MOE Research and Notes

##Inputs

The input to MOE is some set of historical parameters sampled {x⃗ 1,…,x⃗ n} and their associated function valuations {f(x⃗ 1),…,f(x⃗ n)}. The evaluations may require A/B testing, an map reduce job or some other expensive or time consuming process. MOE will never need to evaluate f, it only takes the outputs you provide and suggests new optimal inputs to test/evaluate.

##Objective Function

The objective function is the function f that we are trying to minimize/maximize over some set of Parameters.

argmaxx⃗ ∈ℝd f(x⃗ )f:ℝd→ℝ

The objective function is considered a black box (http://en.wikipedia.org/wiki/Black_box) function. We require no internal knowledge or access to the function. In fact it can potentially be non-convex, non-differentiable or non-continuous. Furthermore, we assume that evaluating the function, f, is expensive and we need to find the best set of parameters/inputs x⃗ with as few function evaluations as possible.

• this seems like it is the computation of the elastic tensor/bulk modulus/poisson's ratio/etc itself

##Install

1. Get virtualbox

2. Get docker and supporting packages:

   $ brew update

   $ brew install docker

   $ brew install boot2docker

3. Initialize boot2docker and start in vm

   $ boot2docker init

   $ boot2docker up

4. Copy export variable(s) into bash profile

Notes taken from http://yelp.github.io/MOE/objective_functions.html, http://viget.com/extend/how-to-use-docker-on-os-x-the-missing-guide