Mathematical theory of computation Books

Book SynopsisContent.- A Methodology for Complex Embedded Systems Design: Petri Nets within a UML Approach.- Efficient System Modeling for Complex Real-Time Industrial Networks using the ACCORD/UML Methodology.- Analog/Digital Co-Design.- A Design Methodology for Embedded Systems based on Multiple Processors.- An Architecture for Reliable Distributed Computer-Controlled Systems.- Generic Architecture Platform for Multiprocessor System-On-Chip Design.- JPURE A Purified Java Execution Environment for Controller Networks.- Optimizing Functional distribution in Complex System Design.- Customizing Software Toolkits for Embedded Systems-On-Chip.- Framework for System Design, Validation and Fast Prototyping of Multiprocessor System-On-Chip.- The Specification Language SpecC within the PARADISE Design Environment.- Real-Time Support for Online Controller Supervision and Optimisation.- A Product Family Approach to Graceful Degradation.- Environment Modelling in Closed Specifications of Embedded Systems.- Test Case Design for the Validation of Component-Based Embedded Systems.- Timing Constraints Validation using UPPAAL: Schedulability Analysis.- A New Dynamic Scheduling Algorithm for Real-Time Multiprocessor Systems.- Deriving Message Passing Protocols from Collective Behavior.- Java Real-Time Publish-Subscribe Middleware for Distributed Embedded Systems.- A Verified Hardware Synthesis of Esterel Programs.- EXPLORA Generic Design Space Exploration during Embedded System Synthesis.- Automatic Code Generation for Multirate Simulink Models with Support for the OSEK Real-Time Operating System.Table of ContentsPreface. Workshop Organisation. Session 1: Methodology I. A Methodology for Complex Embedded Systems Design: Petri Nets within a UML Approach; R. J. Machado, et al. Efficient System Modeling for Complex Real-Time Industrial Networks using the ACCORD/UML Methodology; S. Gérard, et al. Analog/Digital Co-Design; F. Heuschen, K. Waldschmidt. A Design Methodology for Embedded Systems based on Multiple Processors; L. Carro, et al. Session 2: Architecture. An Architecture for Reliable Distributed Computer-Controlled Systems; L. M. Pinho, F. Vasques. Generic Architecture Platform for Multiprocessor System-On-Chip Design; A. Baghdadi, et al. JPURE - A Purified Java Execution Environment for Controller Networks; D. Beuche, et al. Optimizing Functional distribution in Complex System Design; O. P. Dias, et al. Session 3: Design Environments. Customizing Software Toolkits for Embedded Systems-On-Chip; A. Halambi, et al. Framework for System Design, Validation and Fast Prototyping of Multiprocessor System-On-Chip; N. E. Zergainoh, et al. The Specification Language SpecC within the PARADISE Design Environment; A. Rettberg, et al. Session 4: Methodology II. Real-Time Support for Online Controller Supervision and Optimisation; M. Deppe, O. Oberschelp. A Product Family Approach to Graceful Degradation; W. Nace, P. Koopman. Environment Modelling in Closed Specifications of Embedded Systems; M. Katara, A. Luoma. Session 5: Test and Validation. Test Case Design for the Validation of Component-Based Embedded Systems; W. Fleisch. Timing Constraints Validation using UPPAAL: Schedulability Analysis; H. Sun. Session 6: Distribution andCommunication. A New Dynamic Scheduling Algorithm for Real-Time Multiprocessor Systems; Y. Qiao, et al. Deriving Message Passing Protocols from Collective Behavior; P. Kellomäki. Java Real-Time Publish-Subscribe Middleware for Distributed Embedded Systems; D. Kim, et al. Session 7: Synthesis. A Verified Hardware Synthesis of Esterel Programs; K. Schneider. EXPLORA&endash;Generic Design Space Exploration during Embedded System Synthesis; F. Cieslok, et al. Automatic Code Generation for Multirate Simulink Models with Support for the OSEK Real-Time Operating System; C. Homburg, et al.

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Book SynopsisFrom the Foreword:Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies.---Sartaj Sahni, University of Florida, USABig Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seaTable of ContentsBig Data Management. Big Data Design, implementation, evaluation and services. Big Data as integration of technologies. Big Data analytics and visualization. Query processing and indexing. Elasticity for data management systems. Self-adaptive and energy-efficient mechanisms. Performance evaluation. Security, privacy, trust, data ownership and risk simulations. Processing. Techniques, algorithms and innovative methods of processing. Business and economic models. Adoption cases, frameworks and user evaluations. Data-intensive and scalable computing on hybrid infrastructures. MapReduce based computations. Many-Task Computing in the Cloud. Streaming and real-time processing. Big Data systems and applications for multidisciplinary applications.

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Book SynopsisTesting is the primary hardware and software verification technique used by industry today. Usually, it is ad hoc, error prone, and very expensive. In recent years, however, many attempts have been made to develop more sophisticated formal testing methods. This coherent book provides an in-depth assessment of this emerging field, focusing on formal testing of reactive systems. This book is based on a seminar held in Dagstuhl Castle, Germany, in January 2004. It presents 19 carefully reviewed and revised lectures given at the seminar in a well-balanced way ensuring competent complementary coverage of all relevant aspects. An appendix provides a glossary for model-based testing and basics on finite state machines and on labelled transition systems. The lectures are presented in topical sections on testing of finite state machines, testing of labelled transition systems, model-based test case generation, tools and case studies, standardized test notation and execution architectures, and beyond testing.Table of ContentsTesting of Finite State Machines.- I. Testing of Finite State Machines.- 1 Homing and Synchronizing Sequences.- 2 State Identification.- 3 State Verification.- 4 Conformance Testing.- II. Testing of Labeled Transition Systems.- Testing of Labeled Transition Systems.- 5 Preorder Relations.- 6 Test Generation Algorithms Based on Preorder Relations.- 7 I/O-automata Based Testing.- 8 Test Derivation from Timed Automata.- 9 Testing Theory for Probabilistic Systems.- III. Model-Based Test Case Generation.- Model-Based Test Case Generation.- 10 Methodological Issues in Model-Based Testing.- 11 Evaluating Coverage Based Testing.- 12 Technology of Test-Case Generation.- 13 Real-Time and Hybrid Systems Testing.- IV. Tools and Case Studies.- Tools and Case Studies.- 14 Tools for Test Case Generation.- 15 Case Studies.- V. Standardized Test Notation and Execution Architecture.- Standardized Test Notation and Execution Architecture.- 16 TTCN-3.- 17 UML 2.0 Testing Profile.- VI. Beyond Testing.- Beyond Testing.- 18 Run-Time Verification.- 19 Model Checking.- VII. Appendices.- Appendices.- 20 Model-Based Testing – A Glossary.- 21 Finite State Machines.- 22 Labelled Transition Systems.

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Book SynopsisThis volume contains selected papers for the Parcella '88, the Fourth International Workshop on "Parallel Processing by Cellular Automata and Arrays" held in Berlin from October 17 to 21, 1988. The profile of the Parcella workshop series is focused on problems of processing by regular structures, i.e. their "flexibilization" or adapting to "irregular" algorithms and, closely related to this, on the "regularization" of algorithms for their embedding into regular structures. It seems that these problems will have an increasing priority within the list of central problems in parallelization and will determine the profile of Parcella for the next years.Table of ContentsMultiprocessor arrays: Topology, efficiency and fault-tolerance.- Unsolved theoretical problems in homogeneous structures.- On simultaneous realizations of boolean functions, with applications.- Parallel microprogramming as a tool for multi-microprocessor systems.- A survey of parallel computational geometry algorithms.- Parallel memories for straight line and rectangle access.- Programming with active data.- Prolog implementations for cellular architectures.- Modular highly-parallel computation and architectures.- Parallel computation and supercomputers and applications.- Fast parallel algorithms and the complexity of parallelism (basic issues and recent advances).- Process-structured architectures to transform information flowing through.- Basic research for cellular processing.- Parallel algorithms in image processing.- VLSI arrays implementing parallel line-drawing algorithms.- Parallel conflict-free optimal access to complete extended q-ary trees.- Systolic preconditioning algorithms for the jacobi iterative solution of sparse linear systems.- Multiprocessor systems for large numerical applications.- Systolic array for eigenvalue of jacobi matrix.- A transitive closure algorithm for a 16-state cellprocessor.- Control of sensory processing — A hypothesis on and simulation of the architecture of an elementary cortical processor.- Bounds for l-selection and related problems on grids of processors.- Recursive design of communication schemes for parallel computation with relacs.- Solution of dense systems of linear equations using cellular processors.- Running order statistics on a bit-level systolic array.- Realization of sets of permutations by permutation networks.- Simulation of learning networks.- Given's rotation on an instruction systolic array.- Worst case analysis for reducing algorithms on instruction systolic arrays with simple instruction sets.- Self-checking processing elements in cellular arrays.- Cellular diagnostic in parallel systems.- Reliable networks for boolean functions with small complexity.- Pipeline-automata — A model for acyclic systolic systems.

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Book SynopsisThis volume contains the texts of the tutorial lecture, five invited lectures and twenty short communications contributed for presentation at the Sixth International Meeting of Young Computer Scientists, IMYCS '90. The aim of these meetings is threefold: (1) to inform on newest trends, results, and problems in theoretical computer science and related fields through a tutorial and invited lectures delivered by internationally distinguished speakers, (2) to provide a possibility for beginners in scientific work to present and discuss their results, and (3) to create an adequate opportunity for establishing first professional relations among the participants.Table of ContentsMethods for generating deterministic fractals and image compression.- Optimum simulation of meshes by small hypercubes.- Seven hard problems in symbolic background knowledge acquisition.- Subsequential functions: Characterizations, minimization, examples.- Past proves more invariance properties but not pca's.- Complexity issues in discrete neurocomputing.- Two-way reading on words.- Proofs and reachability problem for ground rewrite systems.- Problems complete for ?L.- Constructive matching — Explanation based methodology for inductive theorem proving.- Characterizing complexity classes by higher type.- The distributed termination problem : Formal solution and correctness based on petri nets.- Greedy compression systems.- A DIV(N) depth Boolean circuit for smooth modular inverse.- Learning by conjugate gradients.- Monoids described by pushdown automata.- Optimal parallel 3-colouring algorithm for rooted trees and its application.- Hierarchies over the context-free languages.- A hierarchy of unary primitive recursive string-functions.- Minimizing picture words.- Remarks on the frequency-coded neural nets complexity.- Picture generation using matrix systems.- Representing heuristic-relevant information for an automated theorem prover.- A new method for proving lower bounds in the model of algebraic decision trees.- Area time squared and area complexity of VLSI computations is strongly unclosed under union and intersection.- Decision procedure for checking validity of PAL formulas.

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Book SynopsisKonrad Zuse is one of the great pioneers of the computer age. He created thefirst fully automated, program controlled, freely programmable computer using binary floating-point calculation. It was operational in 1941. He built his first machines in Berlin during the Second World War, with bombs falling all around, and after the war he built up a company that was taken over by Siemens in 1967. Zuse was an inventor in the traditional style, full of phantastic ideas, but also gifted with a powerful analytical mind. Single-handedly, he developed one of the first programming languages, the Plan Calculus, including features copied only decades later in other languages. He wrote numerousbooks and articles and won many honors and awards. This is his autobiography, written in an engagingly lively and pleasant style, full of anecdotes, reminiscences, and philosophical asides. It traces his life from his childhood in East Prussia, through tense wartime experiences and hard times building up his business after the war, to a ripe old age andwell-earned celebrity.Trade ReviewFrom the reviews:“The book tells the story of an inventor and an entrepreneur. It is refreshing because it allows one to see things outside of the box, beyond the more traditional story, so that he or she can better appreciate key aspects of computing and computation. Furthermore, the book tells the story of a father, a hard worker, and a recognized inventor, including pictures and plenty of anecdotes. … The book is probably the only reliable source about Konrad Zuse’s life and contributions to the world.” (Hector Zenil, ACM Computing Reviews, November, 2011)Table of Contents1 Ancestors and parents — Early childhood memories — School days — Metropolis — Abitur.- 2 Studies (not without detours and by-ways) and general studies — First inventions — The Akademischer Verein Motiv — Student life between science and politics.- 3 The early years of the computer (and a digression on its prehistory) — Colleagues remember — From mechanics to electromechanics — Schreyer’s electronic computing machine — First outside contacts — Thoughts on the future.- 4 Outbreak of the war and (first) call-up — Structural engineer in aircraft construction — The Z2 and Z3 — Second call-up — Zuse Ingenieurbüro und Apparatebau, Berlin — The first process computer.- 5 Origins of the Z4 — News from the United States — Attempt at a Ph.D. dissertation — Computing machine for logic operations — Final months of the war in Berlin — The evacuation — Z4 completed in Göttingen — Final war days in the Allgäu.- 6 End of the war — Refugees in Hinterstein — The Plankalkül — The computing universe — Automation and self-reproducing systems — A logarithmic computing machine — Computer development in Germany and the United States — Move to Hopferau near Füssen — The mill of the Patent Office.- 7 The Zuse-Ingenieurbüro, Hopferau bei Füssen — First business partners: IBM and Remington Rand — The first pipelining design — Founding of ZUSE KG in Neukirchen — The Z4 in the ETH in Zurich — The computer in Europe: taking stock — Lost opportunities — The first German contract: the Z5.- 8 The partners leave — Computing machine for land use zoning — Electronics gains acceptance — First funds from the Deutsche Forschungsgemeinschaft — Losing one’s way (and possibly a lost opportunity) — The array processor — Custom work for geodesists — The Graphomat Z64 — Growth and crisis of ZUSE KG — The end.- 9 Free for science (again) — Honors — A look to the future.- Appendices.- 1. From Forms to Program Control.- 2. Construction of Devices.- 3. On Computer Architecture.- 4. On the Plan Calculus.- 5. Lecture on the Occasion of the Award of the Honorary Doctorate by the Technical University of Berlin (Extract).- 6. The Computer Did Not Fall from Heaven.- Notes.- References.- Name Index.- Computer Index.

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Book SynopsisThis book constitutes the refereed proceedings of the 23rd International Conference on Computer Aided Verification, CAV 2011, held in Snowbird, UT, USA, in July 2011. The 35 revised full papers presented together with 20 tool papers were carefully reviewed and selected from 161 submissions. The papers are organized in topical sections on the following workshops: 4th International Workshop on Numerical Software Verification (NSV 2011), 10th International Workshop on Parallel and Distributed Methods in Verifications (PDMC 2011), 4th International Workshop on Exploiting Concurrency Efficiently and Correctly (EC2 2011), Frontiers in Analog Circuit Synthesis and Verification (FAC 2011), International Workshop on Satisfiability Modulo Theories, including SMTCOMP (SMT 2011), 18th International SPIN Workshop on Model Checking of Software (SPIN 2011), Formal Methods for Robotics and Automation (FM-R 2011), and Practical Synthesis for Concurrent Systems (PSY 2011).

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Book SynopsisThis book constitutes the proceedings of the Third International Conference on Abstract State Machines, B, VDM, and Z, which took place in Pisa, Italy, in June 2012. The 20 full papers presented together with 2 invited talks and 13 short papers were carefully reviewed and selected from 59 submissions. The ABZ conference series is dedicated to the cross-fertilization of five related state-based and machine-based formal methods: Abstract State Machines (ASM), Alloy, B, VDM, and Z. They share a common conceptual foundation and are widely used in both academia and industry for the design and analysis of hardware and software systems. The main goal of this conference series is to contribute to the integration of these formal methods, clarifying their commonalities and differences to better understand how to combine different approaches for accomplishing the various tasks in modeling, experimental validation and mathematical verification of reliable high-quality hardware/software systems.

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Book SynopsisThis book constitutes the refereed proceedings of the 16th International Conference on Model Driven Engineering Languages and Systems, MODELS 2013, held in Miami, FL, USA, in September/October 2013. The 47 full papers presented in this volume were carefully reviewed and selected from a total of 180 submissions. They are organized in topical sections named: tool support; dependability; comprehensibility; testing; evolution; verification; product lines; semantics; domain-specific modeling languages; models@RT; design and architecture; model transformation; model analysis; and system synthesis.Table of ContentsTool support.- Dependability.- Comprehensibility.- Testing.- Evolution.- Verification.- Product lines.- Semantics.- Domain-specific modeling languages.- Design and architecture.- Model transformation.- Model analysis.- System synthesis.

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Book SynopsisThe design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen­ dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith­ mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa­ tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven­ tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.Table of ContentsI. Foundations.- 1. Machine Models: RAM and RASP.- 2. Randomized Computations.- 3. A High Level Programming Language.- 4. Structured Data Types.- 4.1 Queues and Stacks.- 4.2 Lists.- 4.3 Trees.- 5. Recursion.- 6. Order of Growth.- 7. Secondary Storage.- 8. Exercises.- 9. Bibliographic Notes.- II. Sorting.- 1. General Sorting Methods.- 1.1 Sorting by Selection, a First Attempt.- 1.2 Sorting by Selection: Heapsort.- 1.3 Sorting by Partitioning: Quicksort.- 1.4 Sorting by Merging.- 1.5 Comparing Different Algorithms.- 1.6 Lower Bounds.- 2. Sorting by Distribution.- 2.1 Sorting Words.- 2.2 Sorting Reals by Distribution.- 3. The Lower Bound on Sorting, Revisited.- 4. The Linear Median Algorithm.- 5. Exercises.- 6. Bibliographic Notes.- III. Sets.- 1. Digital Search Trees.- 1.1 Tries.- 1.2 Static Tries or Compressing Sparse Tables.- 2. Hashing.- 2.1 Hashing with Chaining.- 2.2 Hashing with Open Addressing.- 2.3 Perfect Hashing.- 2.4 Universal Hashing.- 2.5 Extendible Hashing.- 3. Searching Ordered Sets.- 3.1 Binary Search and Search Trees.- 3.2 Interpolation Search.- 4. Weighted Trees.- 4.1 Optimum Weighted Trees, Dynamic Programming, and Pattern Matching.- 4.2 Nearly Optimal Binary Search Trees.- 5. Balanced Trees.- 5.1 Weight-Balanced Trees.- 5.2 Height-Balanced Trees.- 5.3 AdvancedTopicson(a,b)-Trees.- 5.3.1 Mergable Priority Queues.- 5.3.2 Amortized Rebalancing Cost and Sorting Presorted Files.- 5.3.3 Finger Trees.- 5.3.4 Fringe Analysis.- 6. Dynamic Weighted Trees.- 6.1 Self-Organizing Data Structures and Their Amortized and Average Case Analysis.- 6.1.1 Self-Organizing Linear Lists.- 6.1.2 Splay Trees.- 6.2 D-trees.- 6.3 An Application to Multidimensional Searching.- 7. A Comparison of Search Structures.- 8. Subsets of a Small Universe.- 8.1 The Boolean Array (Bitvector).- 8.2 The O(log log N) Priority Queue.- 8.3 The Union-Find Problem.- 9. Exercises.- 10. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Book SynopsisDem Leser werden neben praxisnahen Beispielen zu jedem Thema auch zahlreiche Übungsaufgaben mit Lösungen zur Verfügung gestellt. Somit kann der zukünftige Studierende sich zunächst orientieren, ob seine Fähigkeiten für das gewünschte Ingenieurstudium bereits ausreichend sind oder ob er mehr hierfür tun muss.Table of ContentsMathematische Grundlagen.- Elementare Geometrie.- Funktionen.- Differentialrechnung.- Integralrechnung.- Vektorrechnung.- Matrizenrechnung.- Wahrscheinlichkeits- und Fehlerrechnung.- Folgen und Reihen.- Ausblick: Komplexe Zahlen und Differentialgleichungen.

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Book SynopsisA unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, Trade Review “In conclusion, I found reading Metaheuristics: From Design to Implementation to be pleasant and enjoyable. I particularly recommend it as a reference for researchers and students of computer science or operations research who want a global outlook of metaheuristics methods. It would also be extremely useful for introducing graduate and PhD students who are new to the field of heuristics and metaheuristics to the amazing world of the designing of these procedures.” (Informs, 1 July 2012) "It will be an indispensable text for advanced undergraduate and graduate students in computer science, operations research, applied mathematics, control, business and management and engineering." (Zentralblatt MATH, 2010) Table of ContentsPreface. Acknowledgments. Glossary. 1 Common Concepts for Metaheuristics. 1.1 Optimization Models. 1.2 Other Models for Optimization. 1.3 Optimization Methods. 1.4 Main Common Concepts for Metaheuristics. 1.5 Constraint Handling. 1.6 Parameter Tuning. 1.7 Performance Analysis of Metaheuristics. 1.8 Software Frameworks for Metaheuristics. 1.9 Conclusions. 1.10 Exercises. 2 Single-Solution Based Metaheuristics. 2.1 Common Concepts for Single-Solution Based Metaheuristics. 2.2 Fitness Landscape Analysis. 2.3 Local Search. 2.4 Simulated Annealing. 2.5 Tabu Search. 2.6 Iterated Local Search. 2.7 Variable Neighborhood Search. 2.8 Guided Local Search. 2.9 Other Single-Solution Based Metaheuristics. 2.10 S-Metaheuristic Implementation Under ParadisEO. 2.11 Conclusions. 2.12 Exercises. 3 Population-Based Metaheuristics. 3.1 Common Concepts for Population-Based Metaheuristics. 3.2 Evolutionary Algorithms. 3.3 Common Concepts for Evolutionary Algorithms. 3.4 Other Evolutionary Algorithms. 3.5 Scatter Search. 3.6 Swarm Intelligence. 3.7 Other Population-Based Methods. 3.8 P-metaheuristics Implementation Under ParadisEO. 3.9 Conclusions. 3.10 Exercises. 4 Metaheuristics for Multiobjective Optimization. 4.1 Multiobjective Optimization Concepts. 4.2 Multiobjective Optimization Problems. 4.3 Main Design Issues of Multiobjective Metaheuristics. 4.4 Fitness Assignment Strategies. 4.5 Diversity Preservation. 4.6 Elitism. 4.7 Performance Evaluation and Pareto Front Structure. 4.8 Multiobjective Metaheuristics Under ParadisEO. 4.9 Conclusions and Perspectives. 4.10 Exercises. 5 Hybrid Metaheuristics. 5.1 Hybrid Metaheuristics. 5.2 Combining Metaheuristics with Mathematical Programming. 5.3 Combining Metaheuristics with Constraint Programming. 5.4 Hybrid Metaheuristics with Machine Learning and Data Mining. 5.5 Hybrid Metaheuristics for Multiobjective Optimization. 5.6 Hybrid Metaheuristics Under ParadisEO. 5.7 Conclusions and Perspectives. 5.8 Exercises. 6 Parallel Metaheuristics. 6.1 Parallel Design of Metaheuristics. 6.2 Parallel Implementation of Metaheuristics. 6.3 Parallel Metaheuristics for Multiobjective Optimization. 6.4 Parallel Metaheuristics Under ParadisEO. 6.5 Conclusions and Perspectives. 6.6 Exercises. Appendix: UML and C++. A.1 A Brief Overview of UML Notations. A.2 A Brief Overview of the C++ Template Concept. References. Index.

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Book SynopsisCovering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate exploration of data.Trade Review“Altogether, the book provides a very nice overview of nonparametric and semiparametric regression methods with interesting applications to problems in quantitative finance.” (Mathematical Reviews, 1 October 2015) Table of ContentsPreface xvii Introduction xix I.1 Estimation of Functionals of Conditional Distributions xx I.2 Quantitative Finance xxi I.3 Visualization xxi I.4 Literature xxiii PART I METHODS OF REGRESSION AND CLASSIFICATION 1 Overview of Regression and Classification 3 1.1 Regression 3 1.2 Discrete Response Variable 29 1.3 Parametric Family Regression 33 1.4 Classification 37 1.5 Applications in Quantitative Finance 42 1.6 Data Examples 52 1.7 Data Transformations 53 1.8 Central Limit Theorems 58 1.9 Measuring the Performance of Estimators 61 1.10 Confidence Sets 73 1.11 Testing 75 2 Linear Methods and Extensions 77 2.1 Linear Regression 78 2.2 Varying Coefficient Linear Regression 97 2.3 Generalized Linear and Related Models 102 2.4 Series Estimators 107 2.5 Conditional Variance and ARCH models 111 2.6 Applications in Volatility and Quantile Estimation 115 2.7 Linear Classifiers 124 3 Kernel Methods and Extensions 127 3.1 Regressogram 129 3.2 Kernel Estimator 130 3.3 Nearest Neighborhood Estimator 147 3.4 Classification with Local Averaging 148 3.5 Median Smoothing 151 3.6 Conditional Density Estimators 152 3.7 Conditional Distribution Function Estimation 158 3.8 Conditional Quantile Estimation 160 3.9 Conditional Variance Estimation 162 3.10 Conditional Covariance Estimation 176 3.11 Applications in Risk Management 181 3.12 Applications in Portfolio Selection 205 4 Semiparametric and Structural Models 229 4.1 Single Index Model 230 4.2 Additive Model 234 4.3 Other Semiparametric Models 237 5 Empirical Risk Minimization 241 5.1 Empirical Risk 243 5.2 Local Empirical Risk 247 5.3 Support Vector Machines 257 5.4 Stagewise Methods 259 5.5 Adaptive Regressograms 264 PART II VISUALIZATION 6 Visualization of Data 277 6.1 Scatter Plots 278 6.2 Histogram and Kernel Density Estimator 282 6.3 Dimension Reduction 284 6.4 Observations as Objects 288 7 Visualization of Functions 295 7.1 Slices 296 7.2 Partial Dependence Functions 296 7.3 Reconstruction of Sets 299 7.4 Level Set Trees 303 7.5 Unimodal Densities 326 7.5.1 Probability Content of Level Sets 327 7.5.2 Set Visualization 328 Appendix A: R Tutorial 329 A.1 Data Visualization 329 A.2 Linear Regression 331 A.3 Kernel Regression 332 A.4 Local Linear Regression 341 A.5 Additive Models: Backfitting 344 A.6 Single Index Regression 345 A.7 Forward Stagewise Modeling 347 A.8 Quantile Regression 349 References 351 Author Index 361 Topic Index 365

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Book SynopsisThis new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.Table of ContentsPREFACE xv ACKNOWLEDGMENTS xvii 1 REVIEW 1 1.1 Mathematical Notation 1 1.2 Taylor’s Theorem and Mathematical Limit Theory 2 1.3 Statistical Notation and Probability Distributions 4 1.4 Likelihood Inference 9 1.5 Bayesian Inference 11 1.6 Statistical Limit Theory 13 1.7 Markov Chains 14 1.8 Computing 17 PART I OPTIMIZATION 2 OPTIMIZATION AND SOLVING NONLINEAR EQUATIONS 21 2.1 Univariate Problems 22 2.2 Multivariate Problems 34 Problems 54 3 COMBINATORIAL OPTIMIZATION 59 3.1 Hard Problems and NP-Completeness 59 3.2 Local Search 65 3.3 Simulated Annealing 68 3.4 Genetic Algorithms 75 3.5 Tabu Algorithms 85 Problems 92 4 EM OPTIMIZATION METHODS 97 4.1 Missing Data, Marginalization, and Notation 97 4.2 The EM Algorithm 98 4.3 EM Variants 111 Problems 121 PART II INTEGRATION AND SIMULATION 5 NUMERICAL INTEGRATION 129 5.1 Newton–Côtes Quadrature 129 5.2 Romberg Integration 139 5.3 Gaussian Quadrature 142 5.4 Frequently Encountered Problems 146 Problems 148 6 SIMULATION AND MONTE CARLO INTEGRATION 151 6.1 Introduction to the Monte Carlo Method 151 6.2 Exact Simulation 152 6.3 Approximate Simulation 163 6.4 Variance Reduction Techniques 180 Problems 195 7 MARKOV CHAIN MONTE CARLO 201 7.1 Metropolis–Hastings Algorithm 202 7.2 Gibbs Sampling 209 7.3 Implementation 218 Problems 230 8 ADVANCED TOPICS IN MCMC 237 8.1 Adaptive MCMC 237 8.2 Reversible Jump MCMC 250 8.3 Auxiliary Variable Methods 256 8.4 Other Metropolis–Hastings Algorithms 260 8.5 Perfect Sampling 264 8.6 Markov Chain Maximum Likelihood 268 8.7 Example: MCMC for Markov Random Fields 269 Problems 279 PART III BOOTSTRAPPING 9 BOOTSTRAPPING 287 9.1 The Bootstrap Principle 287 9.2 Basic Methods 288 9.3 Bootstrap Inference 292 9.4 Reducing Monte Carlo Error 302 9.5 Bootstrapping Dependent Data 303 9.6 Bootstrap Performance 315 9.7 Other Uses of the Bootstrap 316 9.8 Permutation Tests 317 Problems 319 PART IV DENSITY ESTIMATION AND SMOOTHING 10 NONPARAMETRIC DENSITY ESTIMATION 325 10.1 Measures of Performance 326 10.2 Kernel Density Estimation 327 10.3 Nonkernel Methods 341 10.4 Multivariate Methods 345 Problems 359 11 BIVARIATE SMOOTHING 363 11.1 Predictor–Response Data 363 11.2 Linear Smoothers 365 11.3 Comparison of Linear Smoothers 377 11.4 Nonlinear Smoothers 379 11.5 Confidence Bands 384 11.6 General Bivariate Data 388 Problems 389 12 MULTIVARIATE SMOOTHING 393 12.1 Predictor–Response Data 393 12.2 General Multivariate Data 413 Problems 416 DATA ACKNOWLEDGMENTS 421 REFERENCES 423 INDEX 457

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Book SynopsisThis edition includes developments in chaos theory, with new chapters that tie in with innovations such as fuzzy logic, neural nets and artificial intelligence as they relate to finance.Table of ContentsTHE NEW PARADIGM. Introduction: Life Can Be So Complicated. Random Walks and Efficient Markets. The Failure of the Linear Paradigm. Markets and Chaos: Chance and Necessity. FRACTAL STRUCTURE IN THE CAPITAL MARKETS. Introduction to Fractals. The Fractal Dimension. Fractal Time Series-Biased Random Walks. R/S Analysis of the Capital Markets. Fractal Statistics. Fractals and Chaos. NONLINEAR DYNAMICS. Introduction to Nonlinear Dynamic Systems. Dynamic Analysis of Time Series. Dynamic Analysis of the Capital Markets. LIVING WITH COMPLEXITY. The Coherent Market Hypothesis. Fractional Truth: Fuzzy Logic and Behavioral Finance. Applying Chaos and Nonlinear Methods. What Lies Ahead: Toward a More General Approach. About the Diskette. Appendices. Bibliography. Glossary. Index.

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Book SynopsisA unique, integrated treatment of computer modeling and simulation The future of science belongs to those willing to make the shift to simulation-based modeling, predicts Rice Professor James Thompson, a leading modeler and computational statistician widely known for his original ideas and engaging style. He discusses methods, available to anyone with a fast desktop computer, for integrating simulation into the modeling process in order to create meaningful models of real phenomena. Drawing from a wealth of experience, he gives examples from trading markets, oncology, epidemiology, statistical process control, physics, public policy, combat, real-world optimization, Bayesian analyses, and population dynamics. Dr. Thompson believes that, so far from liberating us from the necessity of modeling, the fast computer enables us to engage in realistic models of processes in , for example, economics, which have not been possible earlier because simple stochastic models in the forward temporal Trade ReviewWith the advent of faster computers with comparatively large storage facilities, simulation-based modeling is rapidly being adopted as an alternative approach to conventional top-down, assumptions-based, continuous, stochastic and discrete differential equation modeling. Thompson offers an interesting exposition to the art of simulation, and views "simulation approach" to modeling as a paradigm for realistic evolutionary modeling. The book is written in a very casual style, and background knowledge in statistics is all that is required to grasp the material contained therein. Thompson begins with an exposition of the generation of randomnumbers and then delves into a variety of special topics, including models for stocks and derivatives, optimization and estimation in a noisy world, Monte-Carlo solutions to differential equations, simulation assessment of multivariate and robust procedures in statistical process control, resampling-based tests, and some exposition to modeling the AIDS epidemic. Several useful algorithms, problem sets, and references to standard simulation packages are provided. Short chapter bibliographies; wide range of examples. The book could be used as a resource for beginning graduate students and professionals in applied statistics, computer science, economics and finance, engineering, and the natural sciences. Highly recommended. Graduate students; faculty; professionals. (CHOICE, April 2001, Vol. 38, No. 8) "...an eclectic survey of computing methods...lively and interesting...the wide variety of example certainly helps bring the material to life." (Journal of the American Statistical Association, Vol. 97, No. 457, March 2002) "...a very useful and entertaining book...a great reference book...contains some valuable material and philosophy that is unavailable anywhere else." (IIE Transactions) "...often entertaining...the level of detain and relevance is appropriate...a worthwhile read for model builders comfortable with both mathematics and simulation." (Complexity, Vol. 7, No. 2, 2002)Table of ContentsThe Generation of "Random" Numbers. Random Quadrature. Monte Carlo Solutions of Differential Equations. Markov Chains, Poisson Processes and Linear Equations. SIMEST, SIMDAT, and Pseudoreality. Models for Stocks and Derivatives. Simulation Assessment of Multivariate and Robust Procedures in Statistical Process Control. Noise and Chaos. Bayesian Approaches. Resampling Based Tests. Optimization and Estimation in a Noisy World. Modeling the USA AIDS Epidemic: Exploration, Simulation and Conjecture. Appendices. Index.

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Book SynopsisReinforcing concepts with frequent exercises and extended applications, this introduction to the theoretical and computational aspects of linear algebra uses the MATLAB software to master the basic algorithms and to increase the speed and efficiency of computations.Table of ContentsMatrix Algebra. Vector Spaces and Linear Transformations. Orthogonality and Projections. Eigenvalues and Eigenvectors. The Spectral Theorem and Applications. Normal Forms. Appendices. Bibliography. Index.

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Book SynopsisThere are many situations in science and engineering where complex output data from a given system is used to formulate a model of how that system operates, or to simulate its response to different inputs. Applications include control, decision theory, and the emerging fields of bioinformatics.Trade Review"To cope with real world uncertainties and provide a philosophical and practical guide...several methodologies are presented..." (SciTech Book News, Vol. 25, No. 4, December 2001) "...certainly a book that should be in the library of any institution where research and advanced study in fuzzy systems are carried out." (Choice, Vol. 39, No. 7, March 2002) "...well organized, easy to read, and self-contained.... I would recommend it to anyone interested in self-study of the basic ideas of fuzzy systems..." (International Journal of General Systems, Vol. 31, No. 6, 2002)Table of ContentsPreface. Acknowledgments. Introduction. System Analysis. Uncertainty Techniques. Learning from Data: System Identification. Propositions as Subsets of the Data Space. Fuzzy Systems and Identification. Random-Set Modelling and Identification. Certain Uncertainty. Fuzzy Inference Engines. Fuzzy Classification. Fuzzy Control. Fuzzy Mathematics. Summary. Appendices. Index.

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Book SynopsisA complete, highly accessible introduction to one of today's most exciting areas of applied mathematics One of the youngest, most vital areas of applied mathematics, combinatorial optimization integrates techniques from combinatorics, linear programming, and the theory of algorithms.Table of ContentsProblems and Algorithms. Optimal Trees and Paths. Maximum Flow Problems. Minimum-Cost Flow Problems. Optimal Matchings. Integrality of Polyhedra. The Traveling Salesman Problem. Matroids. NP and NP-Completeness. Appendix. Bibliography. Index.

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Book SynopsisThe aim of this work is to give breadth and depth to the C++ programmer's existing experience of the language. It presents a large number of algorithms, each of them implemented as ready-to-run (and standalone) programs.Table of ContentsSome Aspects of Programming in C++. Arithmetic. Sorting Arrays and Files. Stacks, Queues and Lists. Searching and String Processing. Binary Trees. B-trees. Tries, Priority Queues and File Compression. Graphs. Some Combinatorial Algorithms. Fundamentals of Interpreters and Compilers. Appendix. Bibliography. Index.

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Book SynopsisThis book presents the basic scientific computing methods for the solution of partial differential equations (PDEs) as they occur in engineering problems. Programming codes in Fortran and C are included for each problem.Table of ContentsSome Partial Differential Equations. PROGRAMMING THE MODEL PROBLEM BY A FINITE ELEMENT METHOD. Introduction to the Finite Element Method: Energy Minimisation. Finite Element Method: Variational Formulation and Direct Methods. Finite Element Method: Optimisation of the Method. GENERAL ELLIPTIC PROBLEMS AND EVOLUTION PROBLEMS. Finite Element Method for General Elliptic Problems. Non-symmetric or Non-linear Partial Differential Equations. Evolution Problems: Finite Differences in Time. COMPLEMENTS ON NUMERICAL METHODS. Integral Methods for the Laplacian. Some Algorithms for Parallel Computing. Bibliography. Index.

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Book Synopsis

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