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Books on the topic 'Algorithme du simplexe'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Orlin, James B. Polynomial dual network simplex algorithms. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1991.

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2

Orlin, James B. Polynomial dual network simplex algorithms. Dept. of Computer Science, Stanford University, 1991.

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3

Currie, James D. The complexity of the simplex algorithm. Carleton University, Mathematics and Statistics, 1985.

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4

The simple genetic algorithm: Foundations and theory. MIT Press, 1999.

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5

Computational techniques of the simplex method. Kluwer Academic Publishers, 2003.

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6

Iwata, Satoru. A simple combinatorial algorithm for submodular function minimization. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2008.

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7

Ahuja, Ravindra K. A fast and simple algorithm for the maximum flow problem. Sloan School of Management, Massachusetts Institute of Technology, 1988.

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8

Ahuja, Ravindra K. A fast and simple algorithm for the maximum flow problem. Sloan School of Management, Massachusetts Institute of Technology, 1987.

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9

Ahuja, Ravindra K. Improved primal simplex algorithms for shortest path, assignment and minimum cost flow problems. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1988.

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10

Riefenberg, Jennifer S. A simplex-method-based algorithm for determining the source location of microseismic events. Dept. of the Interior, 1989.

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11

Riefenberg, Jennifer S. A simplex-method-based algorithm for determining the source location of microseismic events. U.S. Dept. of the Interior, Bureau of Mines, 1989.

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12

Mandra, Yuliya, Elena Semencova, Sergey Griroriev, et al. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.

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The training manual is devoted to the problem of herpetic infection in dentistry and was developed taking into account world scientific and clinical practice, experience working on clinical recommendations of the Ministry of Health of the Russian Federation, as well as experimental, laboratory and clinical data obtained by the authors. This manual presents materials related to modern ideas about the etiology and pathogenesis of herpetic infection, modern diagnostic methods are highlighted, and current complex treatment algorithms are proposed, and clinical cases are presented. Recommended as a guide for practitioners of various specialties, clinical residents, senior students.

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13

Baybars, Ilker. A survey of inexact algorithms for the simple assembly line balancing problem. Carregii-Mellan University, Graduate School of Industrial Administration, 1985.

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14

Guibas, Leonidas J. Linear time algorithms for visibility and shortest path problems inside simple polygons. Courant Institute of Mathematical Sciences, New York University, 1986.

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15

Allum, Clark. An implementation of the cryptographic algorithms required by a simple secure security system. Oxford Brookes University, 2004.

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16

Sagaser, Michael Bernard. A computational comparison of the primal simplex and relaxation algorithms for solving minimum cost flow networks. Naval Postgraduate School, 1989.

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17

Abbott, Walter D. A simple, low overhead data compression algorithm for converting lossy processes to lossless. Naval Postgraduate School, 1993.

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18

Ghellinck, Guy de. An extension of Karmarkar's algorithm for solving a system of linear homogenous equations on the simplex. CORE, 1985.

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19

Hart, Sergiu. Simple adaptive strategies: From regret-matching to uncoupled dynamics. World Scientific Publishing Co. Pte. Ltd., 2012.

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20

Jerrum, Mark. A very simple algorithm for estimating the numberof k-colourings of a low-degree graph. LFCS, Dept. of Computer Science, University of Edinburgh, 1994.

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21

Yap, Chee K. An O(n log n) algorithm for the Voronoi diagram of a set of simple curve segments (preliminary version). Courant Institute of Mathematical Sciences, New York University, 1985.

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22

The Simplex Method Algorithms and Combinatorics. Springer, 1986.

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23

Wu, Youfeng. Parallel simplex algorithms and loop spreading. 1988.

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24

Boudreau, Joseph F., and Eric S. Swanson. Numerical quadrature. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0005.

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This chapter discusses the numerous applications of numerical quadrature (integration) in classical mechanics, in semiclassical approaches to quantum mechanics, and in statistical mechanics; and then describes several ways of implementing integration in C++, for both proper and improper integrals. Various algorithms are described and analyzed, including simple classical quadrature algorithms as well as those enhanced with speedups and convergence tests. Classical orthogonal polynomials, whose properties are reviewed, are the basis of a sophisticated technique known as Gaussian integration. Practical implementations require the roots of these polynomials, so an algorithm for finding them from three-term recurrence relations is presented. On the computational side, the concept of polymorphism is introduced and exploited (prior to the detailed treatment later in the text). The nondimensionalization of physical problems, which is a common and important means of simplifying a problem, is discussed using Compton scattering and the Schrödinger equation as an example.

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25

Allen, Michael P., and Dominic J. Tildesley. Molecular dynamics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0003.

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This chapter introduces the classical equations of motion for a system of molecules, and describes their solution by stable, accurate, time-stepping algorithms. Simple atomic systems, rigid molecules, and flexible molecules with and without constraints, are treated, with examples of program code. Quaternions are introduced as useful parameters for solving the rigid-body equations of motion of molecules. A simple example of a multiple timestep algorithm is given, and there is a brief summary of event-driven (hard-particle) dynamics. Examples of constant-temperature molecular dynamics using stochastic and deterministic methods are presented, and the corresponding constant-pressure molecular dynamics methods for fixed and variable box-shape are described. The molecular dynamics method is extended to the treatment of polarizable systems, and dynamical simulation of the grand canonical ensemble is mentioned.

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26

Sepil, Canan A. Dual simplex algorithms on network flow problems and extensions. 1987.

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27

Montuno, Delfin Yeo *. Algorithms on restricted classes of simple polygons. 1985.

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28

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Network growth algorithms. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0008.

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Growth processes are a fundamentally different approach compared to probability-driven exponential models covered in earlier chapters. This chapter studies how growth rules can be designed to mimic processes observed in the real world, and how the process can be mathematically analyzed in order to obtain information about the likely topological properties of the resulting networks. The configuration (stub joining) model is described, including a careful discussion of how bias can be introduced if backtracking is used instead of restarting if stubs join to form a self or double link. The second class of models looked at is preferential attachment. The simplest variants of this are analyzed with a master equation approach, in order to introduce this technique as a way of obtaining analytical information about the expected properties of the generated graphs. Extensive references are provided to the numerous variants and extensions of both of these models.

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29

Allen, Michael P., and Dominic J. Tildesley. Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0004.

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The estimation of integrals by Monte Carlo sampling is introduced through a simple example. The chapter then explains importance sampling, and the use of the Metropolis and Barker forms of the transition matrix defined in terms of the underlying matrix of the Markov chain. The creation of an appropriately weighted set of states in the canonical ensemble is described in detail and the method is extended to the isothermal–isobaric, grand canonical and semi-grand ensembles. The Monte Carlo simulation of molecular fluids and fluids containing flexible molecules using a reptation algorithm is discussed. The parallel tempering or replica exchange method for more efficient exploration of the phase space is introduced, and recent advances including solute tempering and convective replica exchange algorithms are described.

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30

S, Gillis, ed. Exploring artificial learning algorithms: Learning to stress Dutch simplex words. Universitaire Instelling Antwerpen, Departement Germaanse, Afdeling Linguïstiek, 1992.

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31

Military Antenna Design Using Simple and Competent Genetic Algorithms. Storming Media, 2004.

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32

Ahuja, Ravindra K., James B. Orlin, and Sloan School of Management. A Fast and Simple Algorithm for the Maximum Flow Problem. Palala Press, 2015.

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33

STR, a simple and efficient algorithm for R-Tree packing. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1997.

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34

D, Joslin Ronald, and Langley Research Center, eds. A simple parallel prefix algorithm for compact finite-difference schemes. National Aeronautics and Space Administration, Langley Research Center, 1993.

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35

Lakshmivarahan, S., and Sudarshan K. Dhall. Parallel Computing Using the Prefix Problem. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195088496.001.0001.

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The prefix operation on a set of data is one of the simplest and most useful building blocks in parallel algorithms. This introduction to those aspects of parallel programming and parallel algorithms that relate to the prefix problem emphasizes its use in a broad range of familiar and important problems. The book illustrates how the prefix operation approach to parallel computing leads to fast and efficient solutions to many different kinds of problems. Students, teachers, programmers, and computer scientists will want to read this clear exposition of an important approach.

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36

Matrices and Simplex Algorithms: A Textbook in Mathematical Programming and Its Associated Mathematical Topics. Springer, 2011.

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37

Heesterman, Aaart R. Matrices and Simplex Algorithms: A Textbook in Mathematical Programming and Its Associated Mathematical Topics. Springer, 2011.

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38

Davis, Thomas E. Toward an extrapolation of the simulated annealing convergence theory onto the simple genetic algorithm. 1991.

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39

Newman, Mark. Network search. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0018.

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This chapter gives a discussion of search processes on networks. It begins with a discussion of web search, including crawlers and web ranking algorithms such as PageRank. Search in distributed databases such as peer-to-peer networks is also discussed, including simple breadth-first search style algorithms and more advanced “supernode” approaches. Finally, network navigation is discussed at some length, motivated by consideration of Milgram's letter passing experiment. Kleinberg's variant of the small-world model is introduced and it is shown that efficient navigation is possible only for certain values of the model parameters. Similar results are also derived for the hierarchical model of Watts et al.

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40

Virginia, Torczon, and Langley Research Center, eds. A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. National Aeronautics and Space Administration, Langley Research Center, 1998.

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41

Virginia, Torczon, and Langley Research Center, eds. A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. National Aeronautics and Space Administration, Langley Research Center, 1998.

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42

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.

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43

Martin, Keith M. Historical Cryptosystems. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788003.003.0002.

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This chapter presents several historical cryptosystems. These are all relatively simple, and none are fit for modern use. These cryptosystems serve to illustrate the basic model of a cryptosystem, as well as introduce a number of important design principles for modern encryption algorithms. We demonstrate the importance of a large keyspace, randomness of ciphertext, and positional dependence. We also show how efficiency and security are often traded off against one another when designing a cryptosystem.

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44

Davis, Jimmy Allen. The Permutanomicon: The Design and Implementation of an Expert Algorithm to Rapidly Compute Simple Physical Relations. Trafford Publishing, 2006.

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45

Martinez-Hurtado, Eugenio Daniel, and María Luisa Mariscal Flores, eds. An Update on Airway Management. BENTHAM SCIENCE PUBLISHERS, 2020. http://dx.doi.org/10.2174/97898114323851200301.

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In recent years, there have been many advances in the safe management of the patient's airway, a cornerstone of anesthetic practice. An Update on Airway Management brings forth information about new approaches in airway management in many clinical settings. This volume analyzes and explains new preoperative diagnostic methods, algorithms, intubation devices, extubation procedures, novelties in postoperative management in resuscitation and intensive care units, while providing a simple, accessible and applicable reading experience that helps medical practitioners in daily practice. The comprehensive updates presented in this volume make this a useful reference for anesthesiologists, surgeons and EMTs at all levels. Key topics reviewed in this reference include: New airway devices, clinical management techniques, pharmacology updates (ASA guidelines, DAS algorithms, Vortex approach, etc.), Induced and awake approaches in different settings Updates on diagnostic accuracy of perioperative radiology and ultrasonography Airway management in different settings (nonoperating room locations and emergency rooms) Airway management in specific patient groups (for example, patients suffering from morbid obesity, obstetric patients and critical patients) Algorithms and traditional surgical techniques that include emergency cricothyrotomy and tracheostomy in ‘Cannot Intubate, Cannot Ventilate’ scenarios. Learning techniques to manage airways correctly, focusing on the combination of knowledge, technical abilities, decision making, communication skills and leadership Special topics such as difficult airway management registry, organization, documentation, dissemination of critical information, big data and databases

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46

Boudreau, Joseph F., and Eric S. Swanson. Many body dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0018.

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Specialized techniques for solving the classical many-body problem are explored in the context of simple gases, more complicated gases, and gravitating systems. The chapter starts with a brief review of some important concepts from statistical mechanics and then introduces the classic Verlet method for obtaining the dynamics of many simple particles. The practical problems of setting the system temperature and measuring observables are discussed. The issues associated with simulating systems of complex objects form the next topic. One approach is to implement constrained dynamics, which can be done elegantly with iterative methods. Gravitational systems are introduced next with stress on techniques that are applicable to systems of different scales and to problems with long range forces. A description of the recursive Barnes-Hut algorithm and particle-mesh methods that speed up force calculations close out the chapter.

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47

Jet Propulsion Laboratory (U.S.), ed. STAR (simple tool for automated reasoning): Tutorial guide and reference manual. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1985.

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48

Priest, Graham. Logic: A Very Short Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/actrade/9780198811701.001.0001.

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Logic is often perceived as having little to do with the rest of philosophy, and even less to do with real life. Logic: A Very Short Introduction shows how wrong this conception is. It explores the philosophical roots of the subject, explaining how modern formal logic deals with issues ranging from the existence of God and the reality of time to paradoxes of probability and decision theory. Along the way, the basics of formal logic are explained in simple, non-technical terms, showing that logic is a powerful and exciting part of modern philosophy. It also covers the subjects of algorithms and axioms, and proofs in mathematics.

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49

Voit, Eberhard O. Systems Biology: A Very Short Introduction. Oxford University Press, 2020. http://dx.doi.org/10.1093/actrade/9780198828372.001.0001.

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Systems biology came about as growing numbers of scientists and engineers from different fields created experimental methods and algorithms that supported the analysis of very large quantities of biological data. Systems Biology: A Very Short IntroductionÂ outlines the exciting processes and possibilities in this new field. It describes how modern biology enabled us to learn how intricately the expression of every gene is controlled, how signalling systems keep organisms running smoothly, how complicated even the simplest cells are, and how computational methods may help us understand these complex systems of life. It explores what this field is about, why it is needed, and how it will affect our understanding of life, particularly in the areas of personalized medicine, drug development, food and energy production, and sustainable stewardship of our environments.

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50

Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.

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