Search results for "Python Programming for Mathematics"
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Fractional Programming
Mathematical programming has know a spectacular diversification in the last few decades. This process has happened both at the level of mathematical research and at the level of the applications generated by the solution methods that were created. To write a monograph dedicated to a certain domain of mathematical programming is, under such circumstances,especially difficult. In the present monograph we opt for the domain of fractional programming. Interest of this subject was generated by the fact that various optimization problems from engineering and economics consider the minimization of a ratio between physical and/or economical functions, for example cost/time, cost/volume,cost/profit, or other quantities that measure the efficiency of a system. For example, the productivity of industrial systems, defined as the ratio between the realized services in a system within a given period of time and the utilized resources, is used as one of the best indicators of the quality of their operation. Such problems, where the objective function appears as a ratio of functions, constitute fractional programming problem. Due to its importance in modeling various decision processes in management science, operational research, and economics, and also due to its frequent appearance in other problems that are not necessarily economical, such as information theory, numerical analysis, stochastic programming, decomposition algorithms for large linear systems, etc., the fractional programming method has received particular attention in the last three decades.
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Discovering Mathematics with Magma
Based on the ontology and semantics of algebra, the computer algebra system Magma enables users to rapidly formulate and perform calculations in abstract parts of mathematics. Edited by the principal designers of the program, this book explores Magma. Coverage ranges from number theory and algebraic geometry, through representation theory and group theory to discrete mathematics and graph theory. Includes case studies describing computations underpinning new theoretical results.
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Python 3 Using DeepSeek
This book is your definitive guide to mastering DeepSeek, a leading open-source Large Language Model (LLM), and leveraging its power through practical Python programming. It is structured to build your expertise from foundational Python data structures and object-oriented programming to advanced model deployment.The initial chapters establish a strong technical base in Python's core features, including comprehensions, iterators, and the OOP paradigm. The focus then shifts entirely to DeepSeek, detailing its innovative Mixture-of-Experts (MoE) architecture, its superior performance on code-centric benchmarks like HumanEval, and its efficiency advantages. You will gain crucial insight into model management, specifically through the concept of quantization and local deployment with tools like Ollama, which is essential for maximizing performance while minimizing resource usage. The final chapter provides numerous, practical Python mini-projects, allowing you to immediately apply DeepSeek's capabilities to a range of real-world tasks from basic web scraping and data visualization to advanced object-oriented programming and linear regression.Python 3 Using DeepSeek is essential reading for Python developers (intermediate to advanced), machine learning practitioners, and data scientists. It is your guide to integrating high-performing, open-source LLMs into your workflows, particularly for code generation, algorithmic reasoning, and highly efficient local model deployment.
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Python For Kids For Dummies
The kid-friendly way to learning coding with Python Calling all wanna-be coders! Experts point to Python as one of the best languages to start with when you're learning coding, and Python For Kids For Dummies makes it easier than ever. Packed with approachable, bite-sized projects that won't make you lose your cool, this fun and friendly guide teaches the basics of coding with Python in a language you can understand. In no time, you'll be installing Python tools, creating guessing games, building a geek speak translator, making a trivia game, constructing a Minecraft chat client, and so much more. Whether you don't have the opportunity to take coding classes at school or in camp—or just simply prefer to learn on your own—Python For Kids For Dummies makes getting acquainted with this popular coding language fast and easy. It walks you step-by-step through basic coding projects and provides lots of hands-on tasks that give you a sweet sense of accomplishment when you complete them. What's not to love about that? Navigate the basics of coding with the Python languageCreate your own applications and gamesFind help from other Python usersExpand your technology skills with Python If you're a pre-to-early-teen looking to add coding skills to your creativity toolbox, Python For Kids For Dummies is your sure-fire weapon for getting up and running with one of the hottest programming languages around.
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Programming-Based Formal Languages and Automata Theory
This textbook introductes formal languages and automata theory for upper-level undergraduate or beginning graduate students. While it contains the traditional mathematical development usually employed in computational theory courses, it is also quite different from many of them. Machines, grammars, and algorithms developed as part of a constructive proof are intended to be rendered as programs. The book is divided into four parts that build on each other. Part I reviews fundamental concepts. It introduces programming in FSM and reviews program design. In addition, it reviews essential mathematical background on sets, relations, and reasoning about infinite sets. Part II starts the study of formal languages and automata theory in earnest with regular languages. It first introduces regular expressions and shows how they are used to write programs that generate words in a regular language. Given that regular expressions generate words, it is only natural to ask how a machine can recognize words in a regular language. This leads to the study of deterministic and nondeterministic finite-state machines. Part III starts the exploration of languages that are not regular with context-free languages. It begins with context-free grammars and pushdown automata to generate and recognize context-free languages, and it ends with a discussion of deterministic pushdown automata and illustrates why these automatons are fundamentally different from nondeterministic pushdown automata. Part IV eventually explores languages that are not context-free, known as context-sensitive languages. It starts by discussing the most powerful automaton known to mankind: the Turing machine. It then moves to grammars for context-sensitive languages, and their equivalence with Turing machines is explored. The book ends with a brief chapter introducing complexity theory and explores the question of determining if a solution to a problem is practical.
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Mathematics of Nonlinear Programming
Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization.
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Progress in Mathematics
This volume contains two review articles: "Stochastic Pro gramming" by Vo V. Kolbin, and "Application of Queueing-Theoretic Methods in Operations Research, " by N. Po Buslenko and A. P. Cherenkovo The first article covers almost all aspects of stochastic programming. Many of the results presented in it have not pre viously been surveyed in the Soviet literature and are of interest to both mathematicians and economists. The second article com prises an exhaustive treatise on the present state of the art of the statistical methods of queueing theory and the statistical modeling of queueing systems as applied to the analysis of complex systems. Contents STOCHASTIC PROGRAMMING V. V. Kolbin Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 § 1. The Geometry of Stochastic Linear Programming Problems. . . . . . . . . . . . . . . . . . . . 5 § 2. Chance-Constrained Problems . . . . . . . . . 8 § 3. Rigorous Statement of stochastic Linear Programming Problems . . . . . . . . . . 16 § 4. Game-Theoretic Statement of Stochastic Linear Programming Problems. . . . . . . . 18 § 5. Nonrigorous Statement of SLP Problems . . . 19 § 6. Existence of Domains of Stability of the Solutions of SLP Problems . . . . . . . . . 29 § 7. Stability of a Solution in the Mean. . . . . . . . . . . . 30 § 8. Dual Stochastic Linear Programming Problems. . . 37 § 9. Some Algorithms for the Solution of Stochastic Linear Programming Problems . . . . . . . . . . 40 § 10. Stochastic Nonlinear Programming: Some First Results . . . . . . . . . . . . . . . . . . . . . . 42 § 11. The Two-Stage SNLP Problem. . . . . . . . . . . . 47 § 12. Optimality and Existence of a Plan in Stochastic Nonlinear Programming Problems. 58 Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Python for Data Science For Dummies
Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples. Get a firm background in the basics of Python coding for data analysisLearn about data science careers you can pursue with Python coding skillsIntegrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databasesPython careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.
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Digital Signal Processing (DSP) with Python Programming
The parameter estimation and hypothesis testing are the basic tools in statistical inference. These techniques occur in many applications of data processing., and methods of Monte Carlo have become an essential tool to assess performance. For pedagogical purposes the book includes several computational problems and exercices. To prevent students from getting stuck on exercises, detailed corrections are provided.
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SymbolicC++:An Introduction to Computer Algebra using Object-Oriented Programming
Symbolic C++: An Introduction to Computer Algebra Using Object-Oriented Programming provides a concise introduction to C++ and object-oriented programming, using a step-by-step construction of a new object-oriented designed computer algebra system - Symbolic C++. It shows how object-oriented programming can be used to implement a symbolic algebra system and how this can then be applied to different areas in mathematics and physics. This second revised edition:- * Explains the new powerful classes that have been added to Symbolic C++. * Includes the Standard Template Library. * Extends the Java section. * Contains useful classes in scientific computation. * Contains extended coverage of Maple, Mathematica, Reduce and MuPAD.
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50 Algorithms Every Programmer Should Know - Second Edition
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not n
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Numerical Methods of Mathematics Implemented in Fortran
This book systematically classifies the mathematical formalisms of computational models that are required for solving problems in mathematics, engineering and various other disciplines. It also provides numerical methods for solving these problems using suitable algorithms and for writing computer codes to find solutions. For discrete models, matrix algebra comes into play, while for continuum framework models, real and complex analysis is more suitable. The book clearly describes the method–algorithm–code approach for learning the techniques of scientific computation and how to arrive at accurate solutions by applying the procedures presented. It not only provides instructors with course material but also serves as a useful reference resource. Providing the detailed mathematical proofs behind the computational methods, this book appeals to undergraduate and graduate mathematics and engineering students.The computer codes have been written in the Fortran programming language, which is the traditional language for scientific computation. Fortran has a vast repository of source codes used in real-world applications and has continuously been upgraded in line with the computing capacity of the hardware. The language is fully backwards compatible with its earlier versions, facilitating integration with older source codes.
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Methods of Applied Mathematics with a Software Overview
Broadly organized around the applications of Fourier analysis, "Methods of Applied Mathematics with a MATLAB Overview" covers both classical applications in partial differential equations and boundary value problems, as well as the concepts and methods associated to the Laplace, Fourier, and discrete transforms. Transform inversion problems are also examined, along with the necessary background in complex variables. A final chapter treats wavelets, short-time Fourier analysis, and geometrically-based transforms. The computer program MATLAB is emphasized throughout, and an introduction to MATLAB is provided in an appendix. Rich in examples, illustrations, and exercises of varying difficulty, this text can be used for a one- or two-semester course and is ideal for students in pure and applied mathematics, physics, and engineering.
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Numerical Analysis
Students completing a year of Calculus can find novel and practical challenges in mathematics using numerical analysis. This text follows the mathematician's process of examining a problem and deciding which algebraic or calculus techniques lead to reliable solutions. Each algorithm includes all developmental steps, illustrating the beauty of reducing complex systems to streamlined formulae. Accessible proofs using techniques beyond those in geometry are fully explained. All algorithms contain detailed examples of success and situational pitfalls. An exhaustive set of practice problems was meticulously developed by hand, not AI-generated, ensuring deviations at each stage of a particular algorithm. Many exercises highlight advantages and disadvantages of algorithms through comparison.
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Python for Information Professionals
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science. Readers of Python for Information Professionals will learn to:Develop Python applications for the retrieval, cleaning, and analysis of large datasets. Design applications to support traditional library functions and create new opportunities to maximize library value. Consider data security and privacy relevant to data analysis when using the Python language.
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Lectures on Applications-Oriented Mathematics
Meets the need for a program of short courses involving the essentials of a number of mathematical topics taken by physics and engineering students. Basically applications-oriented, the courses do include selected topics of abstract mathematics. While several courses can be used as practical appendices to conventional mathematics, others serve as introductions, providing motivation for self-study in areas of conceptual math.
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