Article
Mathematics
David Orellana-Martin, Antonio Ramirez-de-Arellano, Jose Antonio Andreu-Guzman, Alvaro Romero-Jimenez, Mario J. Perez-Jimenez
Summary: This paper discusses the class R of recognizer membrane systems that can provide polynomial-time and uniform solutions for NP-complete problems, defining it as an efficient class. By representing R as a class of efficient recognizer cell-like P systems with object evolution rules, communication rules, and dissolution rules, the polynomial-time complexity class PMCR is obtained, encompassing both the NP and co-NP classes. The DP class, which includes languages that can be expressed as the difference between any two languages in NP, is considered as a more complex class than the NP class and serves as promising candidates for studying the P vs NP problem. This paper extends previous results to include any class R of efficient recognizer tissue-like membrane systems and presents a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems.
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Qiang Yang, Dequan Guo, Haina Rong, Ming Zhu, Kang Zhou
Summary: This paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) that can solve combinatorial optimization problems without the help of evolutionary algorithms or swarm intelligence algorithms. Extensive experiments demonstrate its superiority over other methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Haina Rong
Summary: An extended numerical spiking neural (ENSN P) system is proposed to solve continuous constrained optimization problems. In ENSN P systems, production functions are selected by probability to achieve updated parameters. Experimental results show that the proposed method outperforms or is competitive with other 28 optimization algorithms in five benchmarks.
INFORMATION SCIENCES
(2023)
Article
Quantum Science & Technology
Caio B. D. Goes, Thiago O. O. Maciel, Giovani G. G. Pollachini, Juan P. L. C. Salazar, Rafael G. G. Cuenca, Eduardo I. I. Duzzioni
Summary: This paper proposes a hybrid algorithm based on machine learning and quantum ensemble learning to find an approximate solution to a partial differential equation with good precision and favorable scaling in the required number of qubits. The classical component trains multiple regressors capable of approximately solving the equation using machine learning, while the quantum component adapts the QBoost algorithm to build an ensemble of classical learners. The algorithm is successfully applied to solve the 1D Burgers' equation with viscosity, demonstrating the improved solutions compared to classical weak-learners, and implemented on D-Wave Systems with good performance compared to other methods.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Computer Science, Theory & Methods
Kelvin Buno, Henry Adorna
Summary: In this study, a string rewriting P system with communication by request is used to solve the satisfiability problem (SAT). The solutions derived from this class of rewriting P system are shown to be uniform in terms of the number of clauses and variables in the input boolean formula. This rewriting P system is then utilized as components in constructing a distributed P system that solves SAT. It is demonstrated that encoding the truth assignment for the communication steps only requires a polynomial number of rules, but achieving a low communication cost requires an exponential-sized set of inter-component communication rules.
THEORETICAL COMPUTER SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Pablo Sanabria, Tomas Felipe Tapia, Rodrigo Toro Icarte, Andres Neyem
Summary: This paper proposes using artificial intelligence to distribute jobs in Dew computing environments. It demonstrates that an AI agent, Proximal Policy Optimization (PPO), can learn to distribute jobs better than existing methods in a simulated Dew environment, even with longer job sequences during testing. The results show a performance improvement of up to 77% compared to human-designed heuristics.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Theory & Methods
Songhai Fan, Yiyu Gong, Gexiang Zhang, Yun Xiao, Haina Rong, Prithwineel Paul, Xiaomin Ma, Han Huang, Marian Gheorghe
Summary: As a newly introduced variant of P systems, kernel P systems combine the features of several kinds of P systems and provide a coherent view on the integration of different P systems into the same formalism. The implementation of kP systems in CUDA for solving various problems, including NP-hard problems, shows an increase in speed of about 5% for the parallel variant compared to the normal CPU implementation.
INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Wei Huang, Jiaxiang Li, Shuming Jiao, Zibang Zhang
Summary: This paper proposes an evolutionary single-pixel imaging (SPI) scheme for solving combinational optimization problems. SPI is a unique optical imaging technique that replaces the pixelated sensor array with a single-pixel detector. In this study, SPI is used to process types of data other than images, such as number partition and graph maximum cut. The Ising machine model is implemented optically with SPI, where the illumination patterns are updated through selection, crossover, and mutation based on the feedback of single-pixel values.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2023)
Article
Computer Science, Theory & Methods
David Orellana-Martin, Luis Valencia-Cabrera, Mario J. Perez-Jimenez
Summary: P systems are computing devices based on sets of rules to efficiently solve NP-complete problems. This work improves a previous result and provides an efficient solution for solving QBF-SAT or QSAT problems.
THEORETICAL COMPUTER SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Mingyu Yang, Ji-Hoon Kang, Ki-Ha Kim, Oh-Kyoung Kwon, Jung-Il Choi
Summary: We introduce an updated library, PaScaL_TDMA 2.0, which is capable of exploiting multi-GPU environments. The library extends its functionality to include GPU support and minimizes CPU-GPU data transfer by utilizing device-resident memory while retaining the original CPU-based capabilities. The library employs pipeline copying with shared memory for low-latency memory access and incorporates CUDA-aware MPI for efficient multi-GPU communication. Our GPU implementation demonstrated outstanding computational performance compared to the original CPU implementation while consuming much less energy.
COMPUTER PHYSICS COMMUNICATIONS
(2023)
Article
Engineering, Multidisciplinary
Gopika Ajith, Debraj Ghosh
Summary: In this work, a scalable method based on domain decomposition is proposed for solving large-scale nonlinear stochastic mechanics problems. The mechanics problem and random fields are discretized using finite element bases and Karhunen-Loeve expansion, respectively. A surrogate model based on stochastic collocation is built for each subdomain to solve deterministic nonlinear problems. Numerical tests show that the proposed method is computationally efficient and accurate for plasticity problems, with good scalability in parallel implementation.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2022)
Review
Mathematics, Applied
Mikhail Ronkin, Elena N. Akimova, Vladimir E. Misilov
Summary: One of the major challenges in the mining industry is estimating resource yield from visual data. This task, known as rock fragmentation estimation, involves identifying rock chunk distribution parameters to estimate blasting quality and other mining parameters. It is crucial for achieving optimal operational efficiency, cost reduction, and profit maximization. Most recent advancements in computer vision and neural networks have been applied to solve this task and the efficient utilization of computing power is essential for real-time operation.
Article
Computer Science, Interdisciplinary Applications
Soyoon Bak, Philsu Kim, Sangbeom Park
Summary: The study introduces a novel CUDA algorithm C-ECM3 for solving a 3D guiding center problem, which significantly improves computational speed and accuracy through decomposition strategy and the use of Cramer's rule in kernel function design.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Yulin Xiong, Zi-Long Zhao, Hongjia Lu, Wei Shen, Yi Min Xie
Summary: In this study, a parallel BESO method is developed to solve high-resolution topology optimisation problems. Significant improvements have been made to the efficiency of the finite element analysis and the filtering process. The developed method has been demonstrated to efficiently solve problems with more than 100 million tetrahedron elements.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Quantum Science & Technology
Elijah Pelofske, Georg Hahn, Hristo N. N. Djidjev
Summary: Quantum annealing has the potential to solve NP-hard problems, but the current hardware is limited in size, which hinders solving larger optimization problems. In this research, we demonstrate that a hybrid approach combining parallel quantum annealing with graph decomposition can accurately solve the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.
QUANTUM INFORMATION PROCESSING
(2023)
