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| geometric programming python: Geometric Programming: Theory and Application Richard James Duffin, Elmor L. Peterson, Clarence Zener, 1967 |
| geometric programming python: Algebra and Geometry with Python Sergei Kurgalin, Sergei Borzunov, 2021-01-18 This book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science. |
| geometric programming python: Programming for Computations - Python Svein Linge, Hans Petter Langtangen, 2016-07-25 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. |
| geometric programming python: Creative Coding in Python Sheena Vaidyanathan, 2018-12-18 Creative Coding in Python presents over 30 creative projects that teach kids how to code in the easy and intuitive programming language, Python. Creative Coding in Python teaches the fundamentals of computer programming and demonstrates how to code 30+ fun, creative projects using Python, a free, intuitive, open-source programming language that's one of the top five most popular worldwide and one of the most popular Google search terms in the U.S. Computer science educator Sheena Vaidyanathan helps kids understand the fundamental ideas of computer programming and the process of computational thinking using illustrations, flowcharts, and pseudocode, then shows how to apply those essentials to code exciting projects in Python: Chatbots: Discover variables, strings, integers, and more to design conversational programs. Geometric art: Use turtle graphics to create original masterpieces. Interactive fiction: Explore booleans and conditionals to invent create your own adventure games. Dice games: Reuse code to devise games of chance. Arcade games and apps: Understand GUI (graphical user interfaces) and create your own arcade games and apps. What’s next? Look at exciting ways to use your powerful new skills and expand your knowledge of coding in Python. Creative Coding in Python gives kids the tools they need to create their own computer programs. |
| geometric programming python: Introduction to Computation and Programming Using Python, second edition John V. Guttag, 2016-08-08 The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics. |
| geometric programming python: An Invitation to 3-D Vision Yi Ma, Stefano Soatto, Jana Kosecká, S. Shankar Sastry, 2012-11-06 This book is intended to give students at the advanced undergraduate or introduc tory graduate level, and researchers in computer vision, robotics and computer graphics, a self-contained introduction to the geometry of three-dimensional (3- D) vision. This is the study of the reconstruction of 3-D models of objects from a collection of 2-D images. An essential prerequisite for this book is a course in linear algebra at the advanced undergraduate level. Background knowledge in rigid-body motion, estimation and optimization will certainly improve the reader's appreciation of the material but is not critical since the first few chapters and the appendices provide a review and summary of basic notions and results on these topics. Our motivation Research monographs and books on geometric approaches to computer vision have been published recently in two batches: The first was in the mid 1990s with books on the geometry of two views, see e. g. [Faugeras, 1993, Kanatani, 1993b, Maybank, 1993, Weng et aI. , 1993b]. The second was more recent with books fo cusing on the geometry of multiple views, see e. g. [Hartley and Zisserman, 2000] and [Faugeras and Luong, 2001] as well as a more comprehensive book on computer vision [Forsyth and Ponce, 2002]. We felt that the time was ripe for synthesizing the material in a unified framework so as to provide a self-contained exposition of this subject, which can be used both for pedagogical purposes and by practitioners interested in this field. |
| geometric programming python: Geometric Algebra for Computer Science Leo Dorst, Daniel Fontijne, Stephen Mann, 2010-07-26 Until recently, almost all of the interactions between objects in virtual 3D worlds have been based on calculations performed using linear algebra. Linear algebra relies heavily on coordinates, however, which can make many geometric programming tasks very specific and complex-often a lot of effort is required to bring about even modest performance enhancements. Although linear algebra is an efficient way to specify low-level computations, it is not a suitable high-level language for geometric programming. Geometric Algebra for Computer Science presents a compelling alternative to the limitations of linear algebra. Geometric algebra, or GA, is a compact, time-effective, and performance-enhancing way to represent the geometry of 3D objects in computer programs. In this book you will find an introduction to GA that will give you a strong grasp of its relationship to linear algebra and its significance for your work. You will learn how to use GA to represent objects and perform geometric operations on them. And you will begin mastering proven techniques for making GA an integral part of your applications in a way that simplifies your code without slowing it down. * The first book on Geometric Algebra for programmers in computer graphics and entertainment computing* Written by leaders in the field providing essential information on this new technique for 3D graphics* This full colour book includes a website with GAViewer, a program to experiment with GA |
| geometric programming python: Math for Programmers Paul Orland, 2020-11-30 A gentle introduction to some of the most useful mathematical concepts that should be in your developer toolbox. - Christopher Haupt, New Relic Explore important mathematical concepts through hands-on coding. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks |
| geometric programming python: Geometric Computation: Foundations for Design Joy Ko, Kyle Steinfeld, 2018-02-15 Geometric Computation: Foundations for Design describes the mathematical and computational concepts that are central to the practical application of design computation in a manner tailored to the visual designer. Uniquely pairing key topics in code and geometry, this book develops the two key faculties required by designers that seek to integrate computation into their creative practice: an understanding of the structure of code in object-oriented programming, and a proficiency in the fundamental geometric constructs that underlie much of the computational media in visual design. |
| geometric programming python: Programming Python Mark Lutz, 2006-08-23 Already the industry standard for Python users, ProgrammingPython fromO'Reilly just got even better. This third edition has been updated toreflect current best practices andthe abundance of changes introduced by the latest version of thelanguage, Python 2.5. Whether you're a novice or an advancedpractitioner, you'll find thisrefreshed book more than lives up to its reputation. ProgrammingPython, 3rd Edition, teaches you the rightway to code. It explains Python language syntax and programmingtechniques in a clear and concisemanner, with numerous examples that illustrate both correct usage andcommon idioms. By reading thiscomprehensive guide, you'll learn how to apply Python in real-worldproblem domains such as: GUI programming Internet scripting Parallel processing Database management Networked applications Programming Python, Third Edition coverseach of thesetarget domainsgradually, beginning with in-depth discussions of core concepts andthen progressing toward completeprograms. Large examples do appear, but only after you've learnedenough to understand their techniques andcode. Along the way, you'll also learn how to use the Python language inrealistically scaled programs--concepts such as Object-Oriented Programming (OOP) and code reuseare recurring side themes throughout thistext. If you're interested in Python programming, then this O'Reillyclassic needs to be within arm's reach. Thewealth of practical advice, snippets of code, and patterns of programdesign can all be put into use on adaily basis--making your life easier and more productive. Reviews of the second edition: ...about as comprehensive as any book can be. --Dr. Dobb's Journal If the language had manuals, they would undoubtedlybe the texts from O'Reilly...'Learning Python' and 'Programming Python'are definitive treatments. --SD Times |
| geometric programming python: Geoprocessing with Python Christine Garrard, 2016-05-05 Summary Geoprocessing with Python teaches you how to use the Python programming language, along with free and open source tools, to read, write, and process geospatial data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology This book is about the science of reading, analyzing, and presenting geospatial data programmatically, using Python. Thanks to dozens of open source Python libraries and tools, you can take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo. The book shows you how. About the Book Geoprocessing with Python teaches you how to access available datasets to make maps or perform your own analyses using free tools like the GDAL, NumPy, and matplotlib Python modules. Through lots of hands-on examples, you’ll master core practices like handling multiple vector file formats, editing geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. The book also covers how to manipulate, resample, and analyze raster data, such as aerial photographs and digital elevation models. What's Inside Geoprocessing from the ground up Read, write, process, and analyze raster data Visualize data with matplotlib Write custom geoprocessing tools Three additional appendixes available online About the Reader To read this book all you need is a basic knowledge of Python or a similar programming language. About the Author Chris Garrard works as a developer for Utah State University and teaches a graduate course on Python programming for GIS. Table of Contents Introduction Python basics Reading and writing vector data Working with different vector file formats Filtering data with OGR Manipulating geometries with OGR Vector analysis with OGR Using spatial reference systems Reading and writing raster data Working with raster data Map algebra with NumPy and SciPy Map classification Visualizing data Appendixes A - Installation B - References C - OGR - online only D - OSR - online only E - GDAL - online only |
| geometric programming python: Programming Computer Vision with Python Jan Erik Solem, 2012-06-19 If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface |
| geometric programming python: Learning Scientific Programming with Python Christian Hill, 2016-02-04 Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming. |
| geometric programming python: Invent Your Own Computer Games with Python, 4th Edition Al Sweigart, 2016-12-16 Invent Your Own Computer Games with Python will teach you how to make computer games using the popular Python programming language—even if you’ve never programmed before! Begin by building classic games like Hangman, Guess the Number, and Tic-Tac-Toe, and then work your way up to more advanced games, like a text-based treasure hunting game and an animated collision-dodging game with sound effects. Along the way, you’ll learn key programming and math concepts that will help you take your game programming to the next level. Learn how to: –Combine loops, variables, and flow control statements into real working programs –Choose the right data structures for the job, such as lists, dictionaries, and tuples –Add graphics and animation to your games with the pygame module –Handle keyboard and mouse input –Program simple artificial intelligence so you can play against the computer –Use cryptography to convert text messages into secret code –Debug your programs and find common errors As you work through each game, you’ll build a solid foundation in Python and an understanding of computer science fundamentals. What new game will you create with the power of Python? The projects in this book are compatible with Python 3. |
| geometric programming python: Solving PDEs in Python Hans Petter Langtangen, Anders Logg, 2017-03-21 This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier–Stokes equations, and systems of nonlinear advection–diffusion–reaction equations, it guides readers through the essential steps to quickly solving a PDE in FEniCS, such as how to define a finite variational problem, how to set boundary conditions, how to solve linear and nonlinear systems, and how to visualize solutions and structure finite element Python programs. This book is open access under a CC BY license. |
| geometric programming python: Beyond the Basic Stuff with Python Al Sweigart, 2020-12-16 BRIDGE THE GAP BETWEEN NOVICE AND PROFESSIONAL You've completed a basic Python programming tutorial or finished Al Sweigart's bestseller, Automate the Boring Stuff with Python. What's the next step toward becoming a capable, confident software developer? Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program--not just in Python but in any language. You'll learn: Coding style, and how to use Python's Black auto-formatting tool for cleaner code Common sources of bugs, and how to detect them with static analyzers How to structure the files in your code projects with the Cookiecutter template tool Functional programming techniques like lambda and higher-order functions How to profile the speed of your code with Python's built-in timeit and cProfile modules The computer science behind Big-O algorithm analysis How to make your comments and docstrings informative, and how often to write them How to create classes in object-oriented programming, and why they're used to organize code Toward the end of the book you'll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book's best practices. You'll test your skills by implementing the program yourself. Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer, as you learn to write readable code that's easy to debug and perfectly Pythonic Requirements: Covers Python 3.6 and higher |
| geometric programming python: Make: Geometry Joan Horvath, Rich Cameron, 2021-06-28 Geometry, of all the branches of mathematics, is the one that is most easily visualized by making something. However, it is all too easy to reduce it to reams of formulas to memorize and proofs to replicate. This book aims to take geometry back to its practical roots with 3D printed models and puzzles as well as demonstrations with household objects like flashlights and paper towel tubes. This is not a traditional geometry textbook, but rather builds up understanding of geometry concepts while also bringing in elements of concepts normally learned much later. Some of the models are counterintuitive, and figuring out how and why they work will both entertain and give insights. Two final chapters suggesting open-ended projects in astronomy and physics, and art and architecture, allow for deeper understanding and integration of the learning in the rest of the book. |
| geometric programming python: Advanced Computational Applications of Geometric Algebra David William Honorio Araujo Da Silva, Dietmar Hildenbrand, Eckhard Hitzer, 2024-06-03 How Geometric Algebra can naturally serve for constructing solutions for pattern recognition, machine learning, data compression, games, robotics, quantum computing, data encoding, to cite a few. Moreover, there is ample evidence that further research on GA and related areas can significantly expand the number of real-world applications in a wide variety of areas. A mathematical system that is very easy to handle, highly robust and superior performance for engineering applications. Good thematic introduction for engineers and researchers new to the subject. Extensive illustrations and code examples. Thematically well structured with many hands on examples. Learning about GA and how to use it for daily tasks in engineering research and development. |
| geometric programming python: A Concise Introduction to Programming in Python Mark J. Johnson, 2018-04-17 A Concise Introduction to Programming in Python, Second Edition provides a hands-on and accessible introduction to writing software in Python, with no prior programming experience required. The Second Edition was thoroughly reorganized and rewritten based on classroom experience to incorporate: A spiral approach, starting with turtle graphics, and then revisiting concepts in greater depth using numeric, textual, and image data Clear, concise explanations written for beginning students, emphasizing core principles A variety of accessible examples, focusing on key concepts Diagrams to help visualize new concepts New sections on recursion and exception handling, as well as an earlier introduction of lists, based on instructor feedback The text offers sections designed for approximately one class period each, and proceeds gradually from procedural to object-oriented design. Examples, exercises, and projects are included from diverse application domains, including finance, biology, image processing, and textual analysis. It also includes a brief How-To sections that introduce optional topics students may be interested in exploring. The text is written to be read, making it a good fit in flipped classrooms. Designed for either classroom use or self-study, all example programs and solutions to odd-numbered exercises (except for projects) are available at: http://www.central.edu/go/conciseintro/. |
| geometric programming python: Applied Geometric Programming Charles S. Beightler, Don T. Phillips, 1976 Constrained optimization problems: basic concepts; Posynomial geometric programming; Practical aspect of G.P. problem-solving; Signomial geometric programming; Tactics for handling posynomial programs with loose constraints and degreess of difficulty; Extensions of geometric programming to non-standard forms; Reversed constraints and transformations to posynomial programs; Solutions of signomial programs through condensation; The underlying primal structure and its use in computation; Selected applications of geometric programming; |
| geometric programming python: Python in High School Arnaud Bodin, 2020-03-10 Python is the ideal language to learn programming. It is a powerful language that will immerse you in the world of algorithms. This book guides you step by step through original mathematical and computer activities adapted to high school. It is complemented by online resources: all the Python codes and colourful chapters. You have everything you need to succeed!* Hello world! * Turtle (Scratch with Python) * If ... then ... * Functions * Arithmetic - While loop - I * Strings - Analysis of a text * Lists I * Statistics - Data visualization * Files * Arithmetic - While loop - II * Binary I * Lists II * Binary II * Probabilities - Parrondo's paradox * Find and replace * Polish calculator - Stacks * Text viewer -Markdown * L-systems * Dynamic images * Game of life * Ramsey graphs and combinatorics * Bitcoin * Random blocks * |
| geometric programming python: The Power of Geometric Algebra Computing Dietmar Hildenbrand, 2021-09-30 Geometric Algebra is a very powerful mathematical system for an easy and intuitive treatment of geometry, but the community working with it is still very small. The main goal of this book is to close this gap from a computing perspective in presenting the power of Geometric Algebra Computing for engineering applications and quantum computing. The Power of Geometric Algebra Computing is based on GAALOPWeb, a new user-friendly, web-based tool for the generation of optimized code for different programming languages as well as for the visualization of Geometric Algebra algorithms for a wide range of engineering applications. Key Features: Introduces a new web-based optimizer for Geometric Algebra algorithms Supports many programming languages as well as hardware Covers the advantages of high-dimensional algebras Includes geometrically intuitive support of quantum computing This book includes applications from the fields of computer graphics, robotics and quantum computing and will help students, engineers and researchers interested in really computing with Geometric Algebra. |
| geometric programming python: Turtle Geometry Harold Abelson, Andrea Disessa, 1986-07-09 Turtle Geometry presents an innovative program of mathematical discovery that demonstrates how the effective use of personal computers can profoundly change the nature of a student's contact with mathematics. Using this book and a few simple computer programs, students can explore the properties of space by following an imaginary turtle across the screen. The concept of turtle geometry grew out of the Logo Group at MIT. Directed by Seymour Papert, author of Mindstorms, this group has done extensive work with preschool children, high school students and university undergraduates. |
| geometric programming python: An Introduction to Linear Programming and Game Theory Paul R. Thie, Gerard E. Keough, 2011-09-15 Praise for the Second Edition: This is quite a well-done book: very tightly organized, better-than-average exposition, and numerous examples, illustrations, and applications. —Mathematical Reviews of the American Mathematical Society An Introduction to Linear Programming and Game Theory, Third Edition presents a rigorous, yet accessible, introduction to the theoretical concepts and computational techniques of linear programming and game theory. Now with more extensive modeling exercises and detailed integer programming examples, this book uniquely illustrates how mathematics can be used in real-world applications in the social, life, and managerial sciences, providing readers with the opportunity to develop and apply their analytical abilities when solving realistic problems. This Third Edition addresses various new topics and improvements in the field of mathematical programming, and it also presents two software programs, LP Assistant and the Solver add-in for Microsoft Office Excel, for solving linear programming problems. LP Assistant, developed by coauthor Gerard Keough, allows readers to perform the basic steps of the algorithms provided in the book and is freely available via the book's related Web site. The use of the sensitivity analysis report and integer programming algorithm from the Solver add-in for Microsoft Office Excel is introduced so readers can solve the book's linear and integer programming problems. A detailed appendix contains instructions for the use of both applications. Additional features of the Third Edition include: A discussion of sensitivity analysis for the two-variable problem, along with new examples demonstrating integer programming, non-linear programming, and make vs. buy models Revised proofs and a discussion on the relevance and solution of the dual problem A section on developing an example in Data Envelopment Analysis An outline of the proof of John Nash's theorem on the existence of equilibrium strategy pairs for non-cooperative, non-zero-sum games Providing a complete mathematical development of all presented concepts and examples, Introduction to Linear Programming and Game Theory, Third Edition is an ideal text for linear programming and mathematical modeling courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for professionals who use game theory in business, economics, and management science. |
| geometric programming python: Geometric Programming for Communication Systems Mung Chiang, 2005 Recently Geometric Programming has been applied to study a variety of problems in the analysis and design of communication systems from information theory and queuing theory to signal processing and network protocols. Geometric Programming for Communication Systems begins its comprehensive treatment of the subject by providing an in-depth tutorial on the theory, algorithms, and modeling methods of Geometric Programming. It then gives a systematic survey of the applications of Geometric Programming to the study of communication systems. It collects in one place various published results in this area, which are currently scattered in several books and many research papers, as well as to date unpublished results. Geometric Programming for Communication Systems is intended for researchers and students who wish to have a comprehensive starting point for understanding the theory and applications of geometric programming in communication systems. |
| geometric programming python: Real Algebraic Geometry and Optimization Thorsten Theobald, 2024-04-18 This book provides a comprehensive and user-friendly exploration of the tremendous recent developments that reveal the connections between real algebraic geometry and optimization, two subjects that were usually taught separately until the beginning of the 21st century. Real algebraic geometry studies the solutions of polynomial equations and polynomial inequalities over the real numbers. Real algebraic problems arise in many applications, including science and engineering, computer vision, robotics, and game theory. Optimization is concerned with minimizing or maximizing a given objective function over a feasible set. Presenting key ideas from classical and modern concepts in real algebraic geometry, this book develops related convex optimization techniques for polynomial optimization. The connection to optimization invites a computational view on real algebraic geometry and opens doors to applications. Intended as an introduction for students of mathematics or related fields at an advanced undergraduate or graduate level, this book serves as a valuable resource for researchers and practitioners. Each chapter is complemented by a collection of beneficial exercises, notes on references, and further reading. As a prerequisite, only some undergraduate algebra is required. |
| geometric programming python: Mixed Integer Nonlinear Programming Jon Lee, Sven Leyffer, 2011-12-02 Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances. |
| geometric programming python: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| geometric programming python: Combinatorial and Computational Geometry Jacob E. Goodman, Janos Pach, Emo Welzl, 2005-08-08 This 2005 book deals with interest topics in Discrete and Algorithmic aspects of Geometry. |
| geometric programming python: The Power of Geometric Algebra Computing Dietmar Hildenbrand, 2021-09-30 Geometric Algebra is a very powerful mathematical system for an easy and intuitive treatment of geometry, but the community working with it is still very small. The main goal of this book is to close this gap from a computing perspective in presenting the power of Geometric Algebra Computing for engineering applications and quantum computing. The Power of Geometric Algebra Computing is based on GAALOPWeb, a new user-friendly, web-based tool for the generation of optimized code for different programming languages as well as for the visualization of Geometric Algebra algorithms for a wide range of engineering applications. Key Features: Introduces a new web-based optimizer for Geometric Algebra algorithms Supports many programming languages as well as hardware Covers the advantages of high-dimensional algebras Includes geometrically intuitive support of quantum computing This book includes applications from the fields of computer graphics, robotics and quantum computing and will help students, engineers and researchers interested in really computing with Geometric Algebra. |
| geometric programming python: Cases on Inquiry through Instructional Technology in Math and Science Lennex, Lesia, Nettleton, Kimberely Fletcher, 2012-01-31 There exists a wealth of information about inquiry and about science, technology, engineering, and mathematics (STEM), but current research lacks meaningfully written, thoughtful applications of both topics.Cases on Inquiry through Instructional Technology in Math and Science represents the work of many authors toward meaningful discourse of inquiry used in STEM teaching. This book presents insightful information to teachers and teacher education candidates about using inquiry in the real classroom, case studies from which research suggests appropriate uses, and tangible direction for creating their own inquiry based STEM activities. Sections take the reader logically through the meaning of inquiry in STEM teaching, how to use technology in modern classrooms, STEM projects which successfully integrate inquiry methodology, and inquiry problem solving within STEM classrooms with the aim of creating activities and models useful for real-world classrooms. |
| geometric programming python: GIS Algorithms Ningchuan Xiao, 2015-11-09 Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises. Using Python code throughout, Xiao breaks the subject down into three fundamental areas: Geometric Algorithms Spatial Indexing Spatial Analysis and Modelling With its comprehensive coverage of the many algorithms involved, GIS Algorithms is a key new textbook in this complex and critical area of geography. |
| geometric programming python: Advances in Architectural Geometry 2014 Philippe Block, Jan Knippers, Niloy J. Mitra, Wenping Wang, 2014-12-26 This book contains 24 technical papers presented at the fourth edition of the Advances in Architectural Geometry conference, AAG 2014, held in London, England, September 2014. It offers engineers, mathematicians, designers, and contractors insight into the efficient design, analysis, and manufacture of complex shapes, which will help open up new horizons for architecture. The book examines geometric aspects involved in architectural design, ranging from initial conception to final fabrication. It focuses on four key topics: applied geometry, architecture, computational design, and also practice in the form of case studies. In addition, the book also features algorithms, proposed implementation, experimental results, and illustrations. Overall, the book presents both theoretical and practical work linked to new geometrical developments in architecture. It gathers the diverse components of the contemporary architectural tendencies that push the building envelope towards free form in order to respond to multiple current design challenges. With its introduction of novel computational algorithms and tools, this book will prove an ideal resource to both newcomers to the field as well as advanced practitioners. |
| geometric programming python: The Grammar of Graphics Leland Wilkinson, 2006-01-28 Preface to First Edition Before writing the graphics for SYSTAT in the 1980’s, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a package that could draw every statistical graphic I had ever seen. The structure of the program was a collection of procedures named after the basic graph types they p- duced. The graphics code was roughly one and a half megabytes in size. In the early 1990’s, I redesigned the SYSTAT graphics package using - ject-based technology. I intended to produce a more comprehensive and - namic package. I accomplished this by embedding graphical elements in a tree structure. Rendering graphics was done by walking the tree and editing worked by adding and deleting nodes. The code size fell to under a megabyte. In the late 1990’s, I collaborated with Dan Rope at the Bureau of Labor Statistics and Dan Carr at George Mason University to produce a graphics p- duction library called GPL, this time in Java. Our goal was to develop graphics components. This book was nourished by that project. So far, the GPL code size is under half a megabyte. |
| geometric programming python: Python Scripting for ArcGIS Paul A. Zandbergen, 2013 Python Scripting for ArcGIS is a guide to help experienced users of ArcGIS for Desktop get started with Python scripting. This book teaches how to write Python code that works with spatial data to automate geoprocessing tasks in ArcGIS. Readers can thuslearn the skill set needed to create custom tools. Key topics in this book include Python language fundamentals, automating geoprocessing tasks,exploring and manipulating spatial data, working with geometries and rasters, map scripting, debugging and error handling, creating functions and classes, and creating and sharing script tools-- |
| geometric programming python: The Big Book of Small Python Projects Al Sweigart, 2021-06-25 Best-selling author Al Sweigart shows you how to easily build over 80 fun programs with minimal code and maximum creativity. If you’ve mastered basic Python syntax and you’re ready to start writing programs, you’ll find The Big Book of Small Python Projects both enlightening and fun. This collection of 81 Python projects will have you making digital art, games, animations, counting pro- grams, and more right away. Once you see how the code works, you’ll practice re-creating the programs and experiment by adding your own custom touches. These simple, text-based programs are 256 lines of code or less. And whether it’s a vintage screensaver, a snail-racing game, a clickbait headline generator, or animated strands of DNA, each project is designed to be self-contained so you can easily share it online. You’ll create: • Hangman, Blackjack, and other games to play against your friends or the computer • Simulations of a forest fire, a million dice rolls, and a Japanese abacus • Animations like a virtual fish tank, a rotating cube, and a bouncing DVD logo screensaver • A first-person 3D maze game • Encryption programs that use ciphers like ROT13 and Vigenère to conceal text If you’re tired of standard step-by-step tutorials, you’ll love the learn-by-doing approach of The Big Book of Small Python Projects. It’s proof that good things come in small programs! |
| geometric programming python: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
| geometric programming python: Computer Graphics from Scratch Gabriel Gambetta, 2021-05-18 Computer Graphics from Scratch demystifies the algorithms used in modern graphics software and guides beginners through building photorealistic 3D renders. Computer graphics programming books are often math-heavy and intimidating for newcomers. Not this one. Computer Graphics from Scratch takes a simpler approach by keeping the math to a minimum and focusing on only one aspect of computer graphics, 3D rendering. You’ll build two complete, fully functional renderers: a raytracer, which simulates rays of light as they bounce off objects, and a rasterizer, which converts 3D models into 2D pixels. As you progress you’ll learn how to create realistic reflections and shadows, and how to render a scene from any point of view. Pseudocode examples throughout make it easy to write your renderers in any language, and links to live JavaScript demos of each algorithm invite you to explore further on your own. Learn how to: Use perspective projection to draw 3D objects on a 2D plane Simulate the way rays of light interact with surfaces Add mirror-like reflections and cast shadows to objects Render a scene from any camera position using clipping planes Use flat, Gouraud, and Phong shading to mimic real surface lighting Paint texture details onto basic shapes to create realistic-looking objects Whether you’re an aspiring graphics engineer or a novice programmer curious about how graphics algorithms work, Gabriel Gambetta’s simple, clear explanations will quickly put computer graphics concepts and rendering techniques within your reach. All you need is basic coding knowledge and high school math. Computer Graphics from Scratch will cover the rest. |
| geometric programming python: Introduction to Computational Models with Python JOSE M. GARRIDO, 2020-06-30 Emphasizing analytical skill development and problem solving, this book shows how to implement computational models using the flexible and easy-to-use Python programming language. It provides the foundation for more advanced work in scientific computing. The book uses the Python programming language interpreter and several packages from the huge |
| geometric programming python: Geometric Tools for Computer Graphics Philip Schneider, David H. Eberly, 2002-10-10 Do you spend too much time creating the building blocks of your graphics applications or finding and correcting errors? Geometric Tools for Computer Graphics is an extensive, conveniently organized collection of proven solutions to fundamental problems that you'd rather not solve over and over again, including building primitives, distance calculation, approximation, containment, decomposition, intersection determination, separation, and more. If you have a mathematics degree, this book will save you time and trouble. If you don't, it will help you achieve things you may feel are out of your reach. Inside, each problem is clearly stated and diagrammed, and the fully detailed solutions are presented in easy-to-understand pseudocode. You also get the mathematics and geometry background needed to make optimal use of the solutions, as well as an abundance of reference material contained in a series of appendices. Features - Filled with robust, thoroughly tested solutions that will save you time and help you avoid costly errors. - Covers problems relevant for both 2D and 3D graphics programming. - Presents each problem and solution in stand-alone form allowing you the option of reading only those entries that matter to you. - Provides the math and geometry background you need to understand the solutions and put them to work. - Clearly diagrams each problem and presents solutions in easy-to-understand pseudocode. - Resources associated with the book are available at the companion Web site www.mkp.com/gtcg.* Filled with robust, thoroughly tested solutions that will save you time and help you avoid costly errors.* Covers problems relevant for both 2D and 3D graphics programming.* Presents each problem and solution in stand-alone form allowing you the option of reading only those entries that matter to you.* Provides the math and geometry background you need to understand the solutions and put them to work.* Clearly diagrams each problem and presents solutions in easy-to-understand pseudocode.* Resources associated with the book are available at the companion Web site www.mkp.com/gtcg. |
statistics - What are differences between Geometric, Logarithmic …
Aug 3, 2020 · Now lets do it using the geometric method that is repeated multiplication, in this case we start with x goes from 0 to 5 and our sequence goes like this: 1, 2, 2•2=4, 2•2•2=8, …
why geometric multiplicity is bounded by algebraic multiplicity?
The geometric multiplicity the be the dimension of the eigenspace associated with the eigenvalue $\lambda_i$. For example: $\begin{bmatrix}1&1\\0&1\end{bmatrix}$ has root $1$ with algebraic …
Proof of geometric series formula - Mathematics Stack Exchange
Sep 20, 2021 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for …
Calculate expectation of a geometric random variable
A clever solution to find the expected value of a geometric r.v. is those employed in this video lecture of the MITx course "Introduction to Probability: Part 1 - The Fundamentals" (by the way, …
terminology - Is it more accurate to use the term Geometric …
In both geometric and exponential growth we find multiplication by a fixed factor. The distinction lies in that 'exponential growth' is typically used to describe continuous time growth (steps of …
Solving for the CDF of the Geometric Probability Distribution
Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their …
How do you calculate the geometric multiplicities?
Dec 11, 2014 · Suppose we have a matrix like $\begin{pmatrix}5&0\\0&5 \end{pmatrix}$ and $\begin{pmatrix}5&1\\0&5 \end{pmatrix}$. Is there any simple way to find the geometric …
What does the dot product of two vectors represent?
May 23, 2014 · It might help to think of multiplication of real numbers in a more geometric fashion. $2$ times $3$ is the length of the interval you get starting with an interval of length $3$ and …
When is a Power Series a Geometric Series?
$\begingroup$ So surely you see the answer now, but I'll state it for the record: a power series is a geometric series if its coefficients are constant (i.e. all the same). In particular, not all power …
Sum of a power series $n x^n$ - Mathematics Stack Exchange
Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their …
statistics - What are differences between Geometric, Logarithmi…
Aug 3, 2020 · Now lets do it using the geometric method that is repeated multiplication, in this case we start with …
why geometric multiplicity is bounded by algebraic multiplici…
The geometric multiplicity the be the dimension of the eigenspace associated with the eigenvalue $\lambda_i$. For …
Proof of geometric series formula - Mathematics Stack Exchange
Sep 20, 2021 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack …
Calculate expectation of a geometric random variable
A clever solution to find the expected value of a geometric r.v. is those employed in this video lecture of the MITx course …
terminology - Is it more accurate to use the term Geometric Gro…
In both geometric and exponential growth we find multiplication by a fixed factor. The distinction lies in that 'exponential …
