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| numerical recipes in java: Numerical Recipes 3rd Edition William H. Press, 2007-09-06 Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in colour for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions. For support, or to subscribe to an online version, please visit www.nr.com. |
| numerical recipes in java: A Numerical Library in Java for Scientists and Engineers Hang T. Lau, 2003-08-27 At last researchers have an inexpensive library of Java-based numeric procedures for use in scientific computation. The first and only book of its kind, A Numeric Library in Java for Scientists and Engineers is a translation into Java of the library NUMAL (NUMerical procedures in ALgol 60). This groundbreaking text presents procedural descriptions for linear algebra, ordinary and partial differential equations, optimization, parameter estimation, mathematical physics, and other tools that are indispensable to any dynamic research group. The book offers test programs that allow researchers to execute the examples provided; users are free to construct their own tests and apply the numeric procedures to them in order to observe a successful computation or simulate failure. The entry for each procedure is logically presented, with name, usage parameters, and Java code included. This handbook serves as a powerful research tool, enabling the performance of critical computations in Java. It stands as a cost-efficient alternative to expensive commercial software package of procedural components. |
| numerical recipes in java: Numerical Recipes in C William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T. Vetterling, 1992-10-30 The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today. |
| numerical recipes in java: Object-Oriented Implementation of Numerical Methods Didier H. Besset, 2001 There are few books that show how to build programs of any kind. One common theme is compiler building, and there are shelves full of them. There are few others. It's an area, or a void, that needs filling. this book does a great job of showing how to build numerical analysis programs. -David N. Smith, IBM T J Watson Research Center Numerical methods naturally lend themselves to an object-oriented approach. Mathematics builds high- level ideas on top of previously described, simpler ones. Once a property is demonstrated for a given concept, it can be applied to any new concept sharing the same premise as the original one, similar to the ideas of reuse and inheritance in object-oriented (OO) methodology. Few books on numerical methods teach developers much about designing and building good code. Good computing routines are problem-specific. Insight and understanding are what is needed, rather than just recipes and black box routines. Developers need the ability to construct new programs for different applications. Object-Oriented Implementation of Numerical Methods reveals a complete OO design methodology in a clear and systematic way. Each method is presented in a consistent format, beginning with a short explanation and following with a description of the general OO architecture for the algorithm. Next, the code implementations are discussed and presented along with real-world examples that the author, an experienced software engineer, has used in a variety of commercial applications. Features: Reveals the design methodology behind the code, including design patterns where appropriate, rather than just presenting canned solutions. Implements all methods side by side in both Java and Smalltalk. This contrast can significantly enhance your understanding of the nature of OO programming languages. Provides a step-by-step pathway to new object-oriented techniques for programmers familiar with using procedural languages such as C or Fortran for numerical methods. Includes a chapter on data mining, a key application of numerical methods. |
| numerical recipes in java: Numerical Recipes in FORTRAN 77: Volume 1, Volume 1 of Fortran Numerical Recipes William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T. Vetterling, 1992-09-25 This is the greatly revised and greatly expanded Second Edition of the hugely popular Numerical Recipes: The Art of Scientific Computing. The product of a unique collaboration among four leading scientists in academic research and industry Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, this new edition remains the most practical, comprehensive handbook of scientific computing available today. Highlights of the new material include: -A new chapter on integral equations and inverse methods -Multigrid and other methods for solving partial differential equations -Improved random number routines - Wavelet transforms -The statistical bootstrap method -A new chapter on less-numerical algorithms including compression coding and arbitrary precision arithmetic. The book retains the informal easy-to-read style that made the first edition so popular, while introducing some more advanced topics. It is an ideal textbook for scientists and engineers and an indispensable reference for anyone who works in scientific computing. The Second Edition is availabe in FORTRAN, the traditional language for numerical calculations and in the increasingly popular C language. |
| numerical recipes in java: JavaTech, an Introduction to Scientific and Technical Computing with Java Clark S. Lindsey, Johnny S. Tolliver, Thomas Lindblad, 2005-10-13 JavaTech demonstrates the ease with which Java can be used to create powerful network applications and distributed computing applications. It can be used as a textbook for introductory or intermediate level programming courses, and for more advanced students and researchers who need to learn Java for a particular task. JavaTech is up to date with Java 5.0.--BOOK JACKET. |
| numerical recipes in java: Java Methods for Financial Engineering Philip Barker, 2007-05-16 In order to build a successful, Java-based application it is important to have a clear understanding of the principles underlying the various financial models. Those models guide the application designer in choosing the most appropriate Java data structures and implementation strategy. This book describes the principles of model building in financial engineering and explains those models as designs and working implementations for Java-based applications. Throughout the book a series of packaged classes are developed to address a wide range of financial applications. Java methods are designed and implemented based on the most widely used models in financial engineering and investment practice. The classes and methods are explained and designed in a way which allows the financial engineer complete flexibility. The classes can be used as off-the-shelf working solutions or the innovative developer can re-arrange and modify methods to create new products |
| numerical recipes in java: Numerical Recipes with Source Code CD-ROM 3rd Edition William H. Press, Saul A. Teukolsky, 2007-09 The complete Numerical Recipes 3rd edition book/CD bundle, with a hundred new routines, two new chapters and much more. |
| numerical recipes in java: Numerical Methods for Engineers and Scientists, Second Edition, Joe D. Hoffman, Steven Frankel, 2001-05-31 Emphasizing the finite difference approach for solving differential equations, the second edition of Numerical Methods for Engineers and Scientists presents a methodology for systematically constructing individual computer programs. Providing easy access to accurate solutions to complex scientific and engineering problems, each chapter begins with objectives, a discussion of a representative application, and an outline of special features, summing up with a list of tasks students should be able to complete after reading the chapter- perfect for use as a study guide or for review. The AIAA Journal calls the book ...a good, solid instructional text on the basic tools of numerical analysis. |
| numerical recipes in java: Java Cookbook Ian F. Darwin, 2025-02-06 As Java continues to evolve, this cookbook continues to grow in tandem with hundreds of hands-on recipes across a broad range of Java topics. Author Ian Darwin gets developers up to speed right away with useful techniques for everything from string handling and functional programming to network communication and AI. If you're familiar with any release of Java, this book will bolster your knowledge of the language and its many recent changes, including how to apply them in your day-to-day development. Each recipe includes self-contained code solutions that you can freely use, along with a discussion of how and why they work. Downloadable from GitHub, all code examples compile successfully. This updated edition covers changes up to and including Java 21. You will: Learn how to apply many new and old Java APIs Use the new language features in recent Java versions Understand the code you're maintaining Develop code using standard APIs and good practices Explore the brave new world of current Java development Ian Darwin has a lifetime of experience in the software industry, having worked with Java across many platforms and types of software, from Java's initial pre-release to the present, from desktop to enterprise to mobile. |
| numerical recipes in java: Java EE 8 Recipes Josh Juneau, 2018-06-11 Quickly find solutions to dozens of common programming problems with the Java Enterprise Edition Platform for small business web applications, enterprise database applications, and microservices solutions. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! Java EE 8 Recipes provides you with effective and proven solutions that can be used to accomplish just about any task that you may encounter. You can feel confident using the reliable solutions that are demonstrated in this book in your personal or corporate environment. Java is a mature programming language that has been refined over the years into a productive and lucrative language for those with the skills to wield it. One result of this years-long refining process is that that the language carries forward many older feature sets that no longerrepresent the best way of getting work accomplished. You can rest assured that Java EE 8 Recipes provides solutions using the most current approaches implemented in the most current Java Enterprise technologies, including JSON-P 1.1, JSF 2.3, and JAX-RS 2.1. Build a streamlined and reliable application that uses the latest in Java technologies, and develop it much faster than you did with the older technologies. Rejuvenate your Java expertise to use the freshest capabilities, or perhaps learn Java Enterprise development for the first time and discover one of the most widely used and most powerful technologies available for application development today. Develop productively. Develop with proven technology. Develop with Java Enterprise Edition. The book: Teaches how to develop RESTful enterprise applications quickly using the most current Java EE technologies Explores different solutions for developing sophisticated webuser interfaces Walks you through a myriad of different concepts to apply while working with databases using Java technologies What You'll Learn Develop Java Enterprise applications using the latest in Java EE technologies Build great-looking user interfaces using Java Server Faces Employ Java Servlet technology and standard frameworks in developing professional web applications Create enterprise-level database applications using Enterprise Java Beans and JAX-RS RESTFul web services Make use of Arquillian to build a cohesive test suite for Java EE applications Manage Java EE application security through Java EE's container feature set Who This Book Is For Java developers who want to develop effective and proven solutions without reading a lengthy manual and scrubbing for techniques. A beginning Java programmer will find the book handy for learning a variety of different solutions for the platform, while advanced developers will enjoy the ease of the problem-solution approach to quickly broaden their knowledge of the platform’s latest technologies. |
| numerical recipes in java: MATLAB Recipes Michael Paluszek, Stephanie Thomas, 2015-11-23 Learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. This is a practical reference for industry engineers using MATLAB to solve everyday problems. With MATLAB Recipes: A Problem-Solution Approach you will review contemporary MATLAB coding including the latest language features and use MATLAB as a software development environment including code organization, GUI development, and algorithm design and testing. This book provides practical guidance for using MATLAB to build a body of code you can turn to time and again for solving technical problems in your line of work. Develop algorithms, test them, visualize the results, and pass the code along to others to create a functional code base for your firm. |
| numerical recipes in java: Numerical Algorithms Justin Solomon, 2015-06-24 Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig |
| numerical recipes in java: Java Data Analysis John R. Hubbard, 2017-09-19 Get the most out of the popular Java libraries and tools to perform efficient data analysis About This Book Get your basics right for data analysis with Java and make sense of your data through effective visualizations. Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning. This is your companion to understanding and implementing a solid data analysis solution using Java Who This Book Is For If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required. What You Will Learn Develop Java programs that analyze data sets of nearly any size, including text Implement important machine learning algorithms such as regression, classification, and clustering Interface with and apply standard open source Java libraries and APIs to analyze and visualize data Process data from both relational and non-relational databases and from time-series data Employ Java tools to visualize data in various forms Understand multimedia data analysis algorithms and implement them in Java. In Detail Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you'll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you'll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy-to-follow examples, this book will turn you into an ace data analyst in no time. |
| numerical recipes in java: Java Deep Learning Cookbook Rahul Raj, 2019-11-08 Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch Key FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratchExplore recipes for developing, training, and fine-tuning your neural network models in JavaModel neural networks using datasets containing images, text, and time-series dataBook Description Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently. This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results. By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java. What you will learnPerform data normalization and wrangling using DL4JBuild deep neural networks using DL4JImplement CNNs to solve image classification problemsTrain autoencoders to solve anomaly detection problems using DL4JPerform benchmarking and optimization to improve your model's performanceImplement reinforcement learning for real-world use cases using RL4JLeverage the capabilities of DL4J in distributed systemsWho this book is for If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book. |
| numerical recipes in java: Clever Algorithms Jason Brownlee, 2011 This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language. |
| numerical recipes in java: Data Science with Java Michael R. Brzustowicz, PhD, 2017-06-06 Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms |
| numerical recipes in java: NUMERICAL METHODS, ALGORITHMS AND TOOLS IN C#. WALDEMAR. DOS PASSOS, 2020 |
| numerical recipes in java: Regular Expressions Cookbook Jan Goyvaerts, Steven Levithan, 2012-08-13 Take the guesswork out of using regular expressions. With more than 140 practical recipes, this cookbook provides everything you need to solve a wide range of real-world problems. Novices will learn basic skills and tools, and programmers and experienced users will find a wealth of detail. Each recipe provides samples you can use right away. This revised edition covers the regular expression flavors used by C#, Java, JavaScript, Perl, PHP, Python, Ruby, and VB.NET. You’ll learn powerful new tricks, avoid flavor-specific gotchas, and save valuable time with this huge library of practical solutions. Learn regular expressions basics through a detailed tutorial Use code listings to implement regular expressions with your language of choice Understand how regular expressions differ from language to language Handle common user input with recipes for validation and formatting Find and manipulate words, special characters, and lines of text Detect integers, floating-point numbers, and other numerical formats Parse source code and process log files Use regular expressions in URLs, paths, and IP addresses Manipulate HTML, XML, and data exchange formats Discover little-known regular expression tricks and techniques |
| numerical recipes in java: The Finite Element Method Darrell W. Pepper, Juan C. Heinrich, 2017-04-11 This self-explanatory guide introduces the basic fundamentals of the Finite Element Method in a clear manner using comprehensive examples. Beginning with the concept of one-dimensional heat transfer, the first chapters include one-dimensional problems that can be solved by inspection. The book progresses through more detailed two-dimensional elements to three-dimensional elements, including discussions on various applications, and ending with introductory chapters on the boundary element and meshless methods, where more input data must be provided to solve problems. Emphasis is placed on the development of the discrete set of algebraic equations. The example problems and exercises in each chapter explain the procedure for defining and organizing the required initial and boundary condition data for a specific problem, and computer code listings in MATLAB and MAPLE are included for setting up the examples within the text, including COMSOL files. Widely used as an introductory Finite Element Method text since 1992 and used in past ASME short courses and AIAA home study courses, this text is intended for undergraduate and graduate students taking Finite Element Methodology courses, engineers working in the industry that need to become familiar with the FEM, and engineers working in the field of heat transfer. It can also be used for distance education courses that can be conducted on the web. Highlights of the new edition include: - Inclusion of MATLAB, MAPLE code listings, along with several COMSOL files, for the example problems within the text. Power point presentations per chapter and a solution manual are also available from the web. - Additional introductory chapters on the boundary element method and the meshless method. - Revised and updated content. -Simple and easy to follow guidelines for understanding and applying the Finite Element Method. |
| numerical recipes in java: Numerical Methods in "C" J.G. Kori, 2006 |
| numerical recipes in java: Biological Modeling and Simulation Russell Schwartz, 2008-07-25 A practice-oriented survey of techniques for computational modeling and simulation suitable for a broad range of biological problems. There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. It collects in one place a selection of broadly useful models, algorithms, and theoretical analysis tools normally found scattered among many other disciplines. It thereby gives the aspiring student a bag of tricks that will serve him or her well in modeling problems drawn from numerous subfields of biology. These techniques are taught from the perspective of what the practitioner needs to know to use them effectively, supplemented with references for further reading on more advanced use of each method covered. The text, which grew out of a class taught at Carnegie Mellon University, covers models for optimization, simulation and sampling, and parameter tuning. These topics provide a general framework for learning how to formulate mathematical models of biological systems, what techniques are available to work with these models, and how to fit the models to particular systems. Their application is illustrated by many examples drawn from a variety of biological disciplines and several extended case studies that show how the methods described have been applied to real problems in biology. |
| numerical recipes in java: Data-Driven Modeling & Scientific Computation J. Nathan Kutz, 2013-08-08 The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: · statistics, · time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing. |
| numerical recipes in java: Numerical Python Robert Johansson, 2024-09-27 Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more. Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning. What You'll Learn Work with vectors and matrices using NumPy Review Symbolic computing with SymPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Understand statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis. |
| numerical recipes in java: Java Distributed Computing Jim Farley, 1998 This book shows how to build software in which two or more computers cooperate to produce results. It covers Java's RMI (Remote Method Invocation) facility, in addition to CORBA and strategies for developing a distributed framework. It pays attention to often-neglected issues such as protocol design, security, and bandwidth requirements. |
| numerical recipes in java: Spring 5.0 Cookbook Sherwin John Calleja Tragura, 2017-09-27 Over 100 hands-on recipes to build web applications easily and efficiently IN Spring 5.0 About This Book Solve real-world problems using the latest features of the Spring framework like Reactive Streams and the Functional Web Framework. Learn how to use dependency injection and aspect-oriented programming to write compartmentalized and testable code. Understand when to choose between Spring MVC and Spring Web Reactive for your projects Who This Book Is For Java developers who would like to gain in-depth knowledge of how to overcome problems that they face while developing great Spring applications. It will also cater to Spring enthusiasts, users and experts who need an arena for comparative analysis, new ideas and inquiries on some details regarding Spring 5.0 and its previous releases. A basic knowledge of Spring development is essential What You Will Learn Understand how functional programming and concurrency in JDK 1.9 works, and how it will affect Spring 5.0 Learn the importance and application of reactive programming in creating services, and also the process of creating asynchronous MVC applications Implement different Spring Data modules Integrate Spring Security to the container Create applications and deploy using Spring Boot Conceptualize the architecture behind Microservices and learn the details of its implementation Create different test cases for the components of Spring 5.0 components In Detail The Spring framework has been the go-to framework for Java developers for quite some time. It enhances modularity, provides more readable code, and enables the developer to focus on developing the application while the underlying framework takes care of transaction APIs, remote APIs, JMX APIs, and JMS APIs. The upcoming version of the Spring Framework has a lot to offer, above and beyond the platform upgrade to Java 9, and this book will show you all you need to know to overcome common to advanced problems you might face. Each recipe will showcase some old and new issues and solutions, right from configuring Spring 5.0 container to testing its components. Most importantly, the book will highlight concurrent processes, asynchronous MVC and reactive programming using Reactor Core APIs. Aside from the core components, this book will also include integration of third-party technologies that are mostly needed in building enterprise applications. By the end of the book, the reader will not only be well versed with the essential concepts of Spring, but will also have mastered its latest features in a solution-oriented manner. Style and Approach This book follows a cookbook style approach, presenting a problem and showing you how to overcome it with useful recipes. The examples provided will help you code along as you learn. |
| numerical recipes in java: Guide to Scientific Computing in C++ Joe Pitt-Francis, Jonathan Whiteley, 2012-02-15 This easy-to-read textbook/reference presents an essential guide to object-oriented C++ programming for scientific computing. With a practical focus on learning by example, the theory is supported by numerous exercises. Features: provides a specific focus on the application of C++ to scientific computing, including parallel computing using MPI; stresses the importance of a clear programming style to minimize the introduction of errors into code; presents a practical introduction to procedural programming in C++, covering variables, flow of control, input and output, pointers, functions, and reference variables; exhibits the efficacy of classes, highlighting the main features of object-orientation; examines more advanced C++ features, such as templates and exceptions; supplies useful tips and examples throughout the text, together with chapter-ending exercises, and code available to download from Springer. |
| numerical recipes in java: Parallel Scientific Computing in C++ and MPI George Em Karniadakis, Robert M. Kirby II, 2003-06-16 Numerical algorithms, modern programming techniques, and parallel computing are often taught serially across different courses and different textbooks. The need to integrate concepts and tools usually comes only in employment or in research - after the courses are concluded - forcing the student to synthesise what is perceived to be three independent subfields into one. This book provides a seamless approach to stimulate the student simultaneously through the eyes of multiple disciplines, leading to enhanced understanding of scientific computing as a whole. The book includes both basic as well as advanced topics and places equal emphasis on the discretization of partial differential equations and on solvers. Some of the advanced topics include wavelets, high-order methods, non-symmetric systems, and parallelization of sparse systems. The material covered is suited to students from engineering, computer science, physics and mathematics. |
| numerical recipes in java: Collecting, Processing and Presenting Geoscientific Information Martin H. Trauth, Elisabeth Sillmann, 2018-03-26 This second edition is an intensively revised and updated version of the book MATLAB® and Design Recipes for Earth Sciences. It aims to introduce students to the typical course followed by a data analysis project in earth sciences. A project usually involves searching relevant literature, reviewing and ranking published books and journal articles, extracting relevant information from the literature in the form of text, data, or graphs, searching and processing the relevant original data using MATLAB, and compiling and presenting the results as posters, abstracts, and oral presentations using graphics design software. The text of this book includes numerous examples on the use of internet resources, on the visualization of data with MATLAB, and on preparing scientific presentations. As with the book MATLAB Recipes for Earth Sciences–4rd Edition (2015), which demonstrates the use of statistical and numerical methods on earth science data, this book uses state-of-the art software packages, including MATLAB and the Adobe Creative Suite, to process and present geoscientific information collected during the course of an earth science project. The book's supplementary electronic material (available online through the publisher's website) includes color versions of all figures, recipes with all the MATLAB commands featured in the book, the example data, exported MATLAB graphics, and screenshots of the most important steps involved in processing the graphics. |
| numerical recipes in java: NumPy Cookbook Ivan Idris, 2012-10-25 Written in Cookbook style, the code examples will take your Numpy skills to the next level. This book will take Python developers with basic Numpy skills to the next level through some practical recipes. |
| numerical recipes in java: Fortran 8x Explained Michael Metcalf, John Ker Reid, 1989 A new edition of this work on FORTRAN 8X, covering language, programming and procedures. It is aimed at FORTRAN users and programming language specialists. |
| numerical recipes in java: A First Course in Scientific Computing Rubin Landau, 2011-10-30 This book offers a new approach to introductory scientific computing. It aims to make students comfortable using computers to do science, to provide them with the computational tools and knowledge they need throughout their college careers and into their professional careers, and to show how all the pieces can work together. Rubin Landau introduces the requisite mathematics and computer science in the course of realistic problems, from energy use to the building of skyscrapers to projectile motion with drag. He is attentive to how each discipline uses its own language to describe the same concepts and how computations are concrete instances of the abstract. Landau covers the basics of computation, numerical analysis, and programming from a computational science perspective. The first part of the printed book uses the problem-solving environment Maple as its context, with the same material covered on the accompanying CD as both Maple and Mathematica programs; the second part uses the compiled language Java, with equivalent materials in Fortran90 on the CD; and the final part presents an introduction to LaTeX replete with sample files. Providing the essentials of computing, with practical examples, A First Course in Scientific Computing adheres to the principle that science and engineering students learn computation best while sitting in front of a computer, book in hand, in trial-and-error mode. Not only is it an invaluable learning text and an essential reference for students of mathematics, engineering, physics, and other sciences, but it is also a consummate model for future textbooks in computational science and engineering courses. A broad spectrum of computing tools and examples that can be used throughout an academic career Practical computing aimed at solving realistic problems Both symbolic and numerical computations A multidisciplinary approach: science + math + computer science Maple and Java in the book itself; Mathematica, Fortran90, Maple and Java on the accompanying CD in an interactive workbook format |
| numerical recipes in java: A Logical Approach to Discrete Math David Gries, Fred B. Schneider, 2013-03-14 This text attempts to change the way we teach logic to beginning students. Instead of teaching logic as a subject in isolation, we regard it as a basic tool and show how to use it. We strive to give students a skill in the propo sitional and predicate calculi and then to exercise that skill thoroughly in applications that arise in computer science and discrete mathematics. We are not logicians, but programming methodologists, and this text reflects that perspective. We are among the first generation of scientists who are more interested in using logic than in studying it. With this text, we hope to empower further generations of computer scientists and math ematicians to become serious users of logic. Logic is the glue Logic is the glue that binds together methods of reasoning, in all domains. The traditional proof methods -for example, proof by assumption, con tradiction, mutual implication, and induction- have their basis in formal logic. Thus, whether proofs are to be presented formally or informally, a study of logic can provide understanding. |
| numerical recipes in java: Java Deep Learning Projects Md. Rezaul Karim, 2018-06-29 Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book Description Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. What you will learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs Who this book is for If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required. |
| numerical recipes in java: Python for Scientists John M. Stewart, 2017-07-20 Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively. |
| numerical recipes in java: Modern Java Recipes Ken Kousen, 2017-08-11 The introduction of functional programming concepts in Java SE 8 was a drastic change for this venerable object-oriented language. Lambda expressions, method references, and streams fundamentally changed the idioms of the language, and many developers have been trying to catch up ever since. This cookbook will help. With more than 70 detailed recipes, author Ken Kousen shows you how to use the newest features of Java to solve a wide range of problems. For developers comfortable with previous Java versions, this guide covers nearly all of Java SE 8, and includes a chapter focused on changes coming in Java 9. Need to understand how functional idioms will change the way you write code? This cookbook—chock full of use cases—is for you. Recipes cover: The basics of lambda expressions and method references Interfaces in the java.util.function package Stream operations for transforming and filtering data Comparators and Collectors for sorting and converting streaming data Combining lambdas, method references, and streams Creating instances and extract values from Java’s Optional type New I/O capabilities that support functional streams The Date-Time API that replaces the legacy Date and Calendar classes Mechanisms for experimenting with concurrency and parallelism |
| numerical recipes in java: Numerical Methods in Engineering with Python 3 Jaan Kiusalaas, 2013-01-21 Provides an introduction to numerical methods for students in engineering. It uses Python 3, an easy-to-use, high-level programming language. |
| numerical recipes in java: Java 8 Lambdas Richard Warburton, 2014-03-18 If you’re a developer with core Java SE skills, this hands-on book takes you through the language changes in Java 8 triggered by the addition of lambda expressions. You’ll learn through code examples, exercises, and fluid explanations how these anonymous functions will help you write simple, clean, library-level code that solves business problems. Lambda expressions are a fairly simple change to Java, and the first part of the book shows you how to use them properly. Later chapters show you how lambda functions help you improve performance with parallelism, write simpler concurrent code, and model your domain more accurately, including building better DSLs. Use exercises in each chapter to help you master lambda expressions in Java 8 quickly Explore streams, advanced collections, and other Java 8 library improvements Leverage multicore CPUs and improve performance with data parallelism Use techniques to “lambdify” your existing codebase or library code Learn practical solutions for lambda expression unit testing and debugging Implement SOLID principles of object-oriented programming with lambdas Write concurrent applications that efficiently perform message passing and non-blocking I/O |
| numerical recipes in java: Software Design for Engineers and Scientists John Allen Robinson, 2004-08-21 Software Design for Engineers and Scientists integrates three core areas of computing:. Software engineering - including both traditional methods and the insights of 'extreme programming'. Program design - including the analysis of data structures and algorithms. Practical object-oriented programmingWithout assuming prior knowledge of any particular programming language, and avoiding the need for students to learn from separate, specialised Computer Science texts, John Robinson takes the reader from small-scale programing to competence in large software projects, all within one volume. Copious examples and case studies are provided in C++.The book is especially suitable for undergraduates in the natural sciences and all branches of engineering who have some knowledge of computing basics, and now need to understand and apply software design to tasks like data analysis, simulation, signal processing or visualisation. John Robinson introduces both software theory and its application to problem solving using a range of design principles, applied to the creation of medium-sized systems, providing key methods and tools for designing reliable, efficient, maintainable programs. The case studies are presented within scientific contexts to illustrate all aspects of the design process, allowing students to relate theory to real-world applications. - Core computing topics - usually found in separate specialised texts - presented to meetthe specific requirements of science and engineering students - Demonstrates good practice through applications, case studies and worked examplesbased in real-world contexts |
| numerical recipes in java: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
NUMERICAL | English meaning - Cambridge Dictionary
Keep your files in numerical order. The UN forces have a numerical superiority over the rebels (= there are more of the UN forces). Keep your files in numerical order. The phone's voice …
NUMERICAL Definition & Meaning - Merriam-Webster
The meaning of NUMERICAL is of or relating to numbers. How to use numerical in a sentence.
Numerical - definition of numerical by The Free Dictionary
1. of or pertaining to numbers; of the nature of a number. 2. indicating a number, as a symbol. 3. bearing or designated by a number. 4. expressed in numbers: numerical equations. 5. noting …
Numeric vs. Numerical: What’s the Difference?
Oct 3, 2023 · In programming and computing, "Numeric" often alludes to data types or fields that can hold data represented by numbers, whereas "Numerical" might be used to describe …
What does numerical mean? - Definitions.net
Numerical refers to anything related to or involving numbers. It can refer to the use, manipulation, representation, or analysis of numbers in various contexts such as mathematics, statistics, …
Numerical Definition & Meaning - YourDictionary
Numerical definition: Of or relating to a number or series of numbers.
Numerical analysis - Wikipedia
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from …
NUMERICAL Definition & Meaning - Dictionary.com
of or relating to numbers; of the nature of a number. numerical symbols. bearing or designated by a number. numerical cryptography; numerical equations. tests for rating numerical aptitude. …
Numerical - Definition, Meaning & Synonyms - Vocabulary.com
Anything numerical is related to numbers or can be expressed in numbers. Your height is a numerical concept, but your love for your friends is not. You will learn many numerical …
numerical adjective - Definition, pictures, pronunciation and …
Definition of numerical adjective in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
NUMERICAL | English meaning - Cambridge Dictionary
Keep your files in numerical order. The UN forces have a numerical superiority over the rebels (= there are more of the UN forces). Keep your files in numerical order. The phone's voice …
NUMERICAL Definition & Meaning - Merriam-Webster
The meaning of NUMERICAL is of or relating to numbers. How to use numerical in a sentence.
Numerical - definition of numerical by The Free Dictionary
1. of or pertaining to numbers; of the nature of a number. 2. indicating a number, as a symbol. 3. bearing or designated by a number. 4. expressed in numbers: numerical equations. 5. noting …
Numeric vs. Numerical: What’s the Difference?
Oct 3, 2023 · In programming and computing, "Numeric" often alludes to data types or fields that can hold data represented by numbers, whereas "Numerical" might be used to describe …
What does numerical mean? - Definitions.net
Numerical refers to anything related to or involving numbers. It can refer to the use, manipulation, representation, or analysis of numbers in various contexts such as mathematics, statistics, …
Numerical Definition & Meaning - YourDictionary
Numerical definition: Of or relating to a number or series of numbers.
Numerical analysis - Wikipedia
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from …
NUMERICAL Definition & Meaning - Dictionary.com
of or relating to numbers; of the nature of a number. numerical symbols. bearing or designated by a number. numerical cryptography; numerical equations. tests for rating numerical aptitude. …
Numerical - Definition, Meaning & Synonyms - Vocabulary.com
Anything numerical is related to numbers or can be expressed in numbers. Your height is a numerical concept, but your love for your friends is not. You will learn many numerical …
numerical adjective - Definition, pictures, pronunciation and …
Definition of numerical adjective in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
