Graph neural networkbased resource allocation strategies for multiobject spectroscopy
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 (GNN) 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 usersupplied, 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, sciencedriven solutions for resource allocation problems.
 Publication:

Machine Learning: Science and Technology
 Pub Date:
 March 2022
 DOI:
 10.1088/26322153/ac4d12
 arXiv:
 arXiv:2109.13361
 Bibcode:
 2022MLS&T...3a5023W
 Keywords:

 graph neural network;
 resource allocation;
 spectroscopy;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 Computer Science  Machine Learning
 EPrint:
 The GNN code used in this paper is available at https://github.com/tianshuwang/PFSGNNbipartite