Design of Complex Experiments Using Mixed Integer Linear Programming

TL;DR

This paper demonstrates how Mixed Integer Linear Programming (MILP) can optimize complex neuroscience experiment designs by incorporating diverse real-world constraints, improving efficiency and flexibility.

Contribution

It introduces the mathematical foundations of MILP for experimental design and compares it to other techniques, with four case studies illustrating its practical application.

Findings

MILP effectively incorporates diverse constraints in experimental design.

Compared to traditional methods, MILP offers greater flexibility and optimization.

Four case studies demonstrate MILP's practical benefits in neuroscience experiments.

Abstract

Over the past few decades, neuroscience experiments have become increasingly complex and naturalistic. Experimental design has in turn become more challenging, as experiments must conform to an ever-increasing diversity of design constraints. In this article we demonstrate how this design process can be greatly assisted using an optimization tool known as Mixed Integer Linear Programming (MILP). MILP provides a rich framework for incorporating many types of real-world design constraints into a neuroimaging experiment. We introduce the mathematical foundations of MILP, compare MILP to other experimental design techniques, and provide four case studies of how MILP can be used to solve complex experimental design challenges.