After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment ...
Author: Robert Johansson
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.
Fitted numerical methods for delay differential equations arising in biology. PhD
thesis, University ... SIAM - Society for Industrial and Applied Mathematics, 2014. [
4] M.D. ... Exploring sparsity in the direct transcripion method for optimal control.
Author: Eihab B. M. Bashier
Publisher: CRC Press
This book concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors. The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems. This book has the following advantages: It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields. It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python. It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems. Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs. This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.
This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models.
Author: Serge Kruk
Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations. Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study. What You Will Learn Build basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suite Create several types of projects using Python and Google OR-Tools Who This Book Is For Developers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.
Chapter 7–Linear Algebra: Linear algebra (LA) techniques are used throughout
the area of financial engineering. ... Topics include (1) the bisection method, (2)
the secant method, and (3) Newton's method. ... However, it is hard to solve
certain classes of equations exactly, and numerical methods need to be
employed in such cases. ... the solutions and algorithms discussed in the text as
external libraries for scripting languages that are commonly employed in the
Author: Carlos Oliveira
Practical C++ Financial Programming is a hands-on book for programmers wanting to apply C++ to programming problems in the financial industry. The book explains those aspects of the language that are more frequently used in writing financial software, including the STL, templates, and various numerical libraries. The book also describes many of the important problems in financial engineering that are part of the day-to-day work of financial programmers in large investment banks and hedge funds. The author has extensive experience in the New York City financial industry that is now distilled into this handy guide. Focus is on providing working solutions for common programming problems. Examples are plentiful and provide value in the form of ready-to-use solutions that you can immediately apply in your day-to-day work. You’ll learn to design efficient, numerical classes for use in finance, as well as to use those classes provided by Boost and other libraries. You’ll see examples of matrix manipulations, curve fitting, histogram generation, numerical integration, and differential equation analysis, and you’ll learn how all these techniques can be applied to some of the most common areas of financial software development. These areas include performance price forecasting, optimizing investment portfolios, and more. The book style is quick and to-the-point, delivering a refreshing view of what one needs to master in order to thrive as a C++ programmer in the financial industry. Covers aspects of C++ especially relevant to financial programming. Provides working solutions to commonly-encountered problems in finance. Delivers in a refreshing and easy style with a strong focus on the practical.
A Practical Guide for Scientists and Engineers Using Python and C/C++ Titus A.
Beu ... or secondary textbooks for courses that wish to emphasize the importance
of numerical methods and computational tools in science. ... the physical
sciences as well as academic and industry professionals in physics, engineering,
computer science, applied math, and biology. Titles in the series are targeted to
specific disciplines that currently lack a textbook with a computational physics approach.
Author: Titus A. Beu
Publisher: CRC Press
Makes Numerical Programming More Accessible to a Wider Audience Bearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author’s many years of practical research and teaching experience to offer a systematic approach to relevant programming concepts. Adopting a practical, broad appeal, this user-friendly book offers guidance to anyone interested in using numerical programming to solve science and engineering problems. Emphasizing methods generally used in physics and engineering—from elementary methods to complex algorithms—it gradually incorporates algorithmic elements with increasing complexity. Develop a Combination of Theoretical Knowledge, Efficient Analysis Skills, and Code Design Know-How The book encourages algorithmic thinking, which is essential to numerical analysis. Establishing the fundamental numerical methods, application numerical behavior and graphical output needed to foster algorithmic reasoning, coding dexterity, and a scientific programming style, it enables readers to successfully navigate relevant algorithms, understand coding design, and develop efficient programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning. Begins with an overview on approximate numbers and programming in Python and C/C++, followed by discussion of basic sorting and indexing methods, as well as portable graphic functionality Contains methods for function evaluation, solving algebraic and transcendental equations, systems of linear algebraic equations, ordinary differential equations, and eigenvalue problems Addresses approximation of tabulated functions, regression, integration of one- and multi-dimensional functions by classical and Gaussian quadratures, Monte Carlo integration techniques, generation of random variables, discretization methods for ordinary and partial differential equations, and stability analysis This text introduces platform-independent numerical programming using Python and C/C++, and appeals to advanced undergraduate and graduate students in natural sciences and engineering, researchers involved in scientific computing, and engineers carrying out applicative calculations.
This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students ...
Author: Shayne Fletcher
Publisher: John Wiley & Sons
Category: Business & Economics
"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
Learn how to use Python to create efficient applications About This Book Identify the bottlenecks in your applications and solve them using the best profiling techniques Write efficient numerical code in NumPy, Cython, and Pandas Adapt your ...
Author: Gabriele Lanaro
Publisher: Packt Publishing Ltd
Learn how to use Python to create efficient applications About This Book Identify the bottlenecks in your applications and solve them using the best profiling techniques Write efficient numerical code in NumPy, Cython, and Pandas Adapt your programs to run on multiple processors and machines with parallel programming Who This Book Is For The book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expected What You Will Learn Write efficient numerical code with the NumPy and Pandas libraries Use Cython and Numba to achieve native performance Find bottlenecks in your Python code using profilers Write asynchronous code using Asyncio and RxPy Use Tensorflow and Theano for automatic parallelism in Python Set up and run distributed algorithms on a cluster using Dask and PySpark In Detail Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications. Style and approach A step-by-step practical guide filled with real-world use cases and examples
Over 60 practical recipes to help you explore Python and its robust data science capabilities About This Book The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action Explore ...
Author: Gopi Subramanian
Publisher: Packt Publishing Ltd
Over 60 practical recipes to help you explore Python and its robust data science capabilities About This Book The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes Who This Book Is For This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience. What You Will Learn Explore the complete range of Data Science algorithms Get to know the tricks used by industry engineers to create the most accurate data science models Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively Create meaningful features to solve real-world problems Take a look at Advanced Regression methods for model building and variable selection Get a thorough understanding of the underlying concepts and implementation of Ensemble methods Solve real-world problems using a variety of different datasets from numerical and text data modalities Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on In Detail Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way. This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly. The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional. Style and approach This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms.
The best way to use the book is to read it at the computer keyboard and learn by doing. The book begins by introducing basic skills: how to use the operating system, how to organize data, and how to complete simple programming tasks.
Author: Harry J. Paarsch
Publisher: MIT Press
This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; students entering PhD programs in business, economics, and other social or natural sciences; and those seeking quantitative jobs in industry. No background in computer science is assumed; a learner need only have a computer with access to the Internet. Using the example as its principal pedagogical device, the book offers tried-and-true prototypes that illustrate many important computational tasks required in quantitative research. The best way to use the book is to read it at the computer keyboard and learn by doing. The book begins by introducing basic skills: how to use the operating system, how to organize data, and how to complete simple programming tasks. For its demonstrations, the book uses a UNIX-based operating system and a set of free software tools: the scripting language Python for programming tasks; the database management system SQLite; and the freely available R for statistical computing and graphics. The book goes on to describe particular tasks: analyzing data, implementing commonly used numerical and simulation methods, and creating extensions to Python to reduce cycle time. Finally, the book describes the use of LaTeX, a document markup language and preparation system.
Hence it is necessary to learn the numerical methods with engineering
applications . ... Some of the problems are formulated with practical and
experimental details to explain the application of the methods . ... of the most
efficient and sophisticated parts of the UK construction industry with an annual
value of approximately £1 billion for the frames and £1 . ... frequently used in
buckling as well as dynamic analysis are explained in the Jacobi method ,
inverse power and power methods .
About This Book Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning Want to perform faster and more accurate computations in the field of ...
Author: Giancarlo Zaccone
Publisher: Packt Publishing Ltd
Get up and running with the latest numerical computing library by Google and dive deeper into your data! About This Book Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow! Dive into the next generation of numerical computing and get the most out of your data with this quick guide Who This Book Is For This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful. What You Will Learn Install and adopt TensorFlow in your Python environment to solve mathematical problems Get to know the basic machine and deep learning concepts Train and test neural networks to fit your data model Make predictions using regression algorithms Analyze your data with a clustering procedure Develop algorithms for clustering and data classification Use GPU computing to analyze big data In Detail Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks. This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples. By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application. Style and approach Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and ...
Author: Prabhanjan Tattar
Publisher: Packt Publishing Ltd
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
Author: Ravishankar ChityalaPublish On: 2014-02-19
Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together.
Author: Ravishankar Chityala
Publisher: CRC Press
Category: Technology & Engineering
Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples. A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The last part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry.
You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.
Author: Manuel Amunategui
The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
Author: Ian Goodfellow
Publisher: MIT Press
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Through this work, frequency-domain Maxwell's equations have been discretised using EFEM and validated by comparison with analytical solutions and published data, proving that modelling results are highly accurate.
Author: Octavio Castillo Reyes
Electromagnetic methods (EM) are an invaluable research tool in geophysics whose relevance has increased rapidly in recent years due to its wide industrial adoption. In particular, the forward modelling of three-dimensional marine controlled-source electromagnetics (3D CSEM FM) has become an important technique for reducing ambiguities in the interpretation of geophysical datasets through mapping conductivity variations in the subsurface. As a consequence, the 3D CSEM FM has application in many areas such as hydrocarbon/mineral exploration, reservoir monitoring, CO2 storage characterization, geothermal reservoir imaging and many others due to there quantities often displaying conductivity contrasts with respect to their surrounding sediments. However, the 3D CSEM FM at real scale implies a numerical challenge that requires an important computational effort, often too high for modest multicore computing architectures, especially if it fuels an inversion process. On the other hand, although the HPC code development is dominated by compiled languages, the popularity of high-level languages for scientific computations has increased considerably. Among all of them, Python is probably the language that has shown more interest, mainly because of flexibility and its simple and clean syntax. However, its use for HPC geophysical applications is still limited, which suggests a path for research, development and improvement. Therefore, this thesis reports the attempts at designing and implementing a methodology that has not been systematically applied for solving 3D CSEM FM with an HPC application baked upon Python. The net contribution of this effort is the development and documentation of a new open-source modelling code for 3D CSEM FM in geophysics, namely, the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM). The importance of having this modelling tools lies in the fact that they provide synthetic results that can be compared with real data which has a practical use both in the industry and academia. Still, available 3D CSEM FM codes are usually written in low-level languages whose implemented methods are often innaccessible to the scientific community since they are commercial. PETGEM is written mostly in Python and relies on mpi4py and petsc4py packages for parallel computations. Other scientific Python packages used include Numpy andScipy. This code is designed to cope with the main challenges encountered within the numerical simulation of the problem under consideration: tackle realistic problems with accuracy, efficiency and flexibility. It uses the Nédélec Edge Finite Element Method (EFEM) as discretisation technique because its divergence-free basis is very well suited for solving Maxwell¿s equations. Furthermore, it supports completely unstructured tetrahedral meshes which allows the representation of complex geometries and local refinement, positively impacting the accuracy of the solution. The parallel implementation of the code using shared/distributed-memory architectures is investigated and described throughout this document. In addition, the thesis deals with the numerical and physical challenges of the 3D CSEM FM problem. Through this work, frequency-domain Maxwell's equations have been discretised using EFEM and validated by comparison with analytical solutions and published data, proving that modelling results are highly accurate. Moreover, this work discusses an automatic mesh adaptation strategy and the convergence rate of the iterative solvers that are widely used in the literature for solving the EM problem is presented. In summary, this thesis shows that it is possible to integrate Python and HPC for the solution of 3D CSEM FM at large scale in an effective way. The new modelling tool is easy to use and the adopted algorithms are not only accurate and efficient but also have the possibility to easily add or remove components without having to rewrite large sections of the code.
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Collection of selected, peer reviewed papers from the 2013 2nd International Conference on Information Technology and Management Innovation (ICITMI 2013), July 23-24, 2013, Zhuhai, China. Volume is indexed by Thomson Reuters CPCI-S (WoS).
Author: Prasad Yarlagadda
Publisher: Trans Tech Publications Ltd
Category: Technology & Engineering
Collection of selected, peer reviewed papers from the 2013 2nd International Conference on Information Technology and Management Innovation (ICITMI 2013), July 23-24, 2013, Zhuhai, China. Volume is indexed by Thomson Reuters CPCI-S (WoS). The 642 papers are grouped as follows: Chapter 1: Information Processing and Information Security; Chapter 2: Information Storage and Database System; Chapter 3: Software Engineering; Chapter 4: Computer Networks; Chapter 5: Modern Technologies in Communication and Navigation; Chapter 6: Multimedia Technology; Chapter 7: Data and Signal Processing; Chapter 8: Processing Image and Video; Chapter 9: Applied and Computational Mathematics; Chapter 10: Sensors, Detection Technology and Instrument; Chapter 11: Circuit Theory and Microelectronic Devices and Technologies; Chapter 12: Automation, Control and Mechatronics; Chapter 13: Artificial Intelligence and Optimization Algorithm; Chapter 14: E-commerce, E-government and Management; Chapter 15: Enterprise Resource Planning, Management System and Engineering Management; Chapter 16: Innovative Decisions in Transportation, Supply Chain and Logistic; Chapter 17: Information and Innovation Technologies in Engineering Education; Chapter 18: Applied Research in Materials, Mechanical Engineering and Technologies of Manufacture and Processing; Chapter 19: Applied Biotechnologies.
Author: Institute for Operations Research and the Management Sciences. National MeetingPublish On: 2008
Using martingale techniques, we obtain functional central limit type
approximations for the state. ... can be used to combine the power of math
programming with the flexibility of simulation to provide practical solutions to
complex problems. This tutorial ... These ideas will be illustrated using industrial
applications in transportation, energy and finance. ... and the power of the Python
language promote reusability, readability and rapid development without
sacrificing numerical efficiency.
Author: Institute for Operations Research and the Management Sciences. National Meeting