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Relevant bibliographies by topics / Low Discrepancy Sequence

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Academic literature on the topic 'Low Discrepancy Sequence'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Low Discrepancy Sequence"

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Tezuka, Shu. "Tractability of Multivariate Integration Using Low-Discrepancy Sequences." Uniform distribution theory 11, no. 2 (2016): 23–43. http://dx.doi.org/10.1515/udt-2016-0013.

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Abstract We propose a notion of (t, e, s)-sequences in multiple bases, which unifies the Halton sequence and (t, s)-sequences under one roof, and obtain an upper bound of their discrepancy consisting only of the leading term. By using this upper bound, we improve the tractability results currently known for the Halton sequence, the Niederreiter sequence, the Sobol’ sequence, and the generalized Faure sequence, and also give tractability results for the Xing-Niederreiter sequence and the Hofer-Niederreiter sequence, for which no results have been known so far.

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Bangyal, Waqas Haider, Kashif Nisar, Ag Asri Bin Ag. Ibrahim, Muhammad Reazul Haque, Joel J. P. C. Rodrigues, and Danda B. Rawat. "Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems." Applied Sciences 11, no. 16 (2021): 7591. http://dx.doi.org/10.3390/app11167591.

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Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. These algorithms can influence the convergence to find an efficient optimal solution. Mainly, for recognizing the importance of diversity, several researchers have worked on the performance for the improvement of metaheuristic algorithms. Population initialization is a vital factor in metaheuristic algorithms such as PSO and DE. Instead of applying the random distribution for the initializati

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Ashraf, Adnan, Sobia Pervaiz, Waqas Haider Bangyal, et al. "Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences." Applied Sciences 11, no. 17 (2021): 8190. http://dx.doi.org/10.3390/app11178190.

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To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead o

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Sadakane, Kunihiko, Nadia Takki-Chebihi, and Takeshi Tokuyama. "Combinatorics and algorithms for low-discrepancy roundings of a real sequence." Theoretical Computer Science 331, no. 1 (2005): 23–36. http://dx.doi.org/10.1016/j.tcs.2004.09.030.

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Pausinger, Florian, and Alev Topuzoğlu. "On the Discrepancy of Two Families of Permuted Van der Corput Sequences." Uniform distribution theory 13, no. 1 (2018): 47–64. http://dx.doi.org/10.1515/udt-2018-0003.

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Abstract A permuted van der Corput sequence $S_b^\sigma$ in base b is a one-dimensional, infinite sequence of real numbers in the interval [0, 1), generation of which involves a permutation σ of the set {0, 1,..., b − 1}. These sequences are known to have low discrepancy DN, i.e. $t\left({S_b^\sigma } \right): = {\rm{lim}}\,{\rm{sup}}_{N \to \infty } D_N \left({S_b^\sigma } \right)/{\rm{log}}\,N$ is finite. Restricting to prime bases p we present two families of generating permutations. We describe their elements as polynomials over finite fields 𝔽p in an explicit way. We use this characteriza

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Shinoda, M. "Quasi-Monte Carlo Simulation with Low-Discrepancy Sequence for Reinforced Soil Slopes." Journal of Geotechnical and Geoenvironmental Engineering 133, no. 4 (2007): 393–404. http://dx.doi.org/10.1061/(asce)1090-0241(2007)133:4(393).

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Pausinger, Florian. "On the Intriguing Search for Good Permutations." Uniform distribution theory 14, no. 1 (2019): 53–86. http://dx.doi.org/10.2478/udt-2019-0005.

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AbstractThe intriguing search for permutations that generate generalised van der Corput sequences with exceptionally small discrepancy forms an important part of the research work of Henri Faure. On the occasion of Henri’s 80th birthday we aim to survey (some of) his contributions over the last four decades which considerably improved our understanding of one-dimensional van der Corput sequences and inspired a lot of related work. We recall and compare the different approaches in the search for generalised van der Corput sequences with low discrepancy, i.e., using a single generating permutati

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Tahmasebi, Maryam, and Gholam Hossein Yari. "Minimal relative entropy for equivalent martingale measures by low-discrepancy sequence in Lévy process." Stochastics 92, no. 4 (2019): 595–612. http://dx.doi.org/10.1080/17442508.2019.1642339.

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Yuan, Xiukai, Zhenzhou Lu, Changcong Zhou, and Zhufeng Yue. "A novel adaptive importance sampling algorithm based on Markov chain and low-discrepancy sequence." Aerospace Science and Technology 29, no. 1 (2013): 253–61. http://dx.doi.org/10.1016/j.ast.2013.03.008.

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CARBONE, INGRID, MARIA RITA IACÒ, and ALJOŠA VOLČIČ. "A dynamical system approach to the Kakutani–Fibonacci sequence." Ergodic Theory and Dynamical Systems 34, no. 6 (2013): 1794–806. http://dx.doi.org/10.1017/etds.2013.20.

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AbstractIn this paper we consider the sequence of Kakutani’s $\alpha $-refinements corresponding to the inverse of the golden ratio (which we call the Kakutani–Fibonacci sequence of partitions) and associate to it an ergodic interval exchange (which we call the Kakutani–Fibonacci transformation) using the ‘cutting–stacking’ technique. We prove that the orbit of the origin under this map coincides with a low discrepancy sequence (which we call the Kakutani–Fibonacci sequence of points), which has also been considered by other authors.

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Dissertations / Theses on the topic "Low Discrepancy Sequence"

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Liu, Kwong Ip. "Digital net experimental designs, function interpolations using low discrepancy sequence and goodness of fit tests by discrepancy." HKBU Institutional Repository, 2007. http://repository.hkbu.edu.hk/etd_ra/807.

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Hörmann, Wolfgang, and Josef Leydold. "Importance Sampling to Accelerate the Convergence of Quasi-Monte Carlo." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/284/1/document.pdf.

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Importance sampling is a well known variance reduction technique for Monte Carlo simulation. For quasi-Monte Carlo integration with low discrepancy sequences it was neglected in the literature although it is easy to see that it can reduce the variation of the integrand for many important integration problems. For lattice rules importance sampling is of highest importance as it can be used to obtain a smooth periodic integrand. Thus the convergence of the integration procedure is accelerated. This can clearly speed up QMC algorithms for integration problems up to dimensions 10 to 12. (author's

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Chaudhary, Suneal K. "Acceleration of Monte Carlo methods using low discrepancy sequences." Diss., Restricted to subscribing institutions, 2004. http://proquest.umi.com/pqdweb?did=766110621&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Vesterinen, Niklas. "Discrepancy of sequences and error estimates for the quasi-Monte Carlo method." Thesis, Karlstads universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78525.

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We present the notions of uniform distribution and discrepancy of sequences contained in the unit interval, as well as an important application of discrepancy in numerical integration by way of the quasi-Monte Carlo method. Some fundamental (and other interesting) results with regards to these notions are presented, along with some detalied and instructive examples and comparisons (some of which not often provided by the literature). We go on to analytical and numerical investigations of the asymptotic behaviour of the discrepancy (in particular for the van der Corput-sequence), and for the ge

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Green, Robert C. II. "Novel Computational Methods for the Reliability Evaluation of Composite Power Systems using Computational Intelligence and High Performance Computing Techniques." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1338894641.

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Desrumaux, Pierre-François. "Méthodes statistiques pour l’estimation du rendement paramétrique des circuits intégrés analogiques et RF." Thesis, Montpellier 2, 2013. http://www.theses.fr/2013MON20126/document.

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De nombreuses sources de variabilité impactent la fabrication des circuits intégrés analogiques et RF et peuvent conduire à une dégradation du rendement. Il est donc nécessaire de mesurer leur influence le plus tôt possible dans le processus de fabrications. Les méthodes de simulation statistiques permettent ainsi d'estimer le rendement paramétrique des circuits durant la phase de conception. Cependant, les méthodes traditionnelles telles que la méthode de Monte Carlo ne sont pas assez précises lorsqu'un faible nombre de circuits est simulé. Par conséquent, il est nécessaire de créer un estima

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Tang, Kuang-Hui, and 唐光輝. "The Study of the Pricing of Motgage-Backed Securities - The Application of Low Discrepancy Sequence." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/96161357327342016474.

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碩士<br>國立交通大學<br>經營管理研究所<br>91<br>Taiwan’s financial market has faced the problem of high non-performing loan ratio for financial institutions. Moreover, the more open economy resulting from Taiwan’s ascending the internationalization of financial markets is forcing Taiwan to develop the mortgage -backed securities (MBS). Up to the present, only limited researches have ever investigated the pricing of mortgage-backed securities. While the literature has devoted less attention to the numerical methods, the objective of this research is to apply the low discrepancy sequence method

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Liu, Kai. "Directional Control of Generating Brownian Path under Quasi Monte Carlo." Thesis, 2012. http://hdl.handle.net/10012/6984.

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Quasi-Monte Carlo (QMC) methods are playing an increasingly important role in computational finance. This is attributed to the increased complexity of the derivative securities and the sophistication of the financial models. Simple closed-form solutions for the finance applications typically do not exist and hence numerical methods need to be used to approximate their solutions. QMC method has been proposed as an alternative method to Monte Carlo (MC) method to accomplish this objective. Unlike MC methods, the efficiency of QMC-based methods is highly dependent on the dimensionality of the pro

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Pollanen, Marco Daniel. "Low-discrepancy sequences in probability spaces." 2005. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=370969&T=F.

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Books on the topic "Low Discrepancy Sequence"

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Pollanen, Marco Daniel. Low-discrepancy sequences in probability spaces. 2005.

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Book chapters on the topic "Low Discrepancy Sequence"

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Jordanov, Ivan, and Robert Brown. "Neural Network Learning Using Low-Discrepancy Sequence." In Advanced Topics in Artificial Intelligence. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46695-9_22.

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Gupta, Shubham Kumar, Himanshu Gupta, Sagar Arora, Pranshu Nayak, and Tanmay Shrivastava. "Efficient Initialization of Particle Swarm Optimization Using Low Discrepancy Sequence." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60618-7_43.

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Sadakane, Kunihiko, Nadia Takki-Chebihi, and Takeshi Tokuyama. "Combinatorics and Algorithms on Low-Discrepancy Roundings of a Real Sequence." In Automata, Languages and Programming. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48224-5_14.

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Monica, Tentu, Anguluri Rajasekhar, Millie Pant, and Ajith Abraham. "Enhancing the Local Exploration Capabilities of Artificial Bee Colony Using Low Discrepancy Sobol Sequence." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22606-9_19.

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Davies, T. J. G., R. R. Martin, and A. Bowyer. "Computing Volume Properties Using Low-Discrepancy Sequences." In Geometric Modelling. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6270-5_4.

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Niederreiter, Harald, and Chaoping Xing. "The Algebraic-Geometry Approach to Low-Discrepancy Sequences." In Monte Carlo and Quasi-Monte Carlo Methods 1996. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1690-2_8.

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Keller, Alexander, and Leonhard Grünschloß. "Parallel Quasi-Monte Carlo Integration by Partitioning Low Discrepancy Sequences." In Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27440-4_27.

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Brandolini, Luca, Leonardo Colzani, Giacomo Gigante, and Giancarlo Travaglini. "Low-Discrepancy Sequences for Piecewise Smooth Functions on the Torus." In Contemporary Computational Mathematics - A Celebration of the 80th Birthday of Ian Sloan. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72456-0_8.

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Faure, Henri. "Improvements on Low Discrepancy One-Dimensional Sequences and Two-Dimensional Point Sets." In Monte Carlo and Quasi-Monte Carlo Methods 2006. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-74496-2_19.

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Sánchez, Abraham, and Maria A. Osorio. "On the Use of Randomized Low-Discrepancy Sequences in Sampling-Based Motion Planning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11579427_100.

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Conference papers on the topic "Low Discrepancy Sequence"

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Pugazhendhi, K., and A. K. Dhingra. "Structural Reliability Analysis With Cross Entropy and Low Discrepancy Sampling Methods." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65913.

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In recent years quasi Monte-Carlo (QMC) techniques are gaining more popularity for reliability evaluation because of their increased accuracy over traditional Monte-Carlo simulation. A QMC technique like Low Discrepancy Sequence (LDS) combined with importance sampling is shown to be more accurate and robust in the past for the evaluation of structural reliability. However, one of the challenges in using importance sampling techniques to evaluate the structural reliability is to identify the optimum sampling density. In this article, a novel technique based on a combination of cross entropy and

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Georges, Didier. "A Variational Calculus Approach to Wildfire Monitoring Using a Low-Discrepancy Sequence-Based Deployment of Sensors." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029962.

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Farmer, Joseph A., and Somesh P. Roy. "A PHOTON MONTE CARLO SOLVER UTILIZING A LOW DISCREPANCY SEQUENCE FOR THERMAL RADIATION IN COMBUSTION SYSTEMS." In Proceedings of the 9th International Symposium on Radiative Transfer, RAD-19. Begellhouse, 2019. http://dx.doi.org/10.1615/rad-19.10.

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Nguyen, Xuan H., Quang U. Nguyen, and Robert I. McKay. "PSO with randomized low-discrepancy sequences." In the 9th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1276987.

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Kimura, Shuhei, and Koki Matsumura. "Genetic algorithms using low-discrepancy sequences." In the 2005 conference. ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068225.

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Nair, Dinesh, and Lothar Wenzel. "Image processing and low-discrepancy sequences." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Franklin T. Luk. SPIE, 1999. http://dx.doi.org/10.1117/12.367625.

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Pant, Millie, Radha Thangaraj, Crina Grosan, and Ajith Abraham. "Improved Particle Swarm Optimization with low-discrepancy sequences." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631204.

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De Rainville, François-Michel, Christian Gagné, Olivier Teytaud, and Denis Laurendeau. "Optimizing low-discrepancy sequences with an evolutionary algorithm." In the 11th Annual conference. ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570101.

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Najafi, M. Hassan, David J. Lilja, and Marc Riedel. "Deterministic methods for stochastic computing using low-discrepancy sequences." In ICCAD '18: IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN. ACM, 2018. http://dx.doi.org/10.1145/3240765.3240797.

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Nguyen Quang Uy, Nguyen Xuan Hoai, RI McKay, and Pham Minh Tuan. "Initialising PSO with randomised low-discrepancy sequences: the comparative results." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424717.

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