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| numerical recipes source code: 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 source code: Numerical Recipes Example Book (C++) William T. Vetterling, 2002-02-07 Contains C++ source programs that exercise and demonstrate all of the subroutines, procedures, and functions in Numerical Recipes in C++. |
| numerical recipes source code: 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 source code: 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 source code: Analog Circuit Simulators for Integrated Circuit Designers Mikael Sahrling, 2021-03-26 Learn how analog circuit simulators work with these easy to use numerical recipes implemented in the popular Python programming environment. This book covers the fundamental aspects of common simulation analysis techniques and algorithms used in professional simulators today in a pedagogical way through simple examples. The book covers not just linear analyses but also nonlinear ones like steady state simulations. It is rich with examples and exercises and many figures to help illustrate the points. For the interested reader, the fundamental mathematical theorems governing the simulation implementations are covered in the appendices. Demonstrates circuit simulation algorithms through actual working code, enabling readers to build an intuitive understanding of what are the strengths and weaknesses with various methods Provides details of all common, modern circuit simulation methods in one source Provides Python code for simulations via download Includes transistor numerical modeling techniques, based on simplified transistor physics Provides detailed mathematics and ample references in appendices |
| numerical recipes source code: IPython Interactive Computing and Visualization Cookbook Cyrille Rossant, 2014-09-25 Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods. |
| numerical recipes source code: 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 source code: C++20 Recipes J. Burton Browning, Bruce Sutherland, 2020-11-12 Discover the newest major features of C++20, including modules, concepts, spaceship operators, and smart pointers. This book is a handy code cookbook reference guide that covers the C++ core language standard as well as some of the code templates available in standard template library (STL). In C++20 Recipes: A Problem-Solution Approach, you'll find numbers, strings, dates, times, classes, exceptions, streams, flows, pointers, and more. Also, you'll see various code samples, templates for C++ algorithms, parallel processing, multithreading, and numerical processes. It also includes 3D graphics programming code. A wealth of STL templates on function objects, adapters, allocators, and extensions are also available. This is a must-have, contemporary reference for your technical library to help with just about any project that involves the C++ programming language. What You Will Learn See what's new in C++20 Write modules Work with text, numbers, and classes Use the containers and algorithms available in the standard library Work with templates, memory, concurrency, networking, scripting, and more Code for 3D graphics Who This Book Is For Programmers with at least some prior experience with C++. |
| numerical recipes source code: 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 source code: 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 source code: 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 source code: LAPACK95 Users' Guide V. A. Barker, L. S. Blackford, J. Dongarra, J. Du Croz, S. Hammarling, M. Marinova, J. Wa?niewski, P. Yalamov, 2001-01-01 LAPACK95 Users' Guide provides an introduction to the design of the LAPACK95 package. |
| numerical recipes source code: Fundamentals of Data Visualization Claus O. Wilke, 2019-03-18 Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story |
| numerical recipes source code: 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 source code: Numerical Methods in Finance with C++ Maciej J. Capiński, Marek Capiński, Tomasz Zastawniak, 2012-08-02 This book provides aspiring quant developers with the numerical techniques and programming skills needed in quantitative finance. No programming background required. |
| numerical recipes source code: 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 source code: Common Lisp Recipes Edmund Weitz, 2016-01-01 Find solutions to problems and answers to questions you are likely to encounter when writing real-world applications in Common Lisp. This book covers areas as diverse as web programming, databases, graphical user interfaces, integration with other programming languages, multi-threading, and mobile devices as well as debugging techniques and optimization, to name just a few. Written by an author who has used Common Lisp in many successful commercial projects over more than a decade, Common Lisp Recipes is also the first Common Lisp book to tackle such advanced topics as environment access, logical pathnames, Gray streams, delivery of executables, pretty printing, setf expansions, or changing the syntax of Common Lisp. The book is organized around specific problems or questions each followed by ready-to-use example solutions and clear explanations of the concepts involved, plus pointers to alternatives and more information. Each recipe can be read independently of the others and thus the book will earn a special place on your bookshelf as a reference work you always want to have within reach. Common Lisp Recipes is aimed at programmers who are already familiar with Common Lisp to a certain extent but do not yet have the experience you typically only get from years of hacking in a specific computer language. It is written in a style that mixes hands-on no-frills pragmatism with precise information and prudent mentorship. If you feel attracted to Common Lisp's mix of breathtaking features and down-to-earth utilitarianism, you'll also like this book. |
| numerical recipes source code: Recipes for Continuation Harry Dankowicz, Frank Schilder, 2013-01-01 This book provides a comprehensive introduction to the mathematical methodology of parameter continuation, the computational analysis of families of solutions to nonlinear mathematical equations. It develops a systematic formalism for constructing abstract representations of continuation problems and for implementing these in an existing computational platform. Recipes for Continuation lends equal importance to theoretical rigor, algorithm development, and software engineering; demonstrates the use of fully developed toolbox templates for single- and multisegment boundary-value problems to the analysis of periodic orbits in smooth and hybrid dynamical systems, quasi-periodic invariant tori, and homoclinic and heteroclinic connecting orbits between equilibria and/or periodic orbits; shows the use of vectorization for optimal computational efficiency, an object-oriented paradigm for the modular construction of continuation problems, and adaptive discretization algorithms for guaranteed bounds on estimated errors; and contains extensive and fully worked examples that illustrate the application of the MATLAB®-based Computational Continuation Core (COCO) to problems from recent research literature that are relevant to dynamical system models from mechanics, electronics, biology, economics, and neuroscience. |
| numerical recipes source code: Python Recipes for Earth Sciences Martin H. Trauth, 2024-10-07 Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. Codes are available online through GitHub. |
| numerical recipes source code: Credit-Risk Modelling David Jamieson Bolder, 2018-10-31 The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline. |
| numerical recipes source code: 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 recipes source code: Numerical Recipes in FORTRAN 77: Volume 1, Volume 1 of Fortran Numerical Recipes William H. Press, Saul A. Teukolsky, Brian P. Flannery, William T. Vetterling, 1992-09-25 As with Numerical Recipes in C, the FORTRAN edition has been greatly revised to make this edition the most up to date handbook for those working with FORTRAN. Between both editions of Numerical Recipes, over 300,000 copies have been sold. |
| numerical recipes source code: VFLOW2D Walter P. Wolfe, James H. Strickland, Gregory F. Homicz, Albert A. Gossler, 2000 A numerical flow model is developed to simulate two-dimensional fluid flow past immersed, elastically supported tube arrays. This work is motivated by the objective of predicting forces and motion associated with both deep-water drilling and production risers in the oil industry. This work has other engineering applications including simulation of flow past tubular heat exchangers or submarine-towed sensor arrays and the flow about parachute ribbons. In the present work, a vortex method is used for solving the unsteady flow field. This method demonstrates inherent advantages over more conventional grid-based computational fluid dynamics. The vortex method is non-iterative, does not require artificial viscosity for stability, displays minimal numerical diffusion, can easily treat moving boundaries, and allows a greatly reduced computational domain since vorticity occupies only a small fraction of the fluid volume. A gridless approach is used in the flow sufficiently distant from surfaces. A Lagrangian remap scheme is used near surfaces to calculate diffusion and convection of vorticity. A fast multipole technique is utilized for efficient calculation of velocity from the vorticity field. The ability of the method to correctly predict lift and drag forces on simple stationary geometries over a broad range of Reynolds numbers is presented. |
| numerical recipes source code: Numerical Recipes in Quantum Information Theory and Quantum Computing M. S. Ramkarthik, Payal D. Solanki, 2021 This first of a kind textbook provides computational tools in Fortran 90 that are fundamental to quantum information, quantum computing, linear algebra and one dimensional spin half condensed matter systems. Over 160 subroutines are included, and the numerical recipes are aided by detailed flowcharts. Suitable for beginner and advanced readers alike, students and researchers will find this textbook to be a helpful guide and a compendium-- |
| numerical recipes source code: Numerical Methods for Scientists and Engineers Richard Wesley Hamming, 1962 |
| numerical recipes source code: 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 source code: Problem Book in Relativity and Gravitation Alan P Lightman, William H. Press, Richard H. Price, Saul A. Teukolsky, 2017-09-01 An essential resource for learning about general relativity and much more, from four leading experts Important and useful to every student of relativity, this book is a unique collection of some 475 problems--with solutions--in the fields of special and general relativity, gravitation, relativistic astrophysics, and cosmology. The problems are expressed in broad physical terms to enhance their pertinence to readers with diverse backgrounds. In their solutions, the authors have attempted to convey a mode of approach to these kinds of problems, revealing procedures that can reduce the labor of calculations while avoiding the pitfall of too much or too powerful formalism. Although well suited for individual use, the volume may also be used with one of the modem textbooks in general relativity. |
| numerical recipes source code: MATLAB Machine Learning Recipes Michael Paluszek, Stephanie Thomas, 2019-01-31 Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB. |
| numerical recipes source code: 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 source code: Numerical Recipes Source Code in C and C++ CD ROM with Windows or Macintosh Single-Screen License William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T. Vetterling, 2002-02-18 This CDROM contains all the source code for the routines and examples from Numerical Recipes in C: The Art of Scientific Computing (Second Edition) and Numerical Recipes in C++: The Art of Scientific Computing (Second Edition) as well as the affiliated example books. The C++ routines, in ANSI/ISO C++ source code, can be used with almost any existing C++ vector/matrix class library, according to user preference. A simple class library for stand-alone use is also included. The ISO 9660 standard format CD-ROM can be used by Windows (all versions) and Macintosh compatible computers, using any Web browser to navigate among the program files. Included with the CD-ROM is a license to use all the copyrighted Numerical Recipes code on a single Windows or Macintosh compatible computer. Both scientific programmers new to C++, and experienced C++ programmers who need access to the Numerical Recipes routines, can benefit from this new version of a classic text. |
| numerical recipes source code: Practical Neural Network Recipes in C++ Timothy Masters, 1993 |
| numerical recipes source code: 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 source code: Programming for Computations - MATLAB/Octave Svein Linge, Hans Petter Langtangen, 2016-07-28 This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. |
| numerical recipes source code: Turbo Pascal Numerical Methods Toolbox , 1987 |
| numerical recipes source code: Numerical Recipes Multi-Language Code CD ROM with LINUX Or UNIX Single-Screen License Revised Version William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery, 2002 A single omnibus edition containing all the Numerical Recipes source code in all languages, including the brand-new C++, plus a single screen license for a LINUX or UNIX workstation. |
| numerical recipes source code: Numerical Recipes Routines and Examples in BASIC (First Edition) Julien C. Sprott, 1991-04-26 Modern BASIC programmers will be delighted to learn that the routines and demonstration programs from the highly acclaimed reference book Numerical Recipes: The Art of Scientific Computing are now available in their language of choice. Numerical Recipes, by William H. Press, Brian P. Flannery, Saul A. Teukolsky and William T. Vetterling, is a computing and numerical analysis. It is accompanied by the Numerical Recipes Example Book containing programs that demonstrate the subroutines. Julien C. Sprott has translated all of the recipes and programs, over 350 in all, into BASIC. This book brings the routines and programs together in a single source that includes computer code and code captions from both the book and example book and the commentary from the example book. It is recommended for use with one of the main Numerical Recipes books. The author employs Microsoft QuickBasic 4.5, but the recipes are easily adapted for other modern forms of BASIC. The programs contained in this book are also available as machine-readable code on a 5.1/4 inch floppy diskette for IBM compatible computers. |
| numerical recipes source code: Numerical Methods with MATLAB Gerald W. Recktenwald, 2000 |
| numerical recipes source code: Numerical Recipes in FORTRAN 77 Macintosh Diskette Version 2.0 William H. Press, 1992 A complete text and reference book on scientific computing. It proceeds from mathematical and theoretical considerations to actual practical computer routines. |
| numerical recipes source code: CUDA Application Design and Development Rob Farber, 2011-10-31 The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries.--Pub. desc. |
| numerical recipes source code: Dr. Dobb's Journal , 2002 |
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.
