Locality in Network Optimization

TL;DR

This paper introduces a new notion of correlation in optimization based on sensitivity to perturbations, demonstrates locality in network optimization, and develops a bias-variance trade-off approach to improve reoptimization efficiency.

Contribution

It defines a correlation concept in optimization, shows locality in network flow problems, and proposes a localization method that balances bias and variance for efficient reoptimization.

Findings

Correlation decays with graph distance in network optimization.

Localization can reduce computational complexity in reoptimization.

Bias-variance trade-off can be exploited to improve optimization efficiency.

Abstract

In probability theory and statistics notions of correlation among random variables, decay of correlation, and bias-variance trade-off are fundamental. In this work we introduce analogous notions in optimization, and we show their usefulness in a concrete setting. We propose a general notion of correlation among variables in optimization procedures that is based on the sensitivity of optimal points upon (possibly finite) perturbations. We present a canonical instance in network optimization (the min-cost network flow problem) that exhibits locality, i.e., a setting where the correlation decays as a function of the graph-theoretical distance in the network. In the case of warm-start reoptimization, we develop a general approach to localize a given optimization routine in order to exploit locality. We show that the localization mechanism is responsible for introducing a bias in the original algorithm, and that the bias-variance trade-off that emerges can be exploited to minimize the computational complexity required to reach a prescribed level of error accuracy. We provide numerical evidence to support our claims.