Relevant bibliographies by topics / Splitting algorithm

Academic literature on the topic 'Splitting algorithm'

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Published: 11 March 2023

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Journal articles on the topic "Splitting algorithm"

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Luo, Zhi Kang, Huai Ying Sun, and De Wang. "An Improved SPRINT Algorithm." Advanced Materials Research 532-533 (June 2012): 1685–90. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1685.

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This paper presents an improved SPRINT algorithm. The original SPRINT algorithm is a scalable and parallelizable decision tree algorithm, which is a popular algorithm in data mining and machine learning communities. To improve the algorithm's efficiency, we propose an improved algorithm. Firstly, we select the splitting attributes and obtain the best splitting attribute from them by computing the information gain ratio of each attribute. After that, we calculate the best splitting point of the best splitting attribute. Since it avoids a lot of calculations of other attributes, the improved algorithm can effectively reduce the computation.
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Chen, Jinjian, Xingyu Luo, Yuchao Tang, and Qiaoli Dong. "Primal-Dual Splitting Algorithms for Solving Structured Monotone Inclusion with Applications." Symmetry 13, no. 12 (December 13, 2021): 2415. http://dx.doi.org/10.3390/sym13122415.

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This work proposes two different primal-dual splitting algorithms for solving structured monotone inclusion containing a cocoercive operator and the parallel-sum of maximally monotone operators. In particular, the parallel-sum is symmetry. The proposed primal-dual splitting algorithms are derived from two approaches: One is the preconditioned forward–backward splitting algorithm, and the other is the forward–backward–half-forward splitting algorithm. Both algorithms have a simple calculation framework. In particular, the single-valued operators are processed via explicit steps, while the set-valued operators are computed by their resolvents. Numerical experiments on constrained image denoising problems are presented to show the performance of the proposed algorithms.
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Hai, Trinh Ngoc, and Ngo Thi Thuong. "A new splitting algorithm for equilibrium problems and applications." Studia Universitatis Babes-Bolyai Matematica 67, no. 1 (March 10, 2022): 129–44. http://dx.doi.org/10.24193/subbmath.2022.1.09.

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"In this paper, we discuss a new splitting algorithm for solving equilibrium problems arising from Nash-Cournot oligopolistic equilibrium problems in electricity markets with non-convex cost functions. Under the strong pseudomonotonicity of the original bifunction and suitable conditions of the component bifunctions, we prove the strong convergence of the proposed algorithm. Our results improve and develop previously discussed extragradient-like splitting algorithms and general extragradient algorithms. We also present some numerical experiments and compare our algorithm with the existing ones."
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Asqool, Obada, Suhana Koting, and Ahmad Saifizul. "Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations." Sustainability 13, no. 24 (December 15, 2021): 13851. http://dx.doi.org/10.3390/su132413851.

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Malaysia has a high percentage of motorcycles. Due to lane-splitting, travel times of motorcycles are less than passenger cars at congestion. Because of this, collecting travel times using the media access control (MAC) address is not straightforward. Many outlier filtering algorithms for travel time datasets have not been evaluated for their capability to filter lane-splitting observations. This study aims to identify the best travel time filtering algorithms for the data containing lane-splitting observations and how to use the best algorithm. Two stages were adopted to achieve the objective of the study. The first stage validates the performance of the previous algorithms, and the second stage checks the sensitivity of the algorithm parameters for different days. The analysis uses the travel time data for three routes in Kuala Lumpur collected by Wi-Fi detectors in May 2018. The results show that the Jang algorithm has the best performance for two of the three routes, and the TransGuide algorithm is the best algorithm for one route. However, the parameters of Jang and TransGuide algorithms are sensitive for different days, and the parameters require daily calibration to obtain acceptable results. Using proper calibration of the algorithm parameters, the Jang and TransGuide algorithms produced the most accurate filtered travel time datasets compared to other algorithms
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Ashley, J., and B. H. Marcus. "A generalized state-splitting algorithm." IEEE Transactions on Information Theory 43, no. 4 (July 1997): 1326–38. http://dx.doi.org/10.1109/18.605603.

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Kripac, Jiri. "Algorithm for splitting planar faces." Computer-Aided Design 19, no. 6 (July 1987): 293–98. http://dx.doi.org/10.1016/0010-4485(87)90282-x.

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Walraven, F. F. Y., and P. V. Van Rompay. "An improved phase-splitting algorithm." Computers & Chemical Engineering 12, no. 8 (August 1988): 777–82. http://dx.doi.org/10.1016/0098-1354(88)80015-2.

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Zhang, Xiaoyu, Genxiang Chen, and Qi Zhang. "LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting." Electronics 11, no. 3 (January 30, 2022): 428. http://dx.doi.org/10.3390/electronics11030428.

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The iterative Fourier transform algorithm (IFTA) is widely used in various optical communication applications based on liquid crystal on silicon spatial light modulators. However, the traditional iterative method has many disadvantages, such as a poor effect, an inability to select an optimization direction, and the failure to consider zero padding or phase quantization. Moreover, after years of development, the emergence of various variant algorithms also makes it difficult for researchers to choose one. In this paper, a new intelligent hybrid algorithm that combines the IFTA and differential evolution algorithm is proposed in a novel way. The reliability of the proposed algorithm is verified by beam splitting, and the IFTA and symmetrical IFTA algorithms, for comparison, are introduced. The hybrid algorithm improves the defects above while considering the zero padding and phase quantization of a computer-generated hologram, which optimizes the directional optimization in the diffraction efficiency and the fidelity of the output beam and improves the results of these two algorithms. As a result, the engineers’ trouble in the selection of an algorithm has also been reduced.
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Zong, Chunxiang, Yuchao Tang, and Yeol Cho. "Convergence Analysis of an Inexact Three-Operator Splitting Algorithm." Symmetry 10, no. 11 (November 1, 2018): 563. http://dx.doi.org/10.3390/sym10110563.

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The three-operator splitting algorithm is a new splitting algorithm for finding monotone inclusion problems of the sum of three maximally monotone operators, where one is cocoercive. As the resolvent operator is not available in a closed form in the original three-operator splitting algorithm, in this paper, we introduce an inexact three-operator splitting algorithm to solve this type of monotone inclusion problem. The theoretical convergence properties of the proposed iterative algorithm are studied in general Hilbert spaces under mild conditions on the iterative parameters. As a corollary, we obtain general convergence results of the inexact forward-backward splitting algorithm and the inexact Douglas-Rachford splitting algorithm, which extend the existing results in the literature.
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Sun, Yong Yong, and Guang Qiu Huang. "Technique of Code Obfuscation Based on Class Splitting." Applied Mechanics and Materials 271-272 (December 2012): 636–40. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.636.

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The security of software is threatened by piracy, tampering and reverse engineering. Attackers attempt to get important algorithms and sensitive data of software by static analysis or reverse engineering. Software protection becomes an important problem. The algorithm of code obfuscation based on class splitting is proposed that uses of obfuscation technology. The definition of class splitting and realization on algorithm are described. At the same time, the performance on algorithm is analyzed. The obfuscation method enhances the degree of complication about program and static analysis becomes more difficult. The algorithm is applicable to object-oriented program with class structure.
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Dissertations / Theses on the topic "Splitting algorithm"

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Bot, Radu Ioan, Ernö Robert Csetnek, and Erika Nagy. "Solving systems of monotone inclusions via primal-dual splitting techniques." Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-108172.

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In this paper we propose an algorithm for solving systems of coupled monotone inclusions in Hilbert spaces. The operators arising in each of the inclusions of the system are processed in each iteration separately, namely, the single-valued are evaluated explicitly (forward steps), while the set-valued ones via their resolvents (backward steps). In addition, most of the steps in the iterative scheme can be executed simultaneously, this making the method applicable to a variety of convex minimization problems. The numerical performances of the proposed splitting algorithm are emphasized through applications in average consensus on colored networks and image classification via support vector machines.
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Salinger, David H. "A splitting algorithm for multistage stochastic programming with application to hydropower scheduling /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6749.

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Hendrich, Christopher. "Proximal Splitting Methods in Nonsmooth Convex Optimization." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-149548.

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This thesis is concerned with the development of novel numerical methods for solving nondifferentiable convex optimization problems in real Hilbert spaces and with the investigation of their asymptotic behavior. To this end, we are also making use of monotone operator theory as some of the provided algorithms are originally designed to solve monotone inclusion problems. After introducing basic notations and preliminary results in convex analysis, we derive two numerical methods based on different smoothing strategies for solving nondifferentiable convex optimization problems. The first approach, known as the double smoothing technique, solves the optimization problem with some given a priori accuracy by applying two regularizations to its conjugate dual problem. A special fast gradient method then solves the regularized dual problem such that an approximate primal solution can be reconstructed from it. The second approach affects the primal optimization problem directly by applying a single regularization to it and is capable of using variable smoothing parameters which lead to a more accurate approximation of the original problem as the iteration counter increases. We then derive and investigate different primal-dual methods in real Hilbert spaces. In general, one considerable advantage of primal-dual algorithms is that they are providing a complete splitting philosophy in that the resolvents, which arise in the iterative process, are only taken separately from each maximally monotone operator occurring in the problem description. We firstly analyze the forward-backward-forward algorithm of Combettes and Pesquet in terms of its convergence rate for the objective of a nondifferentiable convex optimization problem. Additionally, we propose accelerations of this method under the additional assumption that certain monotone operators occurring in the problem formulation are strongly monotone. Subsequently, we derive two Douglas–Rachford type primal-dual methods for solving monotone inclusion problems involving finite sums of linearly composed parallel sum type monotone operators. To prove their asymptotic convergence, we use a common product Hilbert space strategy by reformulating the corresponding inclusion problem reasonably such that the Douglas–Rachford algorithm can be applied to it. Finally, we propose two primal-dual algorithms relying on forward-backward and forward-backward-forward approaches for solving monotone inclusion problems involving parallel sums of linearly composed monotone operators. The last part of this thesis deals with different numerical experiments where we intend to compare our methods against algorithms from the literature. The problems which arise in this part are manifold and they reflect the importance of this field of research as convex optimization problems appear in lots of applications of interest.
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Birba, Delwende Eliane. "A Comparative study of data splitting algorithms for machine learning model selection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287194.

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Data splitting is commonly used in machine learning to split data into a train, test, or validation set. This approach allows us to find the model hyper-parameter and also estimate the generalization performance. In this research, we conducted a comparative analysis of different data partitioning algorithms on both real and simulated data. Our main objective was to address the question of how the choice of data splitting algorithm can improve the estimation of the generalization performance. Data splitting algorithms used in this study were variants of k-fold, Kennard-Stone, SPXY ( sample set partitioning based on joint x-y distance), and random sampling algorithm. Each algorithm divided the data into two subset, training/validation. The training set was used to fit the model and validation for the evaluation. We then analyzed the different data splitting algorithms based on the generalization performances estimated from the validation and the external test set. From the result, we noted that the important determinant for a good generalization is the size of the dataset. For all the data sample methods applied on small data set, the gap between the performance estimated on the validation and test set was significant. However, we noted that the gap reduced when there was more data in training or validation. Too many or few data in the training set can also lead to bad model performance. So it is importance to have a reasonable balance between the training/validation set sizes. In our study, KS and SPXY was the splitting algorithm with poor model performance estimation. Indeed these methods select the most representative samples to train the model, and poor representative samples are left for model performance estimation.
Datapartitionering används vanligtvis i maskininlärning för att dela data i en tränings, test eller valideringsuppsättning. Detta tillvägagångssätt gör det möjligt för oss att hitta hyperparametrar för modellen och även uppskatta generaliseringsprestanda. I denna forskning genomförde vi en jämförande analys av olika datapartitionsalgoritmer på både verkliga och simulerade data. Vårt huvudmål var att undersöka frågan om hur valet avdatapartitioneringsalgoritm kan förbättra uppskattningen av generaliseringsprestanda. Datapartitioneringsalgoritmer som användes i denna studie var varianter av k-faldig korsvalidering, Kennard-Stone (KS), SPXY (partitionering baserat på gemensamt x-y-avstånd) och bootstrap-algoritm. Varje algoritm användes för att dela upp data i två olika datamängder: tränings- och valideringsdata. Vi analyserade sedan de olika datapartitioneringsalgoritmerna baserat på generaliseringsprestanda uppskattade från valideringen och den externa testuppsättningen. Från resultatet noterade vi att det avgörande för en bra generalisering är storleken på data. För alla datapartitioneringsalgoritmer som använts på små datamängder var klyftan mellan prestanda uppskattad på valideringen och testuppsättningen betydande. Vi noterade emellertid att gapet minskade när det fanns mer data för träning eller validering. För mycket eller för litet data i träningsuppsättningen kan också leda till dålig prestanda. Detta belyser vikten av att ha en korrekt balans mellan storlekarna på tränings- och valideringsmängderna. I vår studie var KS och SPXY de algoritmer med sämst prestanda. Dessa metoder väljer de mest representativa instanserna för att träna modellen, och icke-representativa instanser lämnas för uppskattning av modellprestanda.
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Nagurney, Anna, and Alexander Eydeland. "A Splitting Equilibration Algorithm for the Computation of Large-Scale Constrained Matrix Problems; Theoretical Analysis and Applications." Massachusetts Institute of Technology, Operations Research Center, 1990. http://hdl.handle.net/1721.1/5316.

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In this paper we introduce a general parallelizable computational method for solving a wide spectrum of constrained matrix problems. The constrained matrix problem is a core problem in numerous applications in economics. These include the estimation of input/output tables, trade tables, and social/national accounts, and the projection of migration flows over space and time. The constrained matrix problem, so named by Bacharach, is to compute the best possible estimate X of an unknown matrix, given some information to constrain the solution set, and requiring either that the matrix X be a minimum distance from a given matrix, or that X be a functional form of another matrix. In real-world applications, the matrix X is often very large (several hundred to several thousand rows and columns), with the resulting constrained matrix problem larger still (with the number of variables on the order of the square of the number of rows/columns; typically, in the hundreds of thousands to millions). In the classical setting, the row and column totals are known and fixed, and the individual entries nonnegative. However, in certain applications, the row and column totals need not be known a priori, but must be estimated, as well. Furthermore, additional objective and subjective inputs are often incorporated within the model to better represent the application at hand. It is the solution of this broad class of large-scale constrained matrix problems in a timely fashion that we address in this paper. The constrained matrix problem has become a standard modelling tool among researchers and practitioners in economics. Therefore, the need for a unifying, robust, and efficient computational procedure for solving constrained matrix problems is of importance. Here we introduce a.n algorithm, the splitting equilibration algorithm, for computing the entire class of constrained matrix problems. This algorithm is not only theoretically justiflid, hilt l'n fi,1 vl Pnitsf htnh thP lilnlprxing s-trlrtilre of thpCp !arop-Cspe mrnhlem nn the advantages offered by state-of-the-art computer architectures, while simultaneously enhancing the modelling flexibility. In particular, we utilize some recent results from variational inequality theory, to construct a splitting equilibration algorithm which splits the spectrum of constrained matrix problems into series of row/column equilibrium subproblems. Each such constructed subproblem, due to its special structure, can, in turn, be solved simultaneously via exact equilibration in closed form. Thus each subproblem can be allocated to a distinct processor. \We also present numerical results when the splitting equilibration algorithm is implemented in a serial, and then in a parallel environment. The algorithm is tested against another much-cited algorithm and applied to input/output tables, social accounting matrices, and migration tables. The computational results illustrate the efficacy of this approach.
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Glaudin, Lilian. "Stratégies multicouche, avec mémoire, et à métrique variable en méthodes de point fixe pour l'éclatement d'opérateurs monotones et l'optimisation." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS119.

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Plusieurs stratégies sans liens apparents coexistent pour mettre en œuvre les algorithmes de résolution de problèmes d'inclusion monotone dans les espaces hilbertiens. Nous proposons un cadre synthétique permettant d'englober diverses approches algorithmiques pour la construction de point fixe, clarifions et généralisons leur théorie asymptotique, et concevons de nouveaux schémas itératifs pour l'analyse non linéaire et l'optimisation convexe. Notre méthodologie, qui est ancrée sur un modèle de compositions de quasicontractions moyennées, nous permet de faire avancer sur plusieurs fronts la théorie des algorithmes de point fixe et d'impacter leurs domaines d'applications. Des exemples numériques sont fournis dans le contexte de la restauration d'image, où nous proposons un nouveau point de vue pour la formulation des problèmes variationnels
Several apparently unrelated strategies coexist to implement algorithms for solving monotone inclusions in Hilbert spaces. We propose a synthetic framework for fixed point construction which makes it possible to capture various algorithmic approaches, clarify and generalize their asymptotic behavior, and design new iterative schemes for nonlinear analysis and convex optimization. Our methodology, which is anchored on an averaged quasinonexpansive operator composition model, allows us to advance the theory of fixed point algorithms on several fronts, and to impact their application fields. Numerical examples are provided in the context of image restoration, where we propose a new viewpoint on the formulation of variational problems
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Uygur, Ahmet Bilge. "A Non-iterative Pressure Based Algorithm For The Computation Of Reacting Radiating Flows." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608274/index.pdf.

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A non-iterative pressure based algorithm which consists of splitting the solution of momentum energy and species equations into a sequence of predictor-corrector stages was developed for the simulation of transient reacting radiating flows. A semi-discrete approach called the Method of Lines (MOL) which enables implicit time-integration at all splitting stages was used for the solution of conservation equations. The solution of elliptic pressure equation for the determination of pressure field was performed by a multi-grid solver (MUDPACK package). Radiation calculations were carried out by coupling previously developed gray and non-gray radiation models with the algorithm. A first order (global) reaction mechanism was employed to account for the chemistry. The predictions of the algorithm for the following test cases: i) non-isothermal turbulent pipe flow and ii) laminar methane-air diffusion flame
were benchmarked against experimental data and numerical solutions available in the literature and the capability of the code to predict transient solutions was demonstrated on these test cases. Favorable agreements were obtained for both test cases. The effect of radiation and non-gray treatment of the radiative properties were investigated on the second test case. It was found that incorporation of radiation has significant effect on Temeprature and velocity fields but its effect is limited in species predictions. Executions with both radiation models revealed that the non-gray radiation model considered in the present study produces similar results with the gray model at a considerably higher computational cost. The algorithm developed was found to be an efficient and versatile tool for the timedependent simulation of different flow scenarios constitutes the initial steps towards the computation of transient turbulent combustion.
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Karmouche, El Mostafa. "Modélisation complémentaire des signaux non-stationnaires." Nancy 1, 1993. http://www.theses.fr/1993NAN10015.

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Le travail présenté dans ce mémoire concerne la modélisation des signaux non-stationnaires. Dans une première partie, les différentes méthodes de modélisation et d'analyse de ces signaux sont passées en revue. Ensuite, deux méthodes de détection des non-stationnarités sont rappelées et nous introduisons une nouvelle. Basée sur inter-corrélation entre les vecteurs de paramètres du même modèle AR, mais considérés à deux instants d'échantillonnage successifs, cette méthode donne de très bons résultats. La deuxième partie présente les méthodes non-paramétriques d'analyse des signaux non-stationnaires. Articulée autour de la représentation de Wigner-Ville et ses variantes, cette partie montre les limitations et les difficultés d'interprétation de ces méthodes. Une troisième partie est consacrée aux méthodes paramétriques où sont décrits différents algorithmes récursifs adaptés aux situations localement stationnaires et non-stationnaires. La modélisation complémentaire est la contribution originale de ce mémoire. Elle consiste à décomposer spectralement le signal initial en deux signaux sous-complémentaires, au moyen d'un filtre passe-bas et d'un filtre passe-haut, puis à modéliser ces deux signaux sous forme d'un modèle AR dont les paramètres sont estimés au moyen d'une version du filtre de Kalman; enfin, les deux étapes précédentes permettent la synthèse du modèle global du signal de départ. Cette méthode modélise au mieux les signaux présentant des changements rapides au cours du temps
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Hiltunen, Sonja. "Proximal Splitting Algorithms for Disparity Estimation Using Convex Relaxation." Thesis, KTH, Ljud- och bildbehandling, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-55342.

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Darapuram, Rajasekhar Venkata. "AN INVESTIGATION OF FLUX-SPLITTING ALGORITHMS FOR CHEMICALLY REACTING FLOWS." MSSTATE, 2001. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04102001-104000/.

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This paper presents an investigation of seven different flux splitting algorithms for the discretization of inviscid fluxes, which are the primary source for the non-linear behavior (eg. shocks, contact discontinuities). The aim of the present work is to enhance the accuracy and robustness of CHEM, a three-dimensional flow solver, which is capable of simulating a wide range of flow conditions, including chemical non-equilibrium. Five different test cases cases are considered and thoroughly analyzed. The overall goal is to find a numerical scheme that can meet some stringent specifications of efficiency, accuracy and robustness on the widest possible spectrum of flow conditions.
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Books on the topic "Splitting algorithm"

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Siebert, Christian. A scalable parallel sorting algorithm using exact splitting. Aachen: Universitätsbibliothek der RWTH Aachen, 2011.

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NASA Dryden Flight Research Center., ed. A parallel, finite-volume algorithm for large-eddy simulation of turbulent flows. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1999.

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NASA Dryden Flight Research Center., ed. A parallel, finite-volume algorithm for large-eddy simulation of turbulent flows. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1999.

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Bui, Trong T. A parallel, finite-volume algorithm for large-eddy simulation of turbulent flows. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1999.

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NASA Dryden Flight Research Center., ed. A parallel, finite-volume algorithm for large-eddy simulation of turbulent flows. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1999.

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NASA Dryden Flight Research Center., ed. A parallel, finite-volume algorithm for large-eddy simulation of turbulent flows. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1999.

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Bauschke, Heinz H., Regina S. Burachik, and D. Russell Luke, eds. Splitting Algorithms, Modern Operator Theory, and Applications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25939-6.

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Anderson, W. Kyle. Three-dimensional multigrid algorithms for the flux-split Euler equations. Hampton, Va: Langley Research Center, 1988.

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1952-, Thomas James L., Whitfield David L, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Three-dimensional multigrid algorithms for the flux-split Euler equations. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1989.

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Anderson, W. Kyle. Three-dimensional multigrid algorithms for the flux-split Euler equations. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1989.

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Book chapters on the topic "Splitting algorithm"

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Dehne, Frank, Michael R. Fellows, and Frances A. Rosamond. "An FPT Algorithm for Set Splitting." In Graph-Theoretic Concepts in Computer Science, 180–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39890-5_16.

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Lokshtanov, Daniel, and Saket Saurabh. "Even Faster Algorithm for Set Splitting!" In Parameterized and Exact Computation, 288–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11269-0_24.

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Liu, Jun, and Kotagiri Ramamohanarao. "Improved Self-splitting Competitive Learning Algorithm." In Advances in Knowledge Discovery and Data Mining, 372–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_44.

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Lee, Jaeho, JoonYoung Park, Deok-Soo Kim, and HyunChan Lee. "Polyhedron Splitting Algorithm for 3D Layer Generation." In Computational Science and Its Applications – ICCSA 2004, 564–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24709-8_60.

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Khoder, Rami, Rola Naja, and Samir Tohme. "Performance Evaluation of Speed Platoon Splitting Algorithm." In Advances in Intelligent Systems and Computing, 31–42. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3750-9_3.

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Ahmed, Reyan, Stephen Kobourov, and Myroslav Kryven. "An FPT Algorithm for Bipartite Vertex Splitting." In Lecture Notes in Computer Science, 261–68. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22203-0_19.

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Ang, C. H., and T. C. Tan. "New linear node splitting algorithm for R-trees." In Advances in Spatial Databases, 337–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63238-7_38.

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Setzer, Simon. "Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage." In Lecture Notes in Computer Science, 464–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02256-2_39.

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Gorskikh, Olga. "Splitting Algorithm for Detecting Structural Changes in Predictive Relationships." In Advances in Data Mining. Applications and Theoretical Aspects, 405–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_30.

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Mola, Francesco, and Roberta Siciliano. "A Two-Stage Predictive Splitting Algorithm in Binary Segmentation." In Computational Statistics, 179–84. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_26.

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Conference papers on the topic "Splitting algorithm"

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Wang, Liping, and Wenhui Fan. "Research of Optimal Splitting Level of Splitting Algorithm." In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2018. http://dx.doi.org/10.1109/cybermatics_2018.2018.00154.

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Wu, Liang, Guang Tian, and Zhong Xie. "An Algorithm for Splitting Arbitrary Polygon." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.201.

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Montacié, Claude, and Marie-José Caraty. "A silence/noise/music/speech splitting algorithm." In 5th International Conference on Spoken Language Processing (ICSLP 1998). ISCA: ISCA, 1998. http://dx.doi.org/10.21437/icslp.1998-597.

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Shumilov, B. M., and S. M. Matanov. "Supercompact cubic multiwavelets and algorithm with splitting." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002586.

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"A Splitting Algorithm for Medical Image Denoising." In Special Session on Modelling and Simulation in Biology and Medicine. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004634407040709.

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Shaheen, Ahmed Maher Abed, and Kaydar M. Quboa. "Pole Splitting algorithm for UWB breast cancer imaging." In MELECON 2012 - 2012 16th IEEE Mediterranean Electrotechnical Conference. IEEE, 2012. http://dx.doi.org/10.1109/melcon.2012.6196462.

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Gore, Ashutosh Deepak, and Abhay Karandikar. "Power Controlled FCFS Splitting Algorithm for Wireless Networks." In MILCOM 2007 - IEEE Military Communications Conference. IEEE, 2007. http://dx.doi.org/10.1109/milcom.2007.4454867.

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Tanab, Manal El, Yasmine Fahmy, and Mohamed M. Khairy. "Opportunistic splitting algorithm for underlay cognitive radio networks." In 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2014. http://dx.doi.org/10.1109/icufn.2014.6876738.

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Kohle and Schonbauer. "Segregation algorithm: splitting the training set improves learning." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118505.

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Xiangping Qin and R. Berry. "A distributed splitting algorithm for exploiting multiuser diversity." In IEEE International Symposium on Information Theory, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isit.2003.1228385.

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Reports on the topic "Splitting algorithm"

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O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45842.

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Abstract:
This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
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