On Submodular Search and Machine Scheduling

TL;DR

This paper studies a complex search and scheduling problem involving submodular costs and supermodular probabilities, providing approximation algorithms, special case solutions, and game-theoretic analysis.

Contribution

It introduces a 2-approximation algorithm for a submodular search problem, extends scheduling theory with a new application, and analyzes a game between a Hider and Searcher.

Findings

Developed a combinatorial 2-approximation algorithm for the search problem.

Provided better approximations for functions with low total curvature.

Solved the game in series-parallel decomposable cases.

Abstract

Suppose some objects are hidden in a finite set $S$ of hiding places which must be examined one-by-one. The cost of searching subsets of $S$ is given by a submodular function and the probability that all objects are contained in a subset is given by a supermodular function. We seek an ordering of $S$ that finds all the objects in minimal expected cost. This problem is NP-hard and we give an efficient combinatorial $2$-approximation algorithm, generalizing analogous results in scheduling theory. We also give a new scheduling application $1|prec|\sum w_A h(C_A)$, where a set of jobs must be ordered subject to precedence constraints to minimize the weighted sum of some concave function $h$ of the completion times of {\em subsets} of jobs. We go on to give better approximations for submodular functions with low {\em total curvature} and we give a full solution when the problem is what we call {\em series-parallel decomposable}. Next, we consider a zero-sum game between a cost-maximizing Hider and a cost-minimizing Searcher. We prove that the equilibrium mixed strategies for the Hider are in the base polyhedron of the cost function, suitably scaled, and we solve the game in the series-parallel decomposable case, giving approximately optimal strategies in other cases.