Nonmonontone submodular maximization under routing constraints

TL;DR

This paper addresses the complex problem of maximizing nonmonotone submodular functions under routing constraints, proposing an iterated greedy algorithm with proven approximation guarantees and practical efficiency demonstrated through experiments.

Contribution

It introduces a novel iterated greedy algorithm for nonmonotone submodular maximization with complex, NP-hard constraints, providing theoretical approximation bounds and practical insights.

Findings

The proposed algorithm achieves a constant-factor approximation.

It effectively balances time complexity and over-budget constraints.

Experimental results validate the algorithm's practical efficacy.

Abstract

In machine learning and big data, the optimization objectives based on set-cover, entropy, diversity, influence, feature selection, etc. are commonly modeled as submodular functions. Submodular (function) maximization is generally NP-hard, even in the absence of constraints. Recently, submodular maximization has been successfully investigated for the settings where the objective function is monotone or the constraint is computation-tractable. However, maximizing nonmonotone submodular function with complex constraints is not yet well-understood. In this paper, we consider the nonmonotone submodular maximization with a cost budget or feasibility constraint (especially from route planning) that is generally NP-hard to evaluate. Such a problem is common for machine learning, big data, and robotics. This problem is NP-hard, and on top of that, its constraint evaluation is also NP-hard, which adds an additional layer of complexity. So far, few studies have been devoted to proposing effective solutions, making this problem currently unclear. In this paper, we first propose an iterated greedy algorithm, which yields an approximation solution. Then we develop the proof machinery that shows our algorithm is a bi-criterion approximation algorithm: it can achieve a constant-factor approximation to the optimal algorithm, while keeping the over-budget tightly bounded. We also explore practical considerations of achieving a trade-off between time complexity and over-budget. Finally, we conduct numeric experiments on two concrete examples, and show our design's efficacy in practical settings.