Relevant bibliographies by topics / Linear Algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Linear Algorithms"

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Patel, Roshni V., and Jignesh S. Patel. "Optimization of Linear Equations using Genetic Algorithms." Indian Journal of Applied Research 2, no. 3 (October 1, 2011): 56–58. http://dx.doi.org/10.15373/2249555x/dec2012/19.

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Eaves, B. Curtis, and Uriel G. Rothblum. "Linear Problems and Linear Algorithms." Journal of Symbolic Computation 20, no. 2 (August 1995): 207–14. http://dx.doi.org/10.1006/jsco.1995.1047.

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LAWLOR, DAVID, YANG WANG, and ANDREW CHRISTLIEB. "ADAPTIVE SUB-LINEAR TIME FOURIER ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 01 (January 2013): 1350003. http://dx.doi.org/10.1142/s1793536913500039.

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We present a new deterministic algorithm for the sparse Fourier transform problem, in which we seek to identify k ≪ N significant Fourier coefficients from a signal of bandwidth N. Previous deterministic algorithms exhibit quadratic runtime scaling, while our algorithm scales linearly with k in the average case. Underlying our algorithm are a few simple observations relating the Fourier coefficients of time-shifted samples to unshifted samples of the input function. This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unaliased frequencies. We show that empirically our algorithm is orders of magnitude faster than competing algorithms.
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Galperin, E. A. "Linear time algorithms for linear programming." Computers & Mathematics with Applications 37, no. 4-5 (February 1999): 199–208. http://dx.doi.org/10.1016/s0898-1221(99)00069-3.

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Askar, S. S., and A. A. Karawia. "On Solving Pentadiagonal Linear Systems via Transformations." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/232456.

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Many authors have studied numerical algorithms for solving the linear systems of pentadiagonal type. The well-known fast pentadiagonal system solver algorithm is an example of such algorithms. The current paper describes new numerical and symbolic algorithms for solving pentadiagonal linear systems via transformations. The proposed algorithms generalize the algorithms presented in El-Mikkawy and Atlan, 2014. Our symbolic algorithms remove the cases where the numerical algorithms fail. The computational cost of our algorithms is better than those algorithms in literature. Some examples are given in order to illustrate the effectiveness of the proposed algorithms. All experiments are carried out on a computer with the aid of programs written in MATLAB.
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Čiegis, Raimondas, Remigijus Čiegis, Alexander Jakušev, and Gailė Šaltenienė. "PARALLEL VARIATIONAL ITERATIVE LINEAR SOLVERS." Mathematical Modelling and Analysis 12, no. 1 (March 31, 2007): 1–16. http://dx.doi.org/10.3846/1392-6292.2007.12.1-16.

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In this work we consider parallel variational algorithms for solution of linear systems. Theoretical analysis explains the superlinear convergence rate for two step gradient descent method. A new modification of the algorithm is proposed. Results of computational experiments are given for a linear system of equations approximating 3D elliptic boundary value problem. All algorithms are implemented using parallel array object tool ParSol, then a parallel algorithm follows semi‐automatically from the serial one. Results of the scalability analysis are presented and the efficiency of the presented parallel algorithm is investigated experimentally.
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Silva, Jair, Carla T. L. S. Ghidini, Aurelio R. L. Oliveira, and Marta I. V. Fontova. "A Comparison Among Simple Algorithms for Linear Programming." TEMA (São Carlos) 19, no. 2 (September 12, 2018): 305. http://dx.doi.org/10.5540/tema.2018.019.02.305.

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This paper presents a comparison between a family of simple algorithms for linear programming and the optimal pair adjustment algorithm. The optimal pair adjustment algorithm improvements the convergence of von Neumann's algorithm which is very attractive because of its simplicity. However, it is not practical to solve linear programming problems to optimality, since its convergence is slow. The family of simple algorithms results from the generalization of the optimal pair adjustment algorithm, including a parameter on the number of chosen columns instead of just a pair of them. Such generalization preserves the simple algorithms nice features. Significant improvements over the optimal pair adjustment algorithm were demonstrated through numerical experiments on a set of linear programming problems.
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Packel, Edward W. "Do Linear Problems Have Linear Optimal Algorithms?" SIAM Review 30, no. 3 (September 1988): 388–403. http://dx.doi.org/10.1137/1030091.

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Creutzig, Jakob, and P. Wojtaszczyk. "Linear vs. nonlinear algorithms for linear problems." Journal of Complexity 20, no. 6 (December 2004): 807–20. http://dx.doi.org/10.1016/j.jco.2004.05.003.

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Ploskas, Nikolaos, Nikolaos Samaras, and Jason Papathanasiou. "A Decision Support System for Solving Linear Programming Problems." International Journal of Decision Support System Technology 6, no. 2 (April 2014): 46–62. http://dx.doi.org/10.4018/ijdsst.2014040103.

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Linear programming algorithms have been widely used in Decision Support Systems. These systems have incorporated linear programming algorithms for the solution of the given problems. Yet, the special structure of each linear problem may take advantage of different linear programming algorithms or different techniques used in these algorithms. This paper proposes a web-based DSS that assists decision makers in the solution of linear programming problems with a variety of linear programming algorithms and techniques. Two linear programming algorithms have been included in the DSS: (i) revised simplex algorithm and (ii) exterior primal simplex algorithm. Furthermore, ten scaling techniques, five basis update methods and eight pivoting rules have been incorporated in the DSS. All linear programming algorithms and methods have been implemented using MATLAB and converted to Java classes using MATLAB Builder JA, while the web interface of the DSS has been designed using Java Server Pages.
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Dissertations / Theses on the topic "Linear Algorithms"

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Wilbanks, John W. (John Winston). "Linear Unification." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc500971/.

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Efficient unification is considered within the context of logic programming. Unification is explained in terms of equivalence classes made up of terms, where there is a constraint that no equivalence class may contain more than one function term. It is demonstrated that several well-known "efficient" but nonlinear unification algorithms continually maintain the said constraint as a consequence of their choice of data structure for representing equivalence classes. The linearity of the Paterson-Wegman unification algorithm is shown largely to be a consequence of its use of unbounded lists of pointers for representing equivalences between terms, which allows it to avoid the nonlinearity of "union-find".
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Rettes, Julio Alberto Sibaja. "Robust algorithms for linear regression and locally linear embedding." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/22445.

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RETTES, Julio Alberto Sibaja. Robust algorithms for linear regression and locally linear embedding. 2017. 105 f. Dissertação (Mestrado em Ciência da Computação)- Universidade Federal do Ceará, Fortaleza, 2017.
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Nowadays a very large quantity of data is flowing around our digital society. There is a growing interest in converting this large amount of data into valuable and useful information. Machine learning plays an essential role in the transformation of data into knowledge. However, the probability of outliers inside the data is too high to marginalize the importance of robust algorithms. To understand that, various models of outliers are studied. In this work, several robust estimators within the generalized linear model for regression framework are discussed and analyzed: namely, the M-Estimator, the S-Estimator, the MM-Estimator, the RANSAC and the Theil-Sen estimator. This choice is motivated by the necessity of examining algorithms with different working principles. In particular, the M-, S-, MM-Estimator are based on a modification of the least squares criterion, whereas the RANSAC is based on finding the smallest subset of points that guarantees a predefined model accuracy. The Theil Sen, on the other hand, uses the median of least square models to estimate. The performance of the estimators under a wide range of experimental conditions is compared and analyzed. In addition to the linear regression problem, the dimensionality reduction problem is considered. More specifically, the locally linear embedding, the principal component analysis and some robust approaches of them are treated. Motivated by giving some robustness to the LLE algorithm, the RALLE algorithm is proposed. Its main idea is to use different sizes of neighborhoods to construct the weights of the points; to achieve this, the RAPCA is executed in each set of neighbors and the risky points are discarded from the corresponding neighborhood. The performance of the LLE, the RLLE and the RALLE over some datasets is evaluated.
Na atualidade um grande volume de dados é produzido na nossa sociedade digital. Existe um crescente interesse em converter esses dados em informação útil e o aprendizado de máquinas tem um papel central nessa transformação de dados em conhecimento. Por outro lado, a probabilidade dos dados conterem outliers é muito alta para ignorar a importância dos algoritmos robustos. Para se familiarizar com isso, são estudados vários modelos de outliers. Neste trabalho, discutimos e analisamos vários estimadores robustos dentro do contexto dos modelos de regressão linear generalizados: são eles o M-Estimator, o S-Estimator, o MM-Estimator, o RANSAC e o Theil-Senestimator. A escolha dos estimadores é motivada pelo principio de explorar algoritmos com distintos conceitos de funcionamento. Em particular os estimadores M, S e MM são baseados na modificação do critério de minimização dos mínimos quadrados, enquanto que o RANSAC se fundamenta em achar o menor subconjunto que permita garantir uma acurácia predefinida ao modelo. Por outro lado o Theil-Sen usa a mediana de modelos obtidos usando mínimos quadradosno processo de estimação. O desempenho dos estimadores em uma ampla gama de condições experimentais é comparado e analisado. Além do problema de regressão linear, considera-se o problema de redução da dimensionalidade. Especificamente, são tratados o Locally Linear Embedding, o Principal ComponentAnalysis e outras abordagens robustas destes. É proposto um método denominado RALLE com a motivação de prover de robustez ao algoritmo de LLE. A ideia principal é usar vizinhanças de tamanhos variáveis para construir os pesos dos pontos; para fazer isto possível, o RAPCA é executado em cada grupo de vizinhos e os pontos sob risco são descartados da vizinhança correspondente. É feita uma avaliação do desempenho do LLE, do RLLE e do RALLE sobre algumas bases de dados.
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Li, Zhentao. "Tree decompositions and linear time algorithms." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107654.

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This thesis concerns tree decompositions. Trees are one of the simplest and most well understood class of graphs. A tree decomposition of a graph improves our understanding of the graph in a similar way. For example, as a consequence of Robertson and Seymour's groundbreaking work in the theory of graph minors, there are linear time algorithms for NP-hard problem on graphs that admit a tree decomposition of a certain type. We classify existing tree decompositions and examine what makes a tree decomposition unique.The first result of this thesis is a linear time algorithm for building a tree decomposition for the class of graphs that exclude K5 as a minor. The second result is a significant modification to this algorithm which results in a linear time algorithm to construct the tree decomposition for graphs which exclude a special set of paths. These are vertex disjoint paths between two pairs of input vertices (s_1, t_1), (s_2, t_2), one from s_1 to t_1 and the other from s_2 to t_2.We then use these tree decompositions to improve the running time of existing algorithms and extend the allowed input of other algorithms from planar graphs to graphs that exclude K_5 as a minor.
Cette thèse traite de décompositions arborescentes. Les arbres font partie des classes de graphes les mieux comprises. La décomposition arborescente d'un graphe améliore notre compréhension de ce dernier. Par exemple, grâce aux travaux de Robertson et Seymour sur les mineurs d'un graphe, nous savons qu'il existe, pour des problèmes qui sont en général NP-difficiles, un algorithme linéaire pour les graphes admettant une certaine décomposition arborescente. Nous classons les décompositions arborescentes connues et déterminons les propiétés qui rendent cette décomposition unique.Comme premier résultat, nous donnons un algorithme linéaire pour construire une décomposition arborescente d'un graphe sans mineur du graphe complet K_5. Notre deuxième resultat repose sur une modification de cet algorithme afin d'obtenir un autre algorithme linéaire. Ce dernier permet la construction d'une décomposition arborescente d'un graphe qui ne contient pas deux chemins à sommets disjoints entre deux paires de sommets données (s_1, t_1) et (s_2, t_2).Nous utilisons ces deux décompositions pour améliorer le temps de calcul des algorithmes existants et modifions des algorithmes pour graphes planaires pour leur permettre de prendre comme donnée des graphes sans mineur K_5.
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Lee, Richard. "3D non-linear image restoration algorithms." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338227.

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TORTORELLI, MARCUS MAGNO FERNANDES. "CENTRAL PATH ALGORITHMS FOR LINEAR PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9405@1.

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Neste trabalho estudamos os algoritmos de Pontos Interiores para programação Linear. Publicados após o Algoritmo de Karmarkar. Que seguem, de algum modo, a Trajetória Central. São considerados tanto algoritmos Primais quanto Primais-Duais e também verificadas a eficácia da aplicação da metodologia de busca bidirecional. Estes métodos foram implementados e testados resolvendo um conjunto de problemas gerados aleatoriamente. Através da comparação dos resultados analisamos o desempenho das diferentes metodologias.
We study here the Interior Points Algorithms for Linear Programming, developed after Karmarkar s Algorithm, which follow the Central Path. Both Primal and Primal-dual Algorithms are considered and also the efficiency of applying a bidirecional Search procedure is verified. These methods were implemented and tested solving a set of randomly generated problems. Comparing these results we analyze the performance of the methodologies.
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Yodpinyanee, Anak. "Sub-linear algorithms for graph problems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120411.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 189-199).
In the face of massive data sets, classical algorithmic models, where the algorithm reads the entire input, performs a full computation, then reports the entire output, are rendered infeasible. To handle these data sets, alternative algorithmic models are suggested to solve problems under the restricted, namely sub-linear, resources such as time, memory or randomness. This thesis aims at addressing these limitations on graph problems and combinatorial optimization problems through a number of different models. First, we consider the graph spanner problem in the local computation algorithm (LCA) model. A graph spanner is a subgraph of the input graph that preserves all pairwise distances up to a small multiplicative stretch. Given a query edge from the input graph, the LCA explores a sub-linear portion of the input graph, then decides whether to include this edge in its spanner or not - the answers to all edge queries constitute the output of the LCA. We provide the first LCA constructions for 3 and 5-spanners of general graphs with almost optimal trade-offs between spanner sizes and stretches, and for fixed-stretch spanners of low maximum-degree graphs. Next, we study the set cover problem in the oracle access model. The algorithm accesses a sub-linear portion of the input set system by probing for elements in a set, and for sets containing an element, then computes an approximate minimum set cover: a collection of an approximately-minimum number of sets whose union includes all elements. We provide probe-efficient algorithms for set cover, and complement our results with almost tight lower bound constructions. We further extend our study to the LP-relaxation variants and to the streaming setting, obtaining the first streaming results for the fractional set cover problem. Lastly, we design local-access generators for a collection of fundamental random graph models. We demonstrate how to generate graphs according to the desired probability distribution in an on-the-fly fashion. Our algorithms receive probes about arbitrary parts of the input graph, then construct just enough of the graph to answer these probes, using only polylogarithmic time, additional space and random bits per probe. We also provide the first implementation of random neighbor probes, which is a basic algorithmic building block with applications in various huge graph models.
by Anak Yodpinyanee.
Ph. D.
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Kong, Seunghyun. "Linear programming algorithms using least-squares method." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-04012007-010244/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007.
Martin Savelsbergh, Committee Member ; Joel Sokol, Committee Member ; Earl Barnes, Committee Co-Chair ; Ellis L. Johnson, Committee Chair ; Prasad Tetali, Committee Member.
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Jamieson, Alan C. "Linear-time algorithms for edge-based problems." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1193079463/.

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Amir-Azizi, Siamak. "Linear filtering algorithms for Monte Carlo simulations." Thesis, University of Southampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280859.

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Pullan, Malcolm Craig. "Separated continuous linear programs : theory and algorithms." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260693.

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Books on the topic "Linear Algorithms"

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Sima, Vasile. Algorithms for linear-quadratic optimization. New York: M. Dekker, 1996.

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Mathematical algorithms for linear regression. Boston: Academic Press, 1991.

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Kontoghiorghes, Erricos John. Parallel Algorithms for Linear Models. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4571-2.

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Lee, P. Synthesizing linear-array algorithms from nested for loop algorithms. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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Panik, Michael J. Linear programming: Mathematics, theory and algorithms. Dordrecht: Kluwer Academic, 1996.

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Abdullah, Jalaluddin. Fixed point algorithms for linear programming. Birmingham: University of Birmingham, 1992.

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service), SpringerLink (Online, ed. Max-linear Systems: Theory and Algorithms. London: Springer-Verlag London Limited, 2010.

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Linear network optimization: Algorithms and codes. Cambridge, Mass: MIT Press, 1991.

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Panik, Michael J., ed. Linear Programming: Mathematics, Theory and Algorithms. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-3434-7.

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Butkovič, Peter. Max-linear Systems: Theory and Algorithms. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-299-5.

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Book chapters on the topic "Linear Algorithms"

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Kall, Peter, and János Mayer. "Algorithms." In Stochastic Linear Programming, 285–382. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7729-8_4.

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Bitan, Sara, and Shmuel Zaks. "Optimal linear broadcast." In Algorithms, 368–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52921-7_86.

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Korte, Bernhard, and Jens Vygen. "Linear Programming." In Algorithms and Combinatorics, 53–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56039-6_3.

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Rival, Ivan. "Linear Extensions." In Algorithms and Order, 481–82. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2639-4_17.

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Korte, Bernhard, and Jens Vygen. "Linear Programming." In Algorithms and Combinatorics, 49–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-21708-5_3.

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Korte, Bernhard, and Jens Vygen. "Linear Programming." In Algorithms and Combinatorics, 49–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-21711-5_3.

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Korte, Bernhard, and Jens Vygen. "Linear Programming." In Algorithms and Combinatorics, 51–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24488-9_3.

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Padberg, Manfred. "Simplex Algorithms." In Linear Optimization and Extensions, 49–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-12273-0_5.

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Padberg, Manfred. "Projective Algorithms." In Linear Optimization and Extensions, 239–308. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-12273-0_8.

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Padberg, Manfred. "Ellipsoid Algorithms." In Linear Optimization and Extensions, 309–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-12273-0_9.

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Conference papers on the topic "Linear Algorithms"

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Kalina, Jan, and Jurjen Duintjer Tebbens. "Algorithms for Regularized Linear Discriminant Analysis." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005234901280133.

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Patt-Shamir, Boaz, and Evyatar Yadai. "Non-Linear Ski Rental." In SPAA '20: 32nd ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3350755.3400280.

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Evans, B. L., and J. H. McClellan. "Algorithms for symbolic linear convolution." In Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/acssc.1994.471600.

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FREDET, A. "ALGORITHMS AROUND LINEAR DIFFERENTIAL EQUATIONS." In Proceedings of the International Conference. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812770752_0018.

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Helmicki, A. J., C. A. Jacobson, and C. N. Nett. "Identification in H∞: linear algorithms." In 1990 American Control Conference. IEEE, 1990. http://dx.doi.org/10.23919/acc.1990.4791160.

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Calvetti, Daniela, Salvatore Cuomo, Monica Pragliola, Erkki Somersalo, and Gerardo Toraldo. "Computational issues in linear multistep method particle filtering." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965321.

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Bodwin, Greg. "Linear Size Distance Preservers." In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974782.39.

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Grosslinger, Armin, and Stefan Schuster. "On Computing Solutions of Linear Diophantine Equations with One Non-linear Parameter." In 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2008. http://dx.doi.org/10.1109/synasc.2008.33.

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Cheung, Ho Yee, Lap Chi Lau, and Kai Man Leung. "Algebraic Algorithms for Linear Matroid Parity Problems." In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611973082.105.

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Iwata, Yoichi, Keigo Oka, and Yuichi Yoshida. "Linear-Time FPT Algorithms via Network Flow." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.127.

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Reports on the topic "Linear Algorithms"

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Stechel, E. B. Linear scaling algorithms: Progress and promise. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/285454.

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Subasi, Yigit. Quantum algorithms for linear systems of equations [Slides]. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1774402.

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3

Qi, Liqun, and Egon Balas. Linear-Time Separation Algorithms for the Three-Index Assignment Polytope. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada228854.

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Tapia, R. A., and Yin Zhang. An Optimal Basis Identification Technique for Interior-Point Linear Programming Algorithms. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada455258.

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Klema, V. Numerical Algorithms and Mathematical Software for Linear Control and Estimation Theory. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada157525.

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Ariyawansa, K. A. Parallel algorithms for stochastic linear programs: A summary of research performed. Office of Scientific and Technical Information (OSTI), January 1988. http://dx.doi.org/10.2172/6541143.

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Leininger, Matthew L., Ida Marie B. Nielsen, and Curtis L. Janssen. Scalable fault tolerant algorithms for linear-scaling coupled-cluster electronic structure methods. Office of Scientific and Technical Information (OSTI), October 2004. http://dx.doi.org/10.2172/920444.

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Carey, G. F., and D. M. Young. Parallel supercomputing: Advanced methods, algorithms, and software for large-scale linear and nonlinear problems. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10134847.

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Brown, Ross, Jason Pusey, Muthuvel Murugan, and Dy Le. Comparison of Performance Effectiveness of Linear Control Algorithms Developed for a Simplified Ground Vehicle Suspension System. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada543109.

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Demeure, Cedric J., and Louis L. Scharf. Lattice Algorithms for Computing QR and Cholesky Factors in the Least Squares Theory of Linear Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada196454.

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