Assignment problem

About: Assignment problem is a research topic. Over the lifetime, 7588 publications have been published within this topic receiving 172820 citations. The topic is also known as: marriage problem.

When Edge Computing Meets Microgrid: A Deep Reinforcement Learning Approach

Abstract: The computational tasks at multiaccess edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, nonrenewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we study about the microgrid-enabled MEC networks’ energy supply plan, where we first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks. The problem is a mixed integer nonlinear optimization with computational and latency constraints for tasks fulfillment, and also coupled with the dependencies of uncertainty for both energy consumption and generation. Therefore, we show that the problem is an NP-hard problem. As a result, second, we decompose our formulated problem into two subproblems: 1) energy-efficient tasks assignment problem for MEC into community discovery problem and 2) energy supply plan problem into Markov decision process. Third, we apply a low complexity density-based spatial clustering of applications with noise to solve the first subproblem for each base station distributedly. Sequentially, we use the output of the first subproblem as a input for solving the second subproblem, where we apply a model-based deep reinforcement learning. Finally, the simulation results demonstrate the significant performance gain of the proposed model with a high accuracy energy supply plan.

Time Slot Assignment in SS/TDMA Systems with Intersatellite Links

Abstract: In this paper we study the time slot assignment problem in clusters of SS/TDMA satellite systems interconnected through intersatellite links. We show that the problem of finding an assignment which minimizes the total transmission time is NP-complete, i.e., computationally intractable, even for quite restricted intersatellite link patterns and simplified system models. Successively, we focus our attention on clusters of two satellites, proposing a branch-and-bound optimal algorithm and two fast heuristic algorithms. We investigate the performance of the proposed heuristic algorithms both by a theoretical worst case bound and by simulation trials showing that the produced solutions are close to the optimal on the average.

Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach

Abstract: Graph matching aims to establish node correspondence between two graphs, which has been a fundamental problem for its NP-complete nature One practical consideration is the effective modeling of the affinity function in the presence of noise, such that the mathematically optimal matching result is also physically meaningful This paper resorts to deep neural networks to learn the node and edge feature, as well as the affinity model for graph matching in an end-to-end fashion The learning is supervised by combinatorial permutation loss over nodes Specifically, the parameters belong to convolutional neural networks for image feature extraction, graph neural networks for node embedding that convert the structural (beyond second-order) information into node-wise features that leads to a linear assignment problem, as well as the affinity kernel between two graphs Our approach enjoys flexibility in that the permutation loss is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference Moreover, our network is class-agnostic Experimental results on extensive benchmarks show its state-of-the-art performance It bears some generalization capability across categories and datasets, and is capable for robust matching against outliers