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Bayesian Optimization Allowing for Common Random Numbers

The code for producucing the result in the papaer to appear in the journal "Operations Research". For further details check out

A few disclaimers

  • the code is written entirely in R (yes, python would be a loot better, hopefully one day!)
  • the code was written by a PhD student (me in 2018) with very little knowledge of good coding practice at the time!

Running code

Clone the repo and step inside

git clone https://github.com/scrambledpie/CRN.git
cd CRN

To run different baselines and differnt methods, simply edit the settings in the Experiment_runner.R file.

Run from R terminal

Assuming you have a version or R installed on your computer.

  1. Install R packages FastGP, Rcpp, R6, testit
install.packages(c("FastGP", "Rcpp", "R6", "testit"), repos="https://cloud.r-project.org")
  1. Run an example experiment from the command line
source('Experiment_runner.R')

Run from bash

Assuming you have a version or R installed on your computer.

  1. Install R packages FastGP, Rcpp, R6, testit
Rscript -e 'install.packages(c("FastGP", "Rcpp", "R6", "testit"), repos="https://cloud.r-project.org")'
  1. Run an example experiment from the command line
Rscript Experiment_runner.R

Docker

If you do not have/want R installed, then use the docker container.

  1. Build the docker image form the Dockerfile
docker build -f Dockerfile -t bo_crn .
  1. Mount the local code dir into a container and run it
docker run -v $(pwd):/code/ bo_crn Rscript Experiment_runner.R

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