Search Results - Q-effective implementation of algorithm

Improving Parallel Computing Efficiency by Aleeva, Valentina N.

“… Any numerical algorithm has a Q-determinant that is convenient for implementation. The content of every Q-determinant are Q-terms that number is equal to the number of output data items…”
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High-Performance Computing Using Application of Q-determinant of Numerical Algorithms by Aleeva, Valentina N., Aleev, Rifkhat Zh

“…The conception of Q-determinant is one of the approaches to parallelizing numerical algorithms…”
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The Parallelization of Computations for Ensuring Information Security in Connected Vehicle Systems Using Q-Effective Programming: The Example of Dijkstra's Algorithm by Sokolov, Mikhail P., Manatin, Pavel A., Zyulyarkina, Natalya D.

“…This work researches the possibility of applying the concept of a Q-determinant for the parallelization of calculations in the implementation of algorithms to ensure the security of information…”
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Parallelism Resource of Numerical Algorithms. Version 1 by Aleeva, Valentina N, Rifkhat Zh Aleev

“…\)-determinant of the algorithm has clear structure and is convenient for implementation. The \(Q\)-determinant consists…”
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QL vs. SARSA: Performance Evaluation for Intrusion Prevention Systems in Software-Defined IoT Networks by Moreira, Christian Miranda, Kaddoum, Georges

“… Experimental results demonstrate that the state action reward state action (SARSA) algorithm is more effective than the Q-learning (QL…”
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The Orthogonal Rayleigh Quotient Iteration (ORQI) method by Dax, Achiya

“… T. The algorithm computes an orthogonal matrix Q=[ q 1,…, q n] and a diagonal matrix Λ=diag{ λ 1,…, λ n } such that TQ= QΛ. The basic ideas are rather simple…”
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基于时间差分误差的离线强化学习采样策略 by 张龙飞, 冯旸赫, 梁星星, 刘世旋, 程光权, 黄金才

“…TG142.71; 离线强化学习利用预先收集的专家数据或其他经验数据,在不与环境交互的情况下离线学习动作策略.与在线强化学习相比,离线强化学习具有样本效率高、交互成本低的优势.强化学习中通常使用Q值估计函数或Q值估计网络表示状态-动作的价值.因无法通过与环境交互及时修正Q值估计误差,离线强化学习往往面临外推误差严重…”
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基于图卷积深度强化学习的协同空战机动决策方法 by 欧洋, 郭正玉, 罗德林, 缪克华

“…TG142.71; 针对多无人机智能协同空战对抗决策问题,提出了一种基于长短期记忆与竞争图卷积深度强化学习的多机协同空战机动对抗决策方法.首先,对多机协同空战对抗问题进行描述;其次,在竞争Q网络中,引入长短期记忆网络用于处理带有强时序相关性的空战信息,接着,搭建图卷积网络作为多机之间的通信基础,提出基于长短期记忆与竞争…”
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Adaptive Dynamic Bipartite Graph Matching: A Reinforcement Learning Approach by Wang, Yansheng, Tong, Yongxin, Long, Cheng, Xu, Pan, Xu, Ke, Lv, Weifeng

“…Online bipartite graph matching is attracting growing research attention due to the development of dynamic task assignment in sharing economy applications,…”
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Collaborative air combat maneuvering decision-making method based on graph convolutional deep reinforcement learning by Yang OU, Zhengyu GUO, Delin LUO, Kehua MIAO

“…The effective implementation of multi-unmanned aerial vehicle (UAV) decision making and improvement in the efficiency of coordinated mission execution are currently the top priorities of air combat research…”
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Hardware-Based Model of Node Clustering Using Q-Learning for Wireless Sensor Networks by Delos Santos Manarang, Gienel Francheska, Rodriguez Mina, Rusty John Lloyd, Reyes Salvador, Mikko Chino, Gusad De Leon, Maria Theresa, Jagunap Densing, Chris Vincent, Rosales, Marc Driz, Ballesil Alvarez, Anastacia

“… A hardware implementation of CLIQUE, a Q-learning-based clustering algorithm that enhances the operation and resiliency of the network and improves the energy expenditure of the sensor nodes, is presented…”
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Efficient Method for Calculating Effective Core Potential Integrals by McKenzie, Simon C, Epifanovsky, Evgeny, Barca, Giuseppe M J, Gilbert, Andrew T B, Gill, Peter M W

“… The new algorithm is available in Q-Chem 5.0 and is significantly faster than the ECP implementations in Q-Chem 4.4, GAMESS (US) and Dalton 2016…”
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An effective software implementation of the space-vector modulation by YOUM, J.-H, KWON, B.-H

“…An effective software implementation of the space-vector modulation (SVM) on the rotating d-q frame is proposed…”
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A Faster Implementation of the Pivot Algorithm for Self-Avoiding Walks by Kennedy, Tom

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A faster implementation of the pivot algorithm for self-avoiding walks by Kennedy, Tom

“…; otherwise, it is rejected. Past implementations of the algorithm required a time O(N) per accepted pivot, where N is the number of steps in the walk…”
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