This book provides an accessible introduction to the variational formulation of Lagrangian and Hamiltonian mechanics, with a novel emphasis on global descriptions of the dynamics, which is a significant conceptual departure from more traditional approaches based on the use of local coordinates on the configuration manifold. In particular, we introduce a general methodology for obtaining globally valid equations of motion on configuration manifolds that are Lie groups, homogeneous spaces, and embedded manifolds, thereby avoiding the difficulties associated with coordinate singularities. The material is presented in an approachable fashion by considering concrete configuration manifolds of increasing complexity, which then motivates and naturally leads to the more general formulation that follows. Understanding of the material is enhanced by numerous in-depth examples throughout the book, culminating in non-trivial applications involving multi-body systems. This book is written for a general audience of mathematicians, engineers, and physicists with a basic knowledge of mechanics. Some basic background in differential geometry is helpful, but not essential, as the relevant concepts are introduced in the book, thereby making the material accessible to a broad audience, and suitable for either self-study or as the basis for a graduate course in applied mathematics, engineering, or physics.

This edited monograph provides a compelling analysis of the interplay between neuroscience and aesthetics. The book broaches a wide spectrum of topics including, but not limited to, mathematics and creator algorithms, neurosciences of artistic creativity, paintings and dynamical systems as well as computational research for architecture. The international authorship is genuinely interdisciplinary and the target audience primarily comprises readers interested in transdisciplinary research between neuroscience and the broad field of aesthetics.

This volume contains selected papers authored by speakers and participants of the 2013 Arbeitstagung, held at the Max Planck Institute for Mathematics in Bonn, Germany, from May 22-28. The 2013 meeting (and this resulting proceedings) was dedicated to the memory of Friedrich Hirzebruch, who passed away on May 27, 2012. Hirzebruch organized the first Arbeitstagung in 1957 with a unique concept that would become its most distinctive feature: the program was not determined beforehand by the organizers, but during the meeting by all participants in an open discussion. This ensured that the talks would be on the latest developments in mathematics and that many important results were presented at the conference for the first time. Written by leading mathematicians, the papers in this volume cover various topics from algebraic geometry, topology, analysis, operator theory, and representation theory and display the breadth and depth of pure mathematics that has always been characteristic of the Arbeitstagung.

This volume focuses on contributions from both the mathematics and life science community surrounding the concepts of time and dynamicity of nature, two significant elements which are often overlooked in modeling process to avoid exponential computations. The book is divided into three distinct parts: dynamics of genomes and genetic variation, dynamics of motifs, and dynamics of biological networks. Chapters included in dynamics of genomes and genetic variation analyze the molecular mechanisms and evolutionary processes that shape the structure and function of genomes and those that govern genome dynamics. The dynamics of motifs portion of the volume provides an overview of current methods for motif searching in DNA, RNA and proteins, a key process to discover emergent properties of cells, tissues, and organisms. The part devoted to the dynamics of biological networks covers networks aptly discusses networks in complex biological functions and activities that interpret processes in cells. Moreover, chapters in this section examine several mathematical models and algorithms available for integration, analysis, and characterization. Once life scientists began to produce experimental data at an unprecedented pace, it become clear that mathematical models were necessary to interpret data, to structure information with the aim to unveil biological mechanisms, discover results, and make predictions. The second annual "Bringing Maths to Life" workshop held in Naples, Italy October 2015, enabled a bi-directional flow of ideas from and international group of mathematicians and biologists. The venue allowed mathematicians to introduce novel algorithms, methods, and software that may be useful to model aspects of life science, and life scientists posed new challenges for mathematicians.

This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference "Optimization and Its Applications in Control and Data Science" dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak's fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.

This book gathers selected contributions on emerging research work presented at the International Conference eXtended Discretization MethodS (X-DMS), held in Ferrara in September 2015. It highlights the most relevant advances made at the international level in the context of expanding classical discretization methods, like finite elements, to the numerical analysis of a variety of physical problems. The improvements are intended to achieve higher computational efficiency and to account for special features of the solution directly in the approximation space and/or in the discretization procedure. The methods described include, among others, partition of unity methods (meshfree, XFEM, GFEM), virtual element methods, fictitious domain methods, and special techniques for static and evolving interfaces. The uniting feature of all contributions is the direct link between computational methodologies and their application to different engineering areas.

This volume deals with numerical simulation of coupled problems in soil mechanics and foundations. It contains analysis of both shallow and deep foundations. Several nonlinear problems are considered including, soil plasticity, cracking, reaching the soil bearing capacity, creep, etc. Dynamic analysis together with stability analysis are also included. Several numerical models of dams are considered together with coupled problems in soil mechanics and foundations. It gives wide range of modelling soil in different parts of the world. This volume is part of the proceedings of the 1st GeoMEast International Congress and Exhibition on Sustainable Civil Infrastructures, Egypt 2017.

Presents easy to understand proofs of some of the most difficult results about polynomials demonstrated by means of applications.

This work focuses on the preservation of attractors and saddle points of ordinary differential equations under discretisation. In the 1980s, key results for autonomous ordinary differential equations were obtained - by Beyn for saddle points and by Kloeden & Lorenz for attractors. One-step numerical schemes with a constant step size were considered, so the resulting discrete time dynamical system was also autonomous. One of the aims of this book is to present new findings on the discretisation of dissipative nonautonomous dynamical systems that have been obtained in recent years, and in particular to examine the properties of nonautonomous omega limit sets and their approximations by numerical schemes - results that are also of importance for autonomous systems approximated by a numerical scheme with variable time steps, thus by a discrete time nonautonomous dynamical system.

This book provides a comprehensive coverage of hybrid high-order methods for computational mechanics. The first three chapters offer a gentle introduction to the method and its mathematical foundations for the diffusion problem. The next four chapters address applications of increasing complexity in the field of computational mechanics: linear elasticity, hyperelasticity, wave propagation, contact, friction, and plasticity. The last chapter provides an overview of the main implementation aspects including some examples of Matlab code. The book is primarily intended for graduate students, researchers, and engineers working in related fields of application, and it can also be used as a support for graduate and doctoral lectures.

TOPOLOGICAL OPTIMIZATION OF CONTINUUM STRUCTURE WITH GLOBAL STRESS CONSTRAINTS BASED ON ICM METHOD.- TOPOLOGICAL OPTIMIZATION OF FRAME STRUCTURES UNDER MULTIPLE LOADING CASES$^*$.- OPTIMAL DISPLACEMENT CONTROL SIMULATION OF ELECTRIC-MECHANICAL COUPLED TRUSSES.- PROTEIN SECONDARY STRUCTURE PREDICTION METHODS BASED ONRBF NEURAL NETWORKS.- A HYBRID META-HEURISTIC FOR A ROUTING PROBLEM.- IDENTIFICATION OF GEOMETRIC PARAMETERS OF DRAWBEAD USING NEURAL NETWORKS.- IMPROVED IMMUNE GENETIC ALGORITHM FOR SOLVING FLOW SHOP SCHEDULING PROBLEM.- A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR TRAVELLING SALESMAN PROBLEM.- A SEARCH METHOD FOR FINDING A SIMPLE NASH EQUILIBRIUM.- A HERO EVOLUTIONARY ALGORITHM HYBRIDIZING FROM PSO AND GA.- A DATA COLLECTION MODEL FOR INTRUSION DETECTION SYSTEM BASED ON SIMPLE RANDOM SAMPLING.- DESTRUCTIVE EXTENSION RULE IN PROPOSITION MODAL LOGIC K.- A NOVEL PARTICLE SWARM OPTIMIZATION-BASED APPROACH FOR JOB-SHOP SCHEDULING.- UNCALIBRATED ROBOTIC ARM VISUAL SERVO CONTROL.- INTERLINEATION AND INTERFLATION FUNCTIONS OF MANY VARIABLES (BLENDING FUNCTION INTERPOLATION) AND ECONOMICAL ALGORITHMS IN THE APPROXIMATION THEORY.- INDEPENDENT COMPONENT ANALYSIS OF DYNAMIC CONTRAST-ENHANCED IMAGES: THE NUMBER OF COMPONENTS.- THE GEOMETRIC CONSTRAINT SOLVING BASED ON HYBRID GENETIC ALGORITHM OF CONJUGATE GRADIENT.- FAST IMAGE MOSAICS ALGORITHM USING PARTICLE SWARM OPTIMIZATION.- CHECKING CONSISTENCY IN HYBRID QUALITATIVE SPATIAL REASONING.- ICA-SCS DENOISING METHOD FOR WATERMARKING SCHEME.- STUDY AND IMPLEMENTATION OF SIMPLIFIED WWW DATA MODEL IN USE FOR WEB MINING.- AN EXTENSION OF EARLEY'S ALGORITHM FOR EXTENDED GRAMMARS.- THE RESEARCH ON POLICY-BASED REGISTRATION MECHANISM OF MOBILE TERMINALS IN MOBILE IP NETWORK.- OPERATIONAL SEMANTIC TO THE EXECUTION OF THE PROCESS MODEL.- FUZZY CLUSTERING ON THE WEB IMPLEMENTED BY JSP TECHNOLOGY.- TEXT CLASSIFICATION FOR CHINESE WEB DOCUMENTS.- LOGIC-BASED CONSTRAINT HANDLING IN RESOURCE-CONSTRAINED SCHEDULING PROBLEMS.- PARTICLE SWARM OPTIMIZATION METHOD USED IN PIXEL-BASED TEXTURE SYNTHESIS.- FORMAL METHOD IN IMPLEMENTATION OF ATLAS LANGUAGE*.- GENETIC ALGORITHM FOR EVALUATION METRICS IN TOPICAL WEB CRAWLING.- A BOUNDARY METHOD TO SPEED UP TRAINING SUPPORT VECTOR MACHINES.- A FAST ALGORITHM FOR GENERATING CONCEPTS USING AN ATTRIBUTE TABLE.- ALGORITHM OF MEASUREMENT-BASED ADMISSION CONTROL FOR GPRS.- SURFACE RECOGNITION OF AUTOMOBILE PANEL BASED ON SECTION CURVE IDENTIFICATION.- DYNAMIC CLUSTERING ALGORITHM BASED ON ADAPTIVE RESONANCE THEORY.- ONTOLOGY LEARNING USING WORDNET LEXICON.- GENETIC PROGRAMMING FOR MAXIMUM-LIKELIHOOD PHYLOGENY INFERENCE.- MINING DOMINANCE ASSOCIATION RULES IN PREFERENCE-ORDERED DATA.- Mining Ordinal Patterns For Data Cleaning.- USER ASSOCIATION MINING BASED ON CONCEPT LATTICE.- A PLM-ORIENTED WORKFLOW MODEL.- A LANGUAGE FOR SPECIFYING CONSTRAINTS IN WFMSs.- ONTOLOGY BASED WORKFLOW MODEL.- CONSTRAINED MULTI-SAMPLE TEXTURE SYNTHESIS.- A GENERAL INCREMENTAL HIERARCHICAL CLUSTERING METHOD.- ACTIVATING IRREGULAR DIMENSIONS IN OLAP.- A NEW COMPUTATIONAL METHOD OF INTERSECTION FOR RAY TRACING.- STUDY ON PARTNERS SELECTION OF AGILE VIRTUAL ENTERPRISE BASED ON PARTICLE SWARM OPTIMIZATION.- ADAPTIVE DIRECTIONAL WEIGHTED MEDIAN FILTERING.- AN INTERACTIVE WORKFLOW MANAGEMENT FUNCTION MODEL.- PACKET FAIR SCHEDULING ALGORITHM BASED ON WEIGHTS DYNAMIC COMPENSATION.- EXTENDED ADAPTIVE WEIGHTED AVERAGING FILTER MODEL.- AN IMPROVED QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR CLUSTERING GENE EXPRESSION DATA.- AUTOMATIC BUFFER OVERFLOW DETECTION BASED ON OPERATION SEMANTIC.- QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR TRAVELLING SALESMAN PROBLEM.- AN IMPROVED COEVOLUTION ALGORITHM FOR FUZZY MODELLING.- A COMPUTATIONAL METHOD FOR SOLVING CAUCHY PROBLEMS OF ELLIPTIC OPERATORS.- NUMERICAL DETERMINATION OF THE RESONANCE FREQUENCIES AND EIGENMODES USING THE MFS.- SCATTERED NODE COMPACT FINITE DIFFERENCE-TYPE FORMULAS GENERATED FROM RADIAL BASIS FUNCTIONS.- EXPLOSION SIMULATI

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

The book is a comprehensive yet compressed entry-level introduction on single variable calculus, focusing on the concepts and applications of limits, continuity, derivative, defi nite integral, series, sequences and approximations. Chapters are arranged to outline the essence of each topic and to address learning diffi culties, making it suitable for students and lecturers in mathematics, physics and engineering. Contents Prerequisites for calculus Limits and continuity The derivative Applications of the derivative The definite integral Techniques for integration and improper integrals Applications of the definite integral Infinite series, sequences, and approximations

This book presents a method for evaluating Selberg zeta functions via transfer operators for the full modular group and its congruence subgroups with characters. Studying zeros of Selberg zeta functions for character deformations allows us to access the discrete spectra and resonances of hyperbolic Laplacians under both singular and non-singular perturbations. Areas in which the theory has not yet been sufficiently developed, such as the spectral theory of transfer operators or the singular perturbation theory of hyperbolic Laplacians, will profit from the numerical experiments discussed in this book. Detailed descriptions of numerical approaches to the spectra and eigenfunctions of transfer operators and to computations of Selberg zeta functions will be of value to researchers active in analysis, while those researchers focusing more on numerical aspects will benefit from discussions of the analytic theory, in particular those concerning the transfer operator method and the spectral theory of hyperbolic spaces.

This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier-Stokes equations, and systems of nonlinear advection-diffusion-reaction equations, it guides readers through the essential steps to quickly solving a PDE in FEniCS, such as how to define a finite variational problem, how to set boundary conditions, how to solve linear and nonlinear systems, and how to visualize solutions and structure finite element Python programs. This book is open access under a CC BY license.

This interdisciplinary book presents numerical techniques needed for chemical and biological engineers using Matlab. The book begins by exploring general cases, and moves on to specific ones. The text includes a large number of detailed illustrations, exercises and industrial examples. The book provides detailed mathematics and engineering background in the appendixes, including an introduction to Matlab. The text will be useful to undergraduate students in chemical/biological engineering, and in applied mathematics and numerical analysis.

Featuring up-to-date coverage of three topics lying at the intersection of combinatorics and commutative algebra, namely Koszul algebras, primary decompositions and subdivision operations in simplicial complexes, this book has its focus on computations. "Computations and Combinatorics in Commutative Algebra" has been written by experts in both theoretical and computational aspects of these three subjects and is aimed at a broad audience, from experienced researchers who want to have an easy but deep review of the topics covered to postgraduate students who need a quick introduction to the techniques. The computational treatment of the material, including plenty of examples and code, will be useful for a wide range of professionals interested in the connections between commutative algebra and combinatorics.

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll Learn Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high Who This Book Is For Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Michael Holzhauser discusses generalizations of well-known network flow and packing problems by additional or modified side constraints. By exploiting the inherent connection between the two problem classes, the author investigates the complexity and approximability of several novel network flow and packing problems and presents combinatorial solution and approximation algorithms.

Familiarize yourself with Scilab using this concise, practical tutorial that is focused on writing code to learn concepts. Starting from the basics, this book covers array-based computing, plotting, and working with files in Scilab. Introduction to Scilab is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you'll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. After reading this book, you will come away with sample code that can be re-purposed and applied to your own projects using Scilab. What You'll Learn Apply sample code to your engineering or science problems Work with Scilab arrays, functions, and loops Use Scilab's plotting functions for data visualization Solve numerical computing and computational engineering problems with Scilab Who This Book Is For Engineers, scientists, researchers, and students who are new to Scilab. Some prior programming experience would be helpful but not required.

This book combines mathematics (geometry and topology), computer science (algorithms), and engineering (mesh generation) in order to solve the conceptual and technical problems in the combining of elements of combinatorial and numerical algorithms. The book develops methods from areas that are amenable to combination and explains recent breakthrough solutions to meshing that fit into this category. It should be an ideal graduate text for courses on mesh generation. The specific material is selected giving preference to topics that are elementary, attractive, lend themselves to teaching, are useful, and interesting.

This book provides a valuable glimpse into discrete curvature, a rich new field of research which blends discrete mathematics, differential geometry, probability and computer graphics. It includes a vast collection of ideas and tools which will offer something new to all interested readers. Discrete geometry has arisen as much as a theoretical development as in response to unforeseen challenges coming from applications. Discrete and continuous geometries have turned out to be intimately connected. Discrete curvature is the key concept connecting them through many bridges in numerous fields: metric spaces, Riemannian and Euclidean geometries, geometric measure theory, topology, partial differential equations, calculus of variations, gradient flows, asymptotic analysis, probability, harmonic analysis, graph theory, etc. In spite of its crucial importance both in theoretical mathematics and in applications, up to now, almost no books have provided a coherent outlook on this emerging field.

This book presents a selection of papers based on the XXXIII Bialowieza Workshop on Geometric Methods in Physics, 2014. The Bialowieza Workshops are among the most important meetings in the field and attract researchers from both mathematics and physics. The articles gathered here are mathematically rigorous and have important physical implications, addressing the application of geometry in classical and quantum physics. Despite their long tradition, the workshops remain at the cutting edge of ongoing research. For the last several years, each Bialowieza Workshop has been followed by a School on Geometry and Physics, where advanced lectures for graduate students and young researchers are presented; some of the lectures are reproduced here. The unique atmosphere of the workshop and school is enhanced by its venue, framed by the natural beauty of the Bialowieza forest in eastern Poland.The volume will be of interest to researchers and graduate students in mathematical physics, theoretical physics and mathematmtics.

This comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way. The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds. All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.