here. So not quite yet *shorter, easier, faster: pick any three*, but working on it.

Over the next few days I may try to follow up with a blog post or two

contrasting some code examples and maybe showing a chart from the vignette.

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