The Little Book of Julia Algorithms PDF Download
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Author: Ahan Sengupta
Publisher:
ISBN: 9781637540008
Category :
Languages : en
Pages :
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Book Description
Targeted at middle and high school programmers, this book aims to explain basic computer science concepts while teaching the Julia programming language. As a fast and productive high level language, Julia is ideal for beginner programmers. The learning curve for programming can be quite steep and this book aims to ease this transition by encouraging practise and gradually introducing more complex concepts. The book contains 50 programming challenges that encourages the reader to write their own programs. The solutions to all challenges are given at the end of the book. This book will make readers comfortable with using computers to solve any problems, and leave them well prepared for more significant programming in their maths, science or computer science courses at college. After finishing the exercises in this book, the reader should feel more familiar with: Loops and conditionals, Structuring code with functions, Reading and writing files, Installing and using packages, Sorting and searching, and Simple Statistics and Plotting. With a foreword by Jeff Bezanson, co-creator of the Julia programming language.
Author: Ahan Sengupta
Publisher:
ISBN: 9781637540008
Category :
Languages : en
Pages :
Get Book Here
Book Description
Targeted at middle and high school programmers, this book aims to explain basic computer science concepts while teaching the Julia programming language. As a fast and productive high level language, Julia is ideal for beginner programmers. The learning curve for programming can be quite steep and this book aims to ease this transition by encouraging practise and gradually introducing more complex concepts. The book contains 50 programming challenges that encourages the reader to write their own programs. The solutions to all challenges are given at the end of the book. This book will make readers comfortable with using computers to solve any problems, and leave them well prepared for more significant programming in their maths, science or computer science courses at college. After finishing the exercises in this book, the reader should feel more familiar with: Loops and conditionals, Structuring code with functions, Reading and writing files, Installing and using packages, Sorting and searching, and Simple Statistics and Plotting. With a foreword by Jeff Bezanson, co-creator of the Julia programming language.
Author: Ben Lauwens
Publisher: "O'Reilly Media, Inc."
ISBN: 1492044989
Category : Computers
Languages : en
Pages : 301
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Book Description
If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch. Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also web programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand types, methods, and multiple dispatch Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design and data structures through case studies
Author: Kenneth Lange
Publisher: SIAM
ISBN: 1611976170
Category : Mathematics
Languages : en
Pages : 227
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Book Description
Algorithms are a dominant force in modern culture, and every indication is that they will become more pervasive, not less. The best algorithms are undergirded by beautiful mathematics. This text cuts across discipline boundaries to highlight some of the most famous and successful algorithms. Readers are exposed to the principles behind these examples and guided in assembling complex algorithms from simpler building blocks. Written in clear, instructive language within the constraints of mathematical rigor, Algorithms from THE BOOK includes a large number of classroom-tested exercises at the end of each chapter. The appendices cover background material often omitted from undergraduate courses. Most of the algorithm descriptions are accompanied by Julia code, an ideal language for scientific computing. This code is immediately available for experimentation. Algorithms from THE BOOK is aimed at first-year graduate and advanced undergraduate students. It will also serve as a convenient reference for professionals throughout the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences.
Author: Eric Darve
Publisher: SIAM
ISBN: 1611976553
Category : Mathematics
Languages : en
Pages : 420
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Book Description
Numerical Linear Algebra with Julia provides in-depth coverage of fundamental topics in numerical linear algebra, including how to solve dense and sparse linear systems, compute QR factorizations, compute the eigendecomposition of a matrix, and solve linear systems using iterative methods such as conjugate gradient. Julia code is provided to illustrate concepts and allow readers to explore methods on their own. Written in a friendly and approachable style, the book contains detailed descriptions of algorithms along with illustrations and graphics that emphasize core concepts and demonstrate the algorithms. Numerical Linear Algebra with Julia is a textbook for advanced undergraduate and graduate students in most STEM fields and is appropriate for courses in numerical linear algebra. It may also serve as a reference for researchers in various fields who depend on numerical solvers in linear algebra.
Author: Changhyun Kwon
Publisher: Changhyun Kwon
ISBN: 1798205475
Category : Technology & Engineering
Languages : en
Pages : 262
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Book Description
Last Updated: December 2020 Based on Julia v1.3+ and JuMP v0.21+ The main motivation of writing this book was to help the author himself. He is a professor in the field of operations research, and his daily activities involve building models of mathematical optimization, developing algorithms for solving the problems, implementing those algorithms using computer programming languages, experimenting with data, etc. Three languages are involved: human language, mathematical language, and computer language. His team of students need to go over three different languages, which requires "translation" among the three languages. As this book was written to teach his research group how to translate, this book will also be useful for anyone who needs to learn how to translate in a similar situation. The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science. For more information, visit: http://www.chkwon.net/julia
Author: William Lau
Publisher: Little Book of Algorithms
ISBN: 9781916116344
Category : Education
Languages : en
Pages : 126
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Book Description
This workbook is designed to help those learning and teaching Computer Science at secondary school level. The aim of the book is to help students build fluency in their Python programming. The book would suit students who have already been introduced to the three basic programming constructs of structured programming, namely sequence, selection and iteration. The learning curve for programming can be quite steep and this book aims to ease this transition by encouraging practise and gradually introducing more complex concepts such as lists and 2D lists and file writing. Originally, the book was written for 14-16 year old students studying for their GCSE Computer Science programming exam. However, a wide range of students and teachers will find this book useful.The Little Book of Algorithms concisely presents eighteen problems which computer science students will commonly encounter. These problems are solved efficiently using programs written using Python. However, reading these programs is not enough, so this new version of the book now comes with 48 challenges so that you can apply what you have learnt in various ways: Writing your own programsSolving Parson's puzzlesCompleting quizzes Tracing Gap fillsThis range of exercises will help you to become more fluent in Python and ensure that you are comfortable with any question format in a programming exam. Solutions are provided in the back and a series of video tutorials is also provided so that you can code along with the author, hearing his thought processes as he programs.After finishing this book, you should feel more familiar with: While loops and For loopsConcatenating different data typesUsing procedures and functionsWorking with 1D and 2D lists and arraysFile reading and writing This book will show you how to write better Python programs and will expose you to the key skills that are required to do well in any secondary school programming assignment or exa
Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262047012
Category : Computers
Languages : en
Pages : 701
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Book Description
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262039427
Category : Computers
Languages : en
Pages : 521
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Book Description
A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Author: Justin Solomon
Publisher: CRC Press
ISBN: 1482251892
Category : Computers
Languages : en
Pages : 400
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Book Description
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
Author: Mark Braverman
Publisher: Springer Science & Business Media
ISBN: 3540685472
Category : Computers
Languages : en
Pages : 158
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Book Description
Among all computer-generated mathematical images, Julia sets of rational maps occupy one of the most prominent positions. Their beauty and complexity can be fascinating. They also hold a deep mathematical content. Computational hardness of Julia sets is the main subject of this book. By definition, a computable set in the plane can be visualized on a computer screen with an arbitrarily high magnification. There are countless programs to draw Julia sets. Yet, as the authors have discovered, it is possible to constructively produce examples of quadratic polynomials, whose Julia sets are not computable. This result is striking - it says that while a dynamical system can be described numerically with an arbitrary precision, the picture of the dynamics cannot be visualized. The book summarizes the present knowledge (most of it from the authors' own work) about the computational properties of Julia sets in a self-contained way. It is accessible to experts and students with interest in theoretical computer science or dynamical systems.
