论文标题
使用混合整数线性编程的复杂实验设计
Design of Complex Experiments Using Mixed Integer Linear Programming
论文作者
论文摘要
在过去的几十年中，神经科学实验变得越来越复杂和自然主义。实验设计又变得更具挑战性，因为实验必须符合不断增长的设计约束。在本文中，我们演示了如何使用称为混合整数线性编程（MILP）的优化工具来大力帮助此设计过程。 MILP提供了一个丰富的框架，可以将许多类型的现实设计约束结合到神经影像实验中。我们介绍了MILP的数学基础，将MILP与其他实验设计技术进行了比较，并提供了四个案例研究，涉及如何使用MILP来解决复杂的实验设计挑战。
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.
