Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy

TL;DR

This paper introduces a graph neural network approach for resource allocation in scientific experiments, allowing flexible, trainable strategies that outperform traditional methods in complex, real-world scenarios like astronomical target selection.

Contribution

The paper presents a novel bipartite Graph Neural Network architecture for resource allocation, capable of optimizing complex, science-driven objectives beyond linear functions.

Findings

Outperforms gradient descent optimization in astronomical target selection

Extends capabilities of existing linear objective-based solvers

Enables fast, adaptable, and differentiable resource allocation strategies

Abstract

Resource allocation problems are often approached with linear programming techniques. But many concrete allocation problems in the experimental and observational sciences cannot or should not be expressed in the form of linear objective functions. Even if the objective is linear, its parameters may not be known beforehand because they depend on the results of the experiment for which the allocation is to be determined. To address these challenges, we present a bipartite Graph Neural Network architecture for trainable resource allocation strategies. Items of value and constraints form the two sets of graph nodes, which are connected by edges corresponding to possible allocations. The GNN is trained on simulations or past problem occurrences to maximize any user-supplied, scientifically motivated objective function, augmented by an infeasibility penalty. The amount of feasibility violation can be tuned in relation to any available slack in the system. We apply this method to optimize the astronomical target selection strategy for the highly multiplexed Subaru Prime Focus Spectrograph instrument, where it shows superior results to direct gradient descent optimization and extends the capabilities of the currently employed solver which uses linear objective functions. The development of this method enables fast adjustment and deployment of allocation strategies, statistical analyses of allocation patterns, and fully differentiable, science-driven solutions for resource allocation problems.