Mathematical theory of computation Books
Cambridge University Press DataDriven Computational Methods
Book SynopsisModern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB codes and appendiTrade Review'The MATLAB code used for the examples in the book can be downloaded from the publisher's website; the scripts are short, well commented and can be understood without difficulty (even if you are not a MATLAB expert).' Fabio Mainardi, MAA Reviews'… this book is useful for students or researchers entering in the topic of data assimilation or interested in statistical and computational methods for stochastic differential equations. It complements nicely other recent books in the field and gives a concise overview of some recent research activity in a very comprehensive style.' Nikolas Kantas, SIAM ReviewTable of Contents1. Introduction; 2. Markov chain Monte Carlo; 3. Ensemble Kalman filters; 4. Stochastic spectral methods; 5. Karhunen–Loève expansion; 6. Diffusion forecast; Appendix A. Elementary probability theory; Appendix B. Stochastic processes; Appendix C. Elementary differential geometry; References; Index.
£52.24
Cambridge University Press Mathematical Modelling of the Human Cardiovascular System
Book SynopsisMathematical and numerical modelling of the human cardiovascular system has attracted remarkable research interest due to its intrinsic mathematical difficulty and the increasing impact of cardiovascular diseases worldwide. This book addresses the two principal components of the cardiovascular system: arterial circulation and heart function. It systematically describes all aspects of the problem, stating the basic physical principles, analysing the associated mathematical models that comprise PDE and ODE systems, reviewing sound and efficient numerical methods for their approximation, and simulating both benchmark problems and clinically inspired problems. Mathematical modelling itself imposes tremendous challenges, due to the amazing complexity of the cardiovascular system and the need for computational methods that are stable, reliable and efficient. The final part is devoted to control and inverse problems, including parameter estimation, uncertainty quanti?cation and the developmenTrade Review'Overall, there is a nice interplay between the basic biology and physiology needed to understand the model, the pros and cons of different techniques of obtaining clinical data, and the implementation of the numerical methods. Each section includes beautifully colored schematic representations of the cardiovascular system …' Sarah Patterson, Mathematical Association of AmericaTable of ContentsIntroduction; Part I. Arterial Circulation: 1. Basic facts about quantitative physiology; 2. An insight into vascular data; 3. Modelling blood flow; Part II. Heart Function: 4. Basic facts on quantitative cardiac physiology; 5. An insight into cardiac data; 6. Modelling the heart; Part III. Optimization, Control, Uncertainty and Complexity Reduction: 7. Beyond direct simulation; 8. Control and optimization; 9. Parameter estimation from clinical data; 10. Accounting for uncertainty; 11. Reduced-order modelling; References; Index.
£76.94
Cambridge University Press FiniteState Techniques
Book SynopsisFinite-state methods are the most efficient mechanisms for analysing textual and symbolic data, providing elegant solutions for an immense number of practical problems in computational linguistics and computer science. This book for graduate students and researchers gives a complete coverage of the field, starting from a conceptual introduction and building to advanced topics and applications. The central finite-state technologies are introduced with mathematical rigour, ranging from simple finite-state automata to transducers and bimachines as ''input-output'' devices. Special attention is given to the rich possibilities of simplifying, transforming and combining finite-state devices. All algorithms presented are accompanied by full correctness proofs and executable source code in a new programming language, C(M), which focuses on transparency of steps and simplicity of code. Thus, by enabling readers to obtain a deep formal understanding of the subject and to put finite-state methodsTrade Review'… this volume is well written and very detailed. It is thus a nice reference for those results for the interested graduate or researcher …' Andreas Maletti, ZB Math ReviewsTable of ContentsPart I. Formal Background: 1. Formal preliminaries; 2. Monoidal finite-state automata; 3. Classical finite-state automata and regular languages; 4. Monoidal multi-tape automata and finite-state transducers; 5. Deterministic transducers; 6. Bimachines; Part II. From Theory to Practice: 7. The C(M) language; 8. C(M) implementation of finite-state devices; 9. The Aho–Corasick algorithm; 10. The minimal deterministic finite-state automaton for a finite language; 11. Constructing finite-state devices for text rewriting; Bibliography; Index.
£63.64
APress Architecture of Advanced Numerical Analysis
Book SynopsisThis unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library.You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a compTable of ContentsPrologueA Brief HistoryReductionism vs. HolismKey FeaturesContact MePART 1: NUMERICAL TECHNIQUES1. IntroductionWhat Is Scientific ComputingWhat is Functional ProgrammingWho Is This Book ForStructure of the BookInstallationOption 1: Install from OPAMOption 2: Pull from Docker HubOption 3: Pin the Dev-RepoOption 4: Compile from SourceCBLAS/LAPACKE DependencyInteracting with OwlUsing ToplevelUsing NotebookUsing Owl-JupyterSummary2. ConventionsPure vs. ImpureNdarray vs. ScalarInfix OperatorsOperator ExtensionModule StructuresNumber and PrecisionPolymorphic FunctionsModule ShortcutsType Casting3. VisualisationCreate PlotsSpecificationSubplotsMultiple LinesLegendDrawing PatternsLine PlotScatter PlotStairs PlotBox PlotStem PlotArea PlotHistogram & CDF PlotLog Plot3D PlotAdvanced Statistical PlotSummaryReferences4. Mathematical FunctionsBasic FunctionsBasic Unary Math FunctionsBasic Binary FunctionsExponential and Logarithmic FunctionsTrigonometric FunctionsOther Math FunctionsSpecial FunctionsAiry FunctionsBessel FunctionsElliptic FunctionsGamma FunctionsBeta FunctionsStruve FunctionsZeta FunctionsError FunctionsIntegral FunctionsFactorialsInterpolation and ExtrapolationIntegrationUtility FunctionsSummary5. Statistical FunctionsRandom VariablesDiscrete Random VariablesContinuous Random VariablesDescriptive StatisticsOrder StatisticsSpecial DistributionGamma DistributionBeta DistributionChi-Square DistributionStudent-t DistributionCauchy DistributionMultiple VariablesSamplingHypothesis TestsTheoryGaussian Distribution in Hypothesis TestingTwo-Sample InferencesGoodness-of-fit TestsNon-parametric StatisticsCovariance and CorrelationsAnalysis of VarianceSummary6. N-Dimensional ArraysNdarray TypesCreation FunctionsProperties FunctionsMap FunctionsFold FunctionsScan FunctionsComparison FunctionsVectorised FunctionsIteration FunctionsManipulation FunctionsSerialisationTensorsSummaryReferences7. Slicing and BroadcastingSlicingBasic SlicingFancy SlicingConventions in DefinitionExtended OperatorsAdvanced UsageBroadcastingWhat Is Broadcasting?Shape ConstraintsSupported OperationsSlicing in NumPy and JuliaInternal MechanismSummary8. Linear AlgebraVectors and MatricesCreating MatricesAccessing ElementsIterate, Map, Fold, and FilterMath OperationsGaussian EliminationLU FactorisationInverse and TransposeVector SpacesRank and BasisOrthogonalitySolving Ax = bMatrix SensitivityDeterminantsEigenvalues and EigenvectorsSolving Ax=λ xComplex MatricesSimilarity Transformation and DiagonalisationPositive Definite MatricesPositive DefinitenessSingular Value DecompositionInternal: CBLAS and LAPACKELow-level Interface to CBLAS & LAPACKESparse MatricesSummaryReferences9. Ordinary Differential EquationsWhat Is An ODEExact SolutionsLinear SystemsSolving An ODE NumericallyOwl-ODEExample: Linear Oscillator SystemSolver StructureSymplectic SolversFeatures and LimitsExamples of using Owl-ODEExplicit ODETwo Body ProblemLorenz AttractorDamped OscillationStiffnessSolve Non-Stiff ODEsSolve Stiff ODEsSummaryReferences10. Signal ProcessingDiscrete Fourier TransformFast Fourier TransformExamplesApplications of FFTFind period of sunspotsDecipher the ToneImage ProcessingFilteringExample: SmoothingGaussian FilterSignal ConvolutionFFT and Image ConvolutionSummaryReferences11. Algorithmic DifferentiationChain RuleDifferentiation MethodsHow Algorithmic Differentiation WorksForward ModeReverse ModeForward or Reverse?A Strawman AD EngineSimple Forward ImplementationSimple Reverse ImplementationUnified ImplementationsForward and Reverse Propagation APIExpressing ComputationExample: Forward ModeExample: Reverse ModeHigh-Level APIsDerivative and GradientJacobianHessian and LaplacianOther APIsInternal of Algorithmic DifferentiationGo Beyond Simple ImplementationExtend AD moduleLazy EvaluationSummaryReferences12. OptimisationIntroductionRoot FindingUnivariate Function OptimisationUse DerivativesGolden Section SearchMultivariate Function OptimisationNelder-Mead Simplex MethodGradient Descent MethodsConjugate Gradient MethodNewton and Quasi-Newton MethodsGlobal Optimisation and Constrained OptimisationSummaryReferences13. RegressionLinear RegressionProblem: Where to locate a new McDonald’s restaurant?Cost FunctionSolving Problem with Gradient DescentMultiple RegressionFeature NormalisationAnalytical SolutionNon-linear regressionsRegularisationOls, Ridge, Lasso, and Elastic_netLogistic RegressionSigmoid FunctionCost FunctionExampleMulti-class classificationSupport Vector MachineKernel and Non-linear BoundaryExampleModel error and selectionError MetricsModel SelectionSummaryReferences14. Deep Neural NetworksPerceptronYet Another RegressionModel RepresentationForward PropagationBack propagationFeed Forward NetworkLayersActivation FunctionsInitialisationTrainingTestNeural Network ModuleModule StructureNeuronsNeural GraphTraining ParametersConvolutional Neural NetworkRecurrent Neural NetworkLong Short Term Memory (LSTM)Generative Adversarial NetworkSummaryReferences15. Natural Language ProcessingIntroductionText CorpusStep-by-step OperationUse the Corpus ModuleVector Space ModelsBag of Words (BOW)Term Frequency–Inverse Document Frequency (TF-IDF)Latent Dirichlet Allocation (LDA)ModelsDirichlet DistributionGibbs SamplingTopic Modelling ExampleLatent Semantic Analysis (LSA)Search Relevant DocumentsEuclidean and Cosine SimilarityLinear SearchingSummaryReferences16. Dataframe for Tabular DataBasic ConceptsCreate FramesManipulate FramesQuery FramesIterate, Map, and FilterRead/Write CSV FilesInfer Type and SeparatorSummary17. Symbolic RepresentationIntroductionDesignCore abstractionEnginesONNX EngineExample 1: Basic operationsExample 2: Variable InitialisationExample 3: Neural networkLaTeX EngineOwl EngineSummary18. Probabilistic ProgrammingGenerative Model vs Discriminative ModelBayesian NetworksSampling TechniquesInferencePART 2: SYSTEM ARCHITECTURE19. Architecture OverviewIntroductionArchitecture OverviewCore ImplementationN-dimensional ArrayInterfaced LibrariesAdvanced FunctionalityComputation GraphAlgorithmic DifferentiationRegressionNeural NetworkParallel ComputingActor EngineGPU ComputingOpenMPCommunity-Driven R&DSummary20. Core OptimisationBackgroundNumerical LibrariesOptimisation of Numerical ComputationInterfacing to C CodeNdarray OperationsFrom OCaml to COptimisation TechniquesMap OperationsConvolution OperationsReduction OperationsRepeat OperationsSummaryReferences21. Automatic Empirical TuningWhat is Parameter TuningWhy Parameter Tuning in OwlHow to Tune OpenMP ParametersMake a DifferenceSummary22. Computation GraphIntroductionWhat is a Computation Graph?From Dynamic to StaticSignificance in ComputingExamplesExample 01: Basic CGraphExample 02: CGraph with ADExample 03: CGraph with DNNDesign RationaleOptimisation of CGraphOptimising memory with pebblesAllocation AlgorithmAs Intermediate RepresentationsSummary23. Scripting and Zoo SystemIntroductionShare Script with ZooTypical ScenarioCreate a ScriptShare via GistImport in Another ScriptSelect a Specific VersionCommand Line ToolMore ExamplesSystem DesignServicesType CheckingBackendDomain Specific LanguageService DiscoveryUse CaseSummaryReferences24. Compiler BackendsBase LibraryBackend: JavaScriptUse Native OCamlUse Facebook ReasonBackend: MirageOSMirageOS and UnikernelExample: Gradient DescentExample: Neural NetworkEvaluationSummary25. Distributed ComputingActor SystemDesignActor EnginesMap-Reduce EngineParameter Server EnginePeer-to-Peer EngineClassic Synchronise ParallelBulk Synchronous ParallelAsynchronous ParallelStale Synchronous ParallelProbabilistic Synchronise ParallelBasic idea: samplingCompatibilityBarrier Trade-off DimensionsConvergenceA Distributed Training ExampleStep ProgressAccuracySummaryReferences26. Testing FrameworkUnit TestExampleWhat Could Go WrongCorner CasesTest CoverageUse FunctorSummary27. Constants and Metric SystemWhat Is a Metric SystemFour Metric SystemsSI PrefixExample: Physics and Math constantsInternational System of UnitsTimeLengthAreaVolumeSpeedMassForceEnergyPowerPressureViscosityLuminanceRadioactivity28. Internal Utility ModulesDataset ModuleMNISTCIFAR-10Graph ModuleStack and Heap ModulesCount-Min SketchSummaryPART 3: CASE STUDIES29. Case - Image RecognitionBackgroundLeNetAlexNetVGGResNetSqueezeNetCapsule NetworkBuilding InceptionV3 NetworkInceptionV1 and InceptionV2FactorisationGrid Size ReductionInceptionV3 ArchitecturePreparing WeightsProcessing ImageRunning InferenceApplicationsSummaryReferences30. Case - Instance SegmentationIntroductionMask R-CNN NetworkBuilding Mask R-CNNFeature ExtractorProposal GenerationClassificationRun the CodeSummaryReferences31. Case - Neural Style TransferContent and StyleContent ReconstructionStyle RecreationCombining Content and StyleRunning NSTExtending NSTFast Style TransferBuilding FST NetworkRunning FSTSummaryReferences32. Case - Recommender SystemIntroductionArchitectureBuild Topic ModelsIndex Text CorpusRandom ProjectionOptimising Vector StorageOptimise Data StructureOptimise Index AlgorithmSearch ArticlesCode ImplementationMake It LiveSummaryReferences33. Case - Applications in FinanceIntroductionBond PricingBlack-Scholes ModelMathematical ModelOption PricingPortfolio OptimisationMathematical ModelEfficient FrontierMaximise Sharpe Ratio
£33.74
APress Options and Derivatives Programming in C23
Book SynopsisThis book is a hands-on guide for programmers who want to learn how C++ is used to develop solutions for options and derivatives trading in the financial industry. It explores the main algorithms and programming techniques used in implementing systems and solutions for trading options and derivatives. This updated edition will bring forward new advances in C++ software language and libraries, with a particular focus on the new C++23 standard. The book starts by covering C++ language features that are frequently used to write financial software for options and derivatives. These features include the STL (standard template library), generic templates, functional programming, and support for numerical code. Examples include additional support for lambda functions with simplified syntax, improvements in automatic type detection for templates, custom literals, modules, constant expressions, and improved initialization strategies for C++ objects. This book also provides how-to examples thaTable of Contents
£37.49
Nova Science Publishers Inc Monte Carlo Simulation: Methods, Assessment &
Book SynopsisChapter One presents a study on application of Monte Carlo simulation in reliability assessment of composite electric power systems. Chapter Two develops a PK/PD model to evaluate, by Monte Carlo simulation as a data maximisation strategy, the antiviral activity of two stavudine formulations: conventional stavudine and stavudine-gold nanoparticles (stavudine-AuNPs). In Chapter Three, the magnetic properties of the kagomé lattice is studied with RudermanKittelKasuyaYosida (RKKY) exchange interactions in a spin-7/2 and alternate mixed spin-5/2 and spin-2 Ising model on the Bethe lattice by using the Monte Carlo simulations.
£999.99
Nova Science Publishers Inc Horizons in Computer Science Research: Volume 15
Book Synopsis
£205.59
Nova Science Publishers Inc Scientific Computing & Applications
Book SynopsisScientific Computing & Applications
£85.59
Springer International Publishing AG Higher Education Computer Science: A Manual of
Book SynopsisThe march towards on-line and blended teaching—present before the Covid-19 pandemic—has been accelerated by it, and there is no going back. Students and staff may object, but the economic drive towards “greater productivity” will inevitably result in less face-to-face (f2f) instruction. Therefore, it is incumbent for those delivering this precious, in-person resource to make maximum use of time…which raises the question, “how”?The second edition of Higher Education Computer Science offers some potential answers. It also addresses other questions, such as “why have f2f teaching at all?” “what is the purpose of f2f?” and “what is the appropriate balance between the two?” The first edition began to offer suggestions for optimising limited opportunities to get together with students. Aligned with that, this unique new volume examines how to use the technology available to maximum advantage: For example, resources such as Moocs and other on-line instructional materials can provide invaluable pedagogic support. In addition, the book addresses ‘problem-based learning,’ using robotics in the teaching of programming, and a multidisciplinary approach to data science. Although it includes a chapter on distance learning, there is greater emphasis placed on the soft, transferable skills and employability skills that are best delivered in person. Further, the work provides several examples of putting theory into practice when teaching computer science at both undergraduate and postgraduate levels. Written by experienced practitioners, each chapter tackles a particular teaching activity or topic within computing, presented in such a way that other practitioners can use. As such, this new volume will be an invaluable resource to those who want to protect and optimise in-person teaching.Table of ContentsPart 1: Approaches to Learning.- Changing minds: Multitasking in lectures.- Active learning in large lectures.- The flipped classroom.- Applying cognitive theory to the teaching of programming: Metaphors, robots and problem-based learning.- Distance learning: Lessons learned from a UK masters programme.- Academic integrity for computer science instructors.- Contextualisation in data science.- Part 2: Teaching examples and practice.- Using graphics to inspire failing students.- Best practices for teaching information systems modelling.- Promoting design thinking through knowledge maps: A case study in computer games design and development education.- Teaching fuzzy logic utilising innovative approaches.- Semi-automating the marking of a Java programming portfolio assessment: A case study from a UK undergraduate programme.- Part 3: Employability and group work.- The enterprise showcase experience.- Task versus process: A taxonomy for group projects.- Realising the threshold of employability in higher education.- Exploring the landscape of HE industrial placements within engineering and technology subjects. Observations of recruitment, graduate attributes and student experience.- Baseline skills – Scaffolding soft skills development within the curriculum.- Professionalism and online presence.- Postscript.
£37.49
Clarendon Press A First Course in Coding Theory Paperback
Book SynopsisAlgebraic coding theory is a new and rapidly developing subject, motivated by immediate practical applications, but also rich in mathematical structure. This book provides an elementary yet rigorous introduction to the theory of error-correcting codes.
£75.05
Oxford University Press An Introduction to Quantum Computing
Book SynopsisThis concise, accessible text provides a thorough introduction to quantum computing - an exciting emergent field at the interface of the computer, engineering, mathematical and physical sciences. Aimed at advanced undergraduate and beginning graduate students in these disciplines, the text is technically detailed and is clearly illustrated throughout with diagrams and exercises. Some prior knowledge of linear algebra is assumed, including vector spaces and inner products. However, prior familiarity with topics such as quantum mechanics and computational complexity is not required.Trade ReviewThe book is spiced with Try Its, brief exercises that engage the readers in problem solving (both with and without mathematics) and help them digest the many counter-intuitive quantum information science and quantum computing concepts. * MathSciNet *Table of ContentsPreface ; 1. Introduction and background ; 2. Linear algebra and the Dirac notation ; 3. Qubits and the framework of quantum mechanics ; 4. A quantum model of computation ; 5. Superdense coding and quantum teleportation ; 6. Introductory quantum algorithms ; 7. Algorithms with super-polynomial speed-up ; 8. Algorithms based on amplitude amplification ; 9. Quantum computational complexity theory and lower bounds ; 10. Quantum error correction ; Appendices ; Bibliography ; Index
£54.90
MIT Press Ltd Unsupervised Learning
£47.53
Springer High Performance Discovery in Time Series Techniques and Case Studies Monographs in Computer Science
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£85.49
Springer Computation Engineering
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£78.38
Springer Notes on Set Theory
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£66.49
Springer Notes on Set Theory
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£54.99
Springer Project ESociety Building Bricks
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£85.49
Springer New Approaches to Circle Packing in a Square With Program Codes Springer Optimization and Its Applications 6
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£85.49
Springer Brain Inspired Cognitive Systems 2008
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£170.99
Springer Proceedings of the European Computing Conference
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£170.99
Springer The Joy of Sets
Book Synopsis1 Naive Set Theory.- 1.1 What is a Set?.- 1.2 Operations on Sets.- 1.3 Notation for Sets.- 1.4 Sets of Sets.- 1.5 Relations.- 1.6 Functions.- 1.7 Well-Or der ings and Ordinals.- 1.8 Problems.- 2 The ZermeloFraenkel Axioms.- 2.1 The Language of Set Theory.- 2.2 The Cumulative Hierarchy of Sets.- 2.3 The ZermeloFraenkel Axioms.- 2.4 Classes.- 2.5 Set Theory as an Axiomatic Theory.- 2.6 The Recursion Principle.- 2.7 The Axiom of Choice.- 2.8 Problems.- 3 Ordinal and Cardinal Numbers.- 3.1 Ordinal Numbers.- 3.2 Addition of Ordinals.- 3.3 Multiplication of Ordinals.- 3.4 Sequences of Ordinals.- 3.5 Ordinal Exponentiation.- 3.6 Cardinality, Cardinal Numbers.- 3.7 Arithmetic of Cardinal Numbers.- 3.8 Regular and Singular Cardinals.- 3.9 Cardinal Exponentiation.- 3.10 Inaccessible Cardinals.- 3.11 Problems.- 4 Topics in Pure Set Theory.- 4.1 The Borel Hierarchy.- 4.2 Closed Unbounded Sets.- 4.3 Stationary Sets and Regressive Functions.- 4.4 Trees.- 4.5 Extensions of Lebesgue Measure.- 4.6 A ReTable of ContentsPreface; 1. Naïve Set Theory; 2. The Zermelo-Fraenkel Axioms; 3. Ordinal and Cardinal Numbers; 4. Topics in Pure Set Theory; 5. The Axiom of Constructibility; 6. Independence Proofs in Set Theory; 7. Non-Well-Founded Set Theory; Bibliography; Glossary of Symbols; Index
£38.24
Springer Exploring Computer Science with Scheme
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£78.38
Springer Digital Typography Using Latex Pb
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£85.49
Springer New York the Planiverse Computer Contact With A TwoDimensional World
Book SynopsisA classic book about life in a two-dimensional universe, written by a well-known author. Now brought back into print in this revised and updated edition, the book is written within the great tradition of Abbott's Flatland, and Hinton's famous Sphereland.Table of Contents1 Arde.- 2 A House by the Sea.- 3 On Fiddib Har.- 4 Walking to Is Felblt.- 5 City Below Ground.- 6 The Trek.- 7 The Punizlan Institute.- 8 Traveling on the Wind.- 9 High on Dahl Radam.- 10 Drabk the Sharak of Okbra.- 11 Higher Dimensions.- Ardean Science and Technology.- Acknowledgments.
£31.34
Springer Introduction to Linear Parametric and NonLinear Vibrations
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£151.99
Springer introductiontovlsiprocessengineering
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£85.49
John Wiley & Sons Inc Thinking Recursively
Book SynopsisThe process of solving large problems by breaking them down into smaller, more simple problems that have identical forms. Thinking Recursively: A small text to solve large problems. Concentrating on the practical value of recursion. this text, the first of its kind, is essential to computer science students' education.Table of ContentsThe Idea of Recursion. Mathematical Preliminaries. Recursive Functions. The Procedural Approach. The Tower of Hanoi. Permutations. Sorting. Intelligent Algorithms. Graphical Applications. Recursive Data. Implementation of Recursion.
£97.80
Springer Combinatorics Computing and Complexity 1 Mathematics and its Applications
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£44.99
Springer Mathematical Programming Recent Developments and Applications 6 Mathematics and its Applications
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Springer Neural and Automata Networks Dynamical Behavior and Applications 58 Mathematics and Its Applications
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£87.78
Springer AlgebraicGeometric Codes 58 Mathematics and its Applications
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Springer Decompositions of Graphs 47 Mathematics and its Applications
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£104.49
Springer Issues in Reading Writing and Speaking A Neuropsychological Perspective ISSUES IN READING WRITING AND SPEAKING A NEUROPSYCHOLOGICAL PERSPECTIVE By Caramazza Alfonso Author Nov301990 Hardcover
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Springer Algorithmic Aspects of Flows in Networks 69 Mathematics and Its Applications
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Springer Computational Methods for General Sparse Matrices 65 Mathematics and Its Applications
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Springer Systolic Computations 83 Mathematics and its Applications
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Springer Algorithms Main Ideas and Applications 251 Mathematics and Its Applications
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Springer Interior Point Approach to Linear Quadratic and Convex Programming Algorithms and Complexity 277 Mathematics and Its Applications
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Springer Cellular Automata Dynamical Systems and Neural Networks 282 Mathematics and Its Applications
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Springer Algebraic Structures and Operator Calculus Volume II Special Functions and Computer Science 292 Mathematics and Its Applications
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Springer Identification of Dynamical Systems with Small Noise 300 Mathematics and Its Applications
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Springer Semigroups Formal Languages and Groups Proceedings of the NATO Advanced Study Institute University of York UK 721 August 1993 466 Nato Science Series C
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Springer Discrete Analysis and Operations Research 355 Mathematics and Its Applications
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Springer Problems and Exercises in Discrete Mathematics 14 Texts in the Mathematical Sciences
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Springer Dynamics of Complex Interacting Systems 2 Nonlinear Phenomena and Complex Systems
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Springer VLSI Planarization Methods Models Implementation 399 Mathematics and Its Applications
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Springer Applications of Continuous Mathematics to Computer Science 38 Theory and Decision Library B
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Springer Mind Out of Matter Topics in the Physical Foundations of Consciousness and Cognition 20 Studies in Cognitive Systems
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