Table of Contents
Algorithmic Thinking - A Problem-Based Introduction by Daniel Zingaro
Return to Algorithms, Algorithms and DevOps - Algorithms and SRE - Algorithms and CI/CD, Cloud Native Algorithms - Algorithms and Microservices - Serverless and Algorithms, Algorithms and Security - Algorithms and DevSecOps, Functional Algorithms, Algorithms and Concurrency, Algorithms and Data Science - Algorithms and Databases, Algorithms and Machine Learning, Algorithms Bibliography, Algorithms Courses, Algorithms Glossary, Awesome Algorithms, Algorithms GitHub, Algorithms Topics
Book Summary
A hands-on, problem-based introduction to building algorithms and building data structures to solve problems with a computer.
Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems.
Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like:
- The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book
- Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations
- The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies
- The heap data structure to determine the amount of money given away in a promotion
- The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionary
NOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check?
Who Should Read This Book
This Algorithms book is for any programmer who wants to learn how to solve tough problems. You’ll learn many data structures and algorithms, the benefits of algorithms, the types of problems they can help you solve, and how to implement algorithms.
Brief Contents
See Detailed Algorithmic Thinking - A Problem-Based Introduction Table of Contents
- Foreword
- Acknowledgments
- 1: Hash Tables
- 8: Union-Find
- Afterword
- Appendix A: Algorithm Runtime
- Appendix B: Because I Can’t Resist
- Appendix C: Problem Credits
About the Author
Dr. Daniel Zingaro is an assistant teaching professor of computer science and award-winning teacher at the University of Toronto. His main area of research is computer science education research, where he studies how students learn (and sometimes don’t learn) computer science material.
About the Technical Reviewer
Larry Yueli Zhang is an Assistant Professor, Teaching Stream of Computer Science at the University of Toronto Mississauga. His teaching and research interests include algorithms, data structures, operating systems, computer networks, social networks, and computing education. He has been in the program committees of ACM SIGCSE, ACM ITiCSE, and WCCCE. He holds a PhD in Computer Science from the University of Toronto.
Product Details
- Publication date: December 15, 2020
- Paperback: 408 pages
- Time to Complete: ZZZ
Research More
Fair Use Sources
Fair Use Sources
Algorithms: Big O Notation, Iterative method Chase algorithm, Grokking Algorithms, Edsger Dijkstra, Donald Knuth: The Art of Computer Programming (TAOCP), Analysis of algorithms. Algorithms GitHub. (navbar_algorithms)
Algorithms: Algorithms Fundamentals, Algorithms Inventor: Algorithms Language Designer: ZZZ on DATE, YEAR; Algorithms DevOps - Algorithms SRE, Cloud Native Algorithms (Algorithms on Kubernetes - Algorithms on AWS - Algorithms on Azure - Algorithms on GCP), Algorithms Microservices, Algorithms Containerization (Algorithms Docker - Algorithms on Docker Hub), Serverless Algorithms, Algorithms Data Science - Algorithms DataOps - Algorithms and Databases (Algorithms ORM), Algorithms ML - Algorithms DL, Functional Algorithms (1. Algorithms Immutability, 2. Algorithms Purity - Algorithms No Side-Effects, 3. Algorithms First-Class Functions - Algorithms Higher-Order Functions, Algorithms Lambdas - Algorithms Anonymous Functions - Algorithms Closures, Algorithms Lazy Evaluation, 4. Algorithms Recursion), Reactive Algorithms), Algorithms Concurrency - Algorithms Parallel Programming - Async Algorithms, Algorithms Networking, Algorithms Security - Algorithms DevSecOps - Algorithms OAuth, Algorithms Memory Allocation (Algorithms Heap - Algorithms Stack - Algorithms Garbage Collection), Algorithms CI/CD - Algorithms Dependency Management - Algorithms DI - Algorithms IoC - Algorithms Build Pipeline, Algorithms Automation - Algorithms Scripting, Algorithms Package Managers, Algorithms Modules - Algorithms Packages, Algorithms Installation (Algorithms Windows - Chocolatey Algorithms, Algorithms macOS - Homebrew Algorithms, Algorithms on Linux), Algorithms Configuration, Algorithms Observability (Algorithms Monitoring, Algorithms Performance - Algorithms Logging), Algorithms Language Spec - Algorithms RFCs - Algorithms Roadmap, Algorithms Keywords, Algorithms Operators, Algorithms Functions, Algorithms Data Structures - Algorithms Algorithms, Algorithms Syntax, Algorithms OOP (1. Algorithms Encapsulation - 2. Algorithms Inheritance - 3. Algorithms Polymorphism - 4. Algorithms Abstraction), Algorithms Design Patterns - Algorithms Best Practices - Algorithms Style Guide - Clean Algorithms - Algorithms BDD, Algorithms Generics, Algorithms I/O, Algorithms Serialization - Algorithms Deserialization, Algorithms APIs, Algorithms REST - Algorithms JSON - Algorithms GraphQL, Algorithms gRPC, Algorithms Virtualization, Algorithms Development Tools: Algorithms SDK, Algorithms Compiler - Algorithms Transpiler, Algorithms Interpreter - Algorithms REPL, Algorithms IDEs (JetBrains Algorithms, Algorithms Visual Studio Code), Algorithms Linter, Algorithms Community - Algorithmsaceans - Algorithms User, Algorithms Standard Library - Algorithms Libraries - Algorithms Frameworks, Algorithms Testing - Algorithms TDD, Algorithms History, Algorithms Research, Algorithms Topics, Algorithms Uses - List of Algorithms Software - Written in Algorithms - Algorithms Popularity, Algorithms Bibliography - Algorithms Courses, Algorithms Glossary - Algorithms Official Glossary, Algorithms GitHub, Awesome Algorithms. (navbar_Algorithms)
Algorithms: Algorithms Fundamentals, Data Structures, Mathematical Algorithms, Data Algorithms, Algorithms and Data Structures, Algorithms Syntax, Algorithms and OOP - Algorithms and Design Patterns, Algorithms Best Practices, Algorithms and Containerization, Algorithms and IDEs (IntelliSense), Algorithms and Development Tools, Algorithms and Compilers, Algorithms and Data Science - Algorithms and DataOps, Machine Learning Algorithms - Algorithms and MLOps, Deep Learning Algorithms, Functional Algorithms, Algorithms and Concurrency - Algorithms and Parallel Programming, Algorithms Libraries, Algorithms History, Algorithms Bibliography, Algorithms Courses, Algorithms Glossary, Algorithms Topics, Algorithms Research, Algorithms GitHub, Written in Algorithms, Algorithms Popularity, Algorithms Awesome. (navbar_math_algorithms, navbar_data_algorithms - See also navbar_data_structures)
Data Structures: Array, Linked List, Stack, Queue, Binary Tree, Binary Search Tree, Heap, Hash Table, Graph, Trie, Skip List, Red-Black Tree, AVL Tree, B-Tree, B+ Tree, Splay Tree, Fibonacci Heap, Disjoint Set, Adjacency Matrix, Adjacency List, Circular Linked List, Doubly Linked List, Priority Queue, Dynamic Array, Bloom Filter, Segment Tree, Fenwick Tree, Cartesian Tree, Rope, Suffix Array, Suffix Tree, Ternary Search Tree, Radix Tree, Quadtree, Octree, KD Tree, Interval Tree, Sparse Table, Union-Find, Min-Max Heap, Binomial Heap, And-Or Graph, Bit Array, Bitmask, Circular Buffer, Concurrent Data Structures, Content Addressable Memory, Deque, Directed Acyclic Graph (DAG), Edge List, Eulerian Path and Circuit, Expression Tree, Huffman Tree, Immutable Data Structure, Indexable Skip List, Inverted Index, Judy Array, K-ary Tree, Lattice, Linked Hash Map, Linked Hash Set, List, Matrix, Merkle Tree, Multimap, Multiset, Nested Data Structure, Object Pool, Pairing Heap, Persistent Data Structure, Quad-edge, Queue (Double-ended), R-Tree, Radix Sort Tree, Range Tree, Record, Ring Buffer, Scene Graph, Scapegoat Tree, Soft Heap, Sparse Matrix, Spatial Index, Stack (Min/Max), Suffix Automaton, Threaded Binary Tree, Treap, Triple Store, Turing Machine, Unrolled Linked List, Van Emde Boas Tree, Vector, VList, Weak Heap, Weight-balanced Tree, X-fast Trie, Y-fast Trie, Z-order, Zero-suppressed Decision Diagram, Zigzag Tree
Data Structures Fundamentals - Algorithms Fundamentals, Algorithms, Data Types; Primitive Types (Boolean data type, Character (computing), Floating-point arithmetic, Single-precision floating-point format - Double-precision floating-point format, IEEE 754, Category:Floating point types, Fixed-point arithmetic, Integer (computer science), Reference (computer science), Pointer (computer programming), Enumerated type, Date Time);
Composite Types or Non-Primitive Types: Array data structure, String (computer science) (Array of characters), Record (computer science) (also called Struct (C programming language)), Union type (Tagged union, also called Variant type, Variant record, Discriminated union, or Disjoint union);
Abstract Data Types: Container (data structure), List (abstract data type), Tuple, Associative array (also called Map, Multimap, Set (abstract data type), Multiset (abstract data type) (also called Multiset (bag)), Stack (abstract data type), Queue (abstract data type), (e.g. Priority queue), Double-ended queue, Graph (data structure) (e.g. Tree (data structure), Heap (data structure))
Data Structures and Algorithms, Data Structures Syntax, Data Structures and OOP - Data Structures and Design Patterns, Data Structures Best Practices, Data Structures and Containerization, Data Structures and IDEs (IntelliSense), Data Structures and Development Tools, Data Structures and Compilers, Data Structures and Data Science - Data Structures and DataOps, Machine Learning Data Structures - Data Structures and MLOps, Deep Learning Data Structures, Functional Data Structures, Data Structures and Concurrency - Data Structures and Parallel Programming, Data Structure Libraries, Data Structures History, Data Structures Bibliography (Grokking Data Structures), Data Structures Courses, Data Structures Glossary, Data Structures Topics, Data Structures Research, Data Structures GitHub, Written in Data Structures, Data Structures Popularity, Data Structures Awesome. (navbar_data_structures - see also navbar_cpp_containers, navbar_math_algorithms, navbar_data_algorithms, navbar_design_patterns, navbar_software_architecture)
Cloud Monk is Retired (for now). Buddha with you. © 2005 - 2024 Losang Jinpa or Fair Use. Disclaimers
SYI LU SENG E MU CHYWE YE. NAN. WEI LA YE. WEI LA YE. SA WA HE.