Sparse Graphical Designs via Linear Programming

TL;DR

This paper introduces a linear programming approach to create sparse graphical designs that balance sampling sparsity with accuracy, demonstrated on NYC taxi data.

Contribution

It presents a novel method for designing sparse graphical sampling schemes using linear programming to optimize accuracy.

Findings

Successfully applied to NYC taxi data

Achieved sparse designs with high accuracy

Demonstrated effectiveness of linear programming approach

Abstract

Graphical designs are a framework for sampling and numerical integration of functions on graphs. In this note, we introduce a method to address the trade-off between graphical design sparsity and accuracy. We show how to obtain sparse graphical designs via linear programming and design objective functions that aim to maximize their accuracy. We showcase our approach using yellow taxicab data from New York City.