Lippert E. Fabulous Adventures in Data Structures and Algorithms (MEAP v3) 2026

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Lippert E. Fabulous Adventures in Data Structures and Algorithms (MEAP v3) 2026

Textbook in PDF format Author Eric Lippert introduces fabulous solutions using uncommon algorithms and data structures. There’s a lot more to algorithms than the useful-but-boring recipes you recite for every interview. This unique book introduces a collection of amazing algorithms that have the potential to change the way you program. Fabulous Adventures in Data Structures and Algorithms shows you how to Build efficient, double-ended queues from immutable parts Auto-format structured text so it always looks sharp Generate and manipulation random numbers from custom distributions using the same tools you use for sequences Harness immutability to squeeze data smaller while speeding up repeated computations Uncover the mathematical structures underlying lists, nullables, functions, tasks and probabilities In Fabulous Adventures in Data Structures and Algorithms, language designer and C# legend Eric Lippert explores exciting, esoteric, and exotic data structures and algorithms that will expand your programming toolbox, and your horizons. You’ll upend the way you think about lists, learn the algorithms behind powerful developer tools, and rethink how to handle stochastic quantities in modern programming languages. The first third of the book looks at a few of the standard abstract data types such as stacks, queues and trees, but puts a twist on each. We’ll look at the benefits of immutable data structures, and how to use a finger tree to build lists that can be pushed and popped from both ends. And we’ll explore the sometimes astonishing time and space optimizations afforded by memoization. The middle third looks at some of the algorithms I encountered while building developer tools. We’ll explore how source code formatters use search algorithms to find the best layout, how to find the commonalities between two code changes, and what the relationship is between coloring a map and solving a sudoku puzzle. The final third explores algorithms and data structures for a more principled abstraction for representing random quantities; statistical methods have become important in much of modern programming. Many languages have data types for diverse concepts such as sequences, observables, functions, nullables and tasks, but not for randomness. Moreover, all of these concepts share a common underlying mathematical structure; in between sections we’ll have a series of short interludes that explain how category theory underlies these types. To get the most out of this book you’ll want to have some experience with programming, either professionally or recreationally, and a desire to add new tools to your toolbox. Familiarity with the “big-O” notation used to describe asymptotic performance will be useful, but not required. All the code is written in C#, the language I know best, but will be understandable to any programmer who has a working knowledge of a modern object-oriented language such as Java, C++, and so on. Next, you’ll investigate algorithms drawn from building developer tools, from backtracking search in graph coloring, to unification and anti-unification. Finally, you’ll venture into randomness, developing data structures and algorithms for discrete and continuous distributions, sequence operations on probability distributions, and Bayesian reasoning. Preface Starting a fabulous adventure PART 1: EXTENDING THE BASICS Immutable stacks and queues An immutable deque Memoizing immutable quadtrees to make a better Life What’s up with you, Directed Acyclic Word Graph? Combinatorial algorithms PART 2: SEARCHING, SOLVING, INFERRING Basic category theory for programmers Coloring graphs with backtracking search What is monad? Pretty-printing Unification of binary trees Anti-unification of binary trees PART 3: PROBABILITIES A better abstraction for randomness Basic discrete distributions The weighted discrete distrib Applying Bayes’ Theorem to discrete distributions You guessed it, “probability distribution” is a zero monad Markov processes Continuous distributions