Frank-Wolfe Recursions for the Emergency Response Problem on Measure Spaces

TL;DR

This paper develops a Frank-Wolfe based optimization framework for allocating volunteer resources in emergency response scenarios modeled over measure spaces, demonstrating theoretical properties and practical scalability.

Contribution

It introduces a novel infinite-dimensional Frank-Wolfe algorithm for emergency response optimization, with convergence analysis and real-world application to urban data.

Findings

The algorithm converges even with approximate subproblem solutions.

Complex solution structures are revealed in simple and realistic scenarios.

The influence function's concavity enables efficient computation in certain norms.

Abstract

We consider an optimization problem over measures for emergency response to out-of-hospital cardiac arrest (OHCA), where the goal is to allocate volunteer resources across a spatial region to minimize the probability of death. The problem is infinite-dimensional and poses challenges for analysis and computation. We first establish structural properties, including convexity of the objective functional, compactness of the feasible set, and existence of optimal solutions. We also derive the influence function, which serves as the first-order variational object in our optimization framework. We then adapt and analyze a fully-corrective Frank-Wolfe (fc-FW) algorithm that operates directly on the infinite-dimensional problem without discretization or parametric approximation. We show a form of convergence even when subproblems are not solved to global optimality. Our full implementation of fc-FW demonstrates complex solution structure even in simple discrete cases, reveals nontrivial volunteer allocations in continuous cases, and scales to realistic urban scenarios using OHCA data from the city of Auckland, New Zealand. Finally, we show that when volunteer travel is modeled through the $L_1$ norm, the influence function is piecewise strictly concave, enabling fast computation via support reduction. The proposed framework and analysis extend naturally to a broad class of $P$-means problems.