A Framework for Algorithm Stability

TL;DR

This paper introduces a comprehensive framework for analyzing the stability of algorithms, especially in dynamic data contexts, by examining various stability notions and their impact on solution quality.

Contribution

It presents a novel framework for stability analysis of algorithms, including event, topological, and Lipschitz stability, with applications to kinetic Euclidean minimum spanning trees.

Findings

Framework enables stability analysis in dynamic data scenarios

Trade-offs between stability and solution quality are characterized

Application to kinetic Euclidean minimum spanning trees demonstrates utility

Abstract

We say that an algorithm is stable if small changes in the input result in small changes in the output. This kind of algorithm stability is particularly relevant when analyzing and visualizing time-varying data. Stability in general plays an important role in a wide variety of areas, such as numerical analysis, machine learning, and topology, but is poorly understood in the context of (combinatorial) algorithms. In this paper we present a framework for analyzing the stability of algorithms. We focus in particular on the trade-off between the stability of an algorithm and the quality of the solution it computes. Our framework allows for three types of stability analysis with increasing degrees of complexity: event stability, topological stability, and Lipschitz stability. In addition, we need to refine the model of an algorithm based on how it interacts with the time-varying data, for which we consider several options. We demonstrate the use of our stability framework by applying it to kinetic Euclidean minimum spanning trees.