Tag: Algorithms

Fast Fourier Transform and Convolution Algorithms


Free Download Fast Fourier Transform and Convolution Algorithms by Henri J. Nussbaumer
English | PDF | 1982 | 286 Pages | ISBN : 354011825X | 20.3 MB
In the first edition of this book, we covered in Chapter 6 and 7 the applications to multidimensional convolutions and DFT’s of the transforms which we have introduced, back in 1977, and called polynomial transforms. Since the publication of the first edition of this book, several important new developments concerning the polynomial transforms have taken place, and we have included, in this edition, a discussion of the relationship between DFT and convolution polynomial transform algorithms. This material is covered in Appendix A, along with a presentation of new convolution polynomial transform algorithms and with the application of polynomial transforms to the computation of multidimensional cosine transforms. We have found that the short convolution and polynomial product algorithms of Chap. 3 have been used extensively. This prompted us to include, in this edition, several new one-dimensional and two-dimensional polynomial product algorithms which are listed in Appendix B. Since our book is being used as part of several graduate-level courses taught at various universities, we have added, to this edition, a set of problems which cover Chaps. 2 to 8. Some of these problems serve also to illustrate some research work on DFT and convolution algorithms. I am indebted to Mrs A. Schlageter who prepared the manuscript of this second edition. Lausanne HENRI J. NUSSBAUMER April 1982 Preface to the First Edition This book presents in a unified way the various fast algorithms that are used for the implementation of digital filters and the evaluation of discrete Fourier transforms.

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Advances in Metaheuristic Algorithms for Optimal Design of Structures, Third Edition (2024)


Free Download Advances in Metaheuristic Algorithms for Optimal Design of Structures, Third Edition by Ali Kaveh
English | EPUB | 2021 | 890 Pages | ISBN : 3030593916 | 163.5 MB
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his graduate students, consisting of Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Democratic Particle Swarm Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization.

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The Art of Randomness Randomized Algorithms in the Real World


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English | 2024 | ISBN: 1718503245 | 400 Pages | PDF EPUB | 35 MB
The Art of Randomness is a hands-on guide to mastering the many ways you can use randomized algorithms to solve real programming and scientific problems. You’ll learn how to use randomness to run simulations, hide information, design experiments, and even create art and music. All you need is some Python, basic high school math, and a roll of the dice.

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Python-Based Data Structures and Algorithms


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English | 2024 | ISBN: 9798223165385 | 1071 Pages | EPUB (True) | 9 MB
"Python-Based Data Structures and Algorithms" is a comprehensive guide to mastering the fundamental concepts of algorithms and data structures using the Python programming language. Covering a wide spectrum, from algorithm analysis and problem complexity to computational complexity, this book equips readers with the essential skills for effective programming. Whether you’re a novice or an experienced developer, this resource provides practical insights into implementing algorithms and data structures in Python. With a focus on clarity and efficiency, it’s an invaluable reference for anyone looking to enhance their programming proficiency and tackle real-world challenges in algorithmic problem-solving.

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Optimization Algorithms (MEAP V10)


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English | 2023 | ISBN: 9781633438835 | 1011 Pages | PDF EPUB | 104 MB
Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don’t worry-there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.

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Graph Algorithms for Data Science (MEAP V09)


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English | 2023 | ISBN: 9781617299469 | 432 Pages | PDF EPUB | 22 MB
Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

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Algorithms for Measurement Invariance Testing


Free Download Algorithms for Measurement Invariance Testing: Contrasts and Connections
English | 2023 | ISBN: 100945417X | 94 Pages | PDF (True) | 4 MB
Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.

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