We present a learning-based approach to computing solutions for certain NPhard problems. Our approach combines deep learning techniques with useful algorithmic elements from classic heuristics. The central component is a graph convolutional network that is trained to estimate the likelihood, for each vertex in a graph, of whether this vertex is part of the optimal solution.

Abstract--This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and custom NNs at the edge for personalized healthcare and advanced industrial services such as quality control, predictive maintenance, or fault diagnosis. By preventing data from being disclosed to cloud services, this method safeguards sensitive information, including industrial secrets and personal data. The outcomes of a thorough experimental session confirm that -on the Visual Wake Words dataset-the proposed approach can achieve state-of-the-art results by exploiting a search procedure that runs in less than 10 hours on the Raspberry Pi Zero 2. Index T erms--Neural Architecture Search, Edge AI, Healthcare Internet of Things, Industrial Internet of Things. Neural Networks (NNs) are widely used in Internet of Things (IoT) applications [1]. In this context, often the data collected by the available sensors are added to the training set with the purpose of improving generalization performances. On the other hand, in some cases, the data can be sensitive; healthcare data [2], industrial data [3] and biometric data [4] provide possible examples. Privacy concerns prevent some entities from accessing the benefits of machine learning (ML), as they may be unable or unwilling to share their data with cloud services that can train or even automatically design a custom neural network (NN) [5].

Combinatorial Optimization (CO) has a wide range of real-world applications, from transportation (Contardo et al., 2012) and logistics (Laterre et al., 2018), to energy (Froger et al., 2016). Concretely, leading RL methods typically train a policy to incrementally construct a solution one element at a time.

The Trigger Arc Traveling Salesman Problem (TA-TSP) extends the classical TSP by introducing dynamic arc costs that change when specific "trigger" arcs are traversed, modeling scenarios such as warehouse operations with compactable storage systems. This paper introduces a GRASP-based metaheuristic that combines multiple construction heuristics with a multi-neighborhood local search. The construction phase uses mixed-integer programming (MIP) techniques to transform the TA-TSP into a sequence of tailored TSP instances, while the improvement phase applies 2-Opt, Swap, and Relocate operators. Computational experiments on MESS 2024 competition instances achieved average optimality gaps of 0.77% and 0.40% relative to the best-known solutions within a 60-second limit. On smaller, synthetically generated datasets, the method produced solutions 11.3% better than the Gurobi solver under the same time constraints. The algorithm finished in the top three at MESS 2024, demonstrating its suitability for real-time routing applications with state-dependent travel costs.

Neural Information Processing Systems http://nips.cc/

Table 10 presents the hyperparameters used to train dPads programmatic models.

The paper presents bounds on the search performance of a simple, tree-based nearest neighbor search algorithm. The bounds depend on the vector quantization performance on the tree. It is argued that this result implies that trees with good vector quantization performance are advantageous for nearest neighbor search. The statement is extended to large margin splits. The title of the paper asks "which space partitioning tree to use for search"?