Lippert E. - Fabulous Adventures in Data Structures and Algorithms [2026, PDF, ENG]

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Fabulous Adventures in Data Structures and Algorithms

Год издания: 2026
Автор: Lippert E.

Издательство: Manning
ISBN: 978-1633435032
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 368

Описание:
Fabulous Adventures in Data Structures and Algorithms invites you to step off the beaten path and explore interesting, unfamiliar, and even exotic algorithms that will challenge your perspective and elevate your code. Legendary language designer Eric Lippert guides you with a refreshing, conversational approach, providing beautifully practical examples that highlight the recurring patterns behind stubborn coding problems.
You’ll begin with a fresh look at foundational, thread-safe, and persistent immutable data structures. Lippert demystifies stacks, queues, and finger-tree deques, illustrating how to manage memory efficiently through persistence. For developers working in highly concurrent, cloud native environments, these patterns will help you eliminate hard-to-spot race conditions and state-mutation bugs.
Your fabulous adventure then turns to structural search, compiler design, and the integration of functional programming within mainstream object-oriented languages. You’ll dive into Directed Acyclic Word Graphs (DAWGs), greedy pretty printers, and the complexities of tree unification. This section bridges theory and real-world tools, teaching you how to design highly modular compilers or robust static analysis engines and to write declarative, composable, and expressive APIs.
In the final leg of your journey, you’ll model randomness, statistical reasoning, and continuous probability—core concepts for AI and predictive analytics. Lippert introduces advanced Bayesian and monadic techniques, teaching you how to construct joint distributions and sample them using the powerful Metropolis algorithm. This statistical toolkit aligns perfectly with modern data-driven systems that need to make consistent, sound decisions under real-world uncertainty.
In the age of AI coding, implementation is becoming cheaper. AI can write code, but it cannot relieve developers of responsibility for understanding what the code means, why a given solution was chosen, how it will scale, and whether the problem has been framed correctly. This book develops exactly that judgment. It teaches the part that is becoming more valuable: recognizing, selecting, questioning, and evaluating the ideas behind implementations. It is an apprenticeship in computational taste, taught through unusual and memorable examples.
A data structure is what it says on the tin: a way to store information in an organized, structured manner. We’ll be creating data structures using C# arrays, classes, structs, records, and interfaces, as we’d expect. An algorithm is a finite sequence of steps that, when executed, solves a problem. An algorithm doesn’t have to be deterministic; many algorithms include “choose a random number”. An algorithm doesn’t have to be executed by a computer, either, and it doesn’t have to be conceptually complicated, even if the number of steps performed is large. “Estimate the average revenue generated if this user viewed each of these ads, then show them the ad with the highest expected revenue” does a lot of math but is easy to understand.
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[spoiler="Оглавление"]
1. Starting a fabulous adventure 1
Part 1 Extending the basics 11
2. Immutable stacks and queues 13
3. An immutable deque 44
4. Memoizing immutable quadtrees to make a better Life 68
5. What’s up with you, Directed Acyclic Word Graph? 94
6. Combinatorial algorithms 119
7. First abstract nonsense interlude: Category theory 156
Part 2 Searching, solving, inferring 167
8. Coloring graphs with backtracking search 169
9. Greedy iterative pretty printing 188
10. Unification and anti-unification 209
11. Second abstract nonsense interlude: Monads 235
Part 3 Probabilities 245
12. A better abstraction for randomness 247
13. Conditional probability with Bayes’ theorem 270
14. Third abstract nonsense interlude: The probability monad 286
15. Sampling continuous distributions 293
16. Markov processes and the Metropolis algorithm 309
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