Neural Encoding

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Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Sep 5, 2025Tim Dernedde, Daniela Thyssens, Lars Schmidt-ThiemeRecurrent Neural Networks Combinatorial Optimization
Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim
Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts
Date pendingNi Yang, Rui He, Philipp Homan +3Neural Encoding
Grammar and Cognition Lab, Department of Translation & Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain. · Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland. · Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland. +3

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Recurrent State Encoders for Efficient Neural Combinatorial Optimization

Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme

Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim

Recurrent Neural Networks Combinatorial Optimization Neural Encoding Neural Combinatorial Optimization

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The primary paradigm in Neural Combinatorial Optimization (NCO) consists of construction methods, where a neural network is trained to sequentially add one solution component at a time until a complete solution is formed. We observe that the typical changes to the state between two steps are small, since usually only the node added to the solution is removed from the state. An efficient model should be able to reuse computation from prior steps. To that end, we propose a recurrent encoder that computes state embeddings based not only on the current state but also on embeddings from the previous state. We show that this recurrent encoder can achieve equivalent or better performance than a non-recurrent encoder even with

3\times

fewer layers, thus significantly improving latency. We demonstrate our findings on three different problems: the Traveling Salesman Problem (TSP), the Capacitated Vehicle Routing Problem (CVRP), and the Orienteering Problem (OP), and integrate the models into a large neighborhood search algorithm to showcase the practical relevance of our findings.

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Latest papers 62

Recurrent State Encoders for Efficient Neural Combinatorial Optimization

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts