Generic data structures and algorithms in Go : an applied approach using concurrency, genericity and heuristics / Richard Wiener.
2022
QA76.9.D35
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Details
Title
Generic data structures and algorithms in Go : an applied approach using concurrency, genericity and heuristics / Richard Wiener.
Author
Wiener, Richard, 1941-
ISBN
9781484281918 (electronic bk.)
1484281918 (electronic bk.)
148428190X
9781484281901
1484281918 (electronic bk.)
148428190X
9781484281901
Publication Details
Berkeley, CA : Apress L.P., 2022.
Language
English
Description
1 online resource (590 pages)
Item Number
10.1007/978-1-4842-8191-8 doi
Call Number
QA76.9.D35
Dewey Decimal Classification
005.13/3
Summary
Advance your understanding of generic data structures and algorithms and their applications using Go and the effective use of concurrency. You are invited on a journey that aims to improve your programming and problem-solving skills. This book takes you to the next step by showing how to get your programs to work efficiently as well as correctly. As you explore many data structures and the algorithms and applications associated with them, you'll focus on the trade-offs between speed and storage and the benefits of deploying concurrency when appropriate. This book will demonstrate the huge increases in application performance that are possible. The presentation of classic data structures and techniques of algorithm design (greedy, divide and conquer, branch-and-bound to name a few) provides an essential foundation and toolkit for problem solving. But this book goes further by presenting heuristic algorithms and their implementations for solving computationally intractable combinatoric optimization problems such as the travelling salesperson problem. Simulated annealing and genetic algorithms are among the techniques used. The consistent style of coding used throughout this book exploits Go's ability to implement abstract, generic and constrained generic data types without the use of classes. Although some familiarity with Go is assumed, this book should advance your ability to use Go to tackle server-side applications, games, machine learning, information retrieval and other application domains where speed and storage efficiency is essential. What You'll Learn Explore classical data structures and algorithms aimed at making your applications run faster or require less storage Use the new generic features of Go to build reusable data structures Utilize concurrency for maximizing application performance See the power of heuristic algorithms for computationally intractable problems Enhance and improve your Go programming skills Who This Book Is For Practicing Go software developers and students who wish to advance their programming and problem-solving skills and experience the excitement and see the benefits of using generic data structures and algorithms that utilize concurrency whenever possible.
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Generic Data Structures and Algorithms in Go.
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Table of Contents
1. A Tour Of Generics and Concurrency In Go
2. Algorithm Efficiency Sorting and Searching
3. Abstract Data Types: OOP Without Classes in Go
4. ADT In Action: Game Of Life
5. Stacks
6. Queues and Lists
7. Hash Tables
8. Binary Trees
9. Binary Search Tree
10. AVL Trees
11. Heap Trees
12. Red Black Trees
13. Expression Trees
14. Ecological Simulation With Concurrency
15. Dynamic Programming
16. Graph Structures
17. Travelling Salesperson Problem
18. Branch and Bound Solution to TSP
19. Simulated Annealing Heuristic Solution to TSP
20. Genetic Algorithm for TSP
21. Neural Networks and Machine Learning.
2. Algorithm Efficiency Sorting and Searching
3. Abstract Data Types: OOP Without Classes in Go
4. ADT In Action: Game Of Life
5. Stacks
6. Queues and Lists
7. Hash Tables
8. Binary Trees
9. Binary Search Tree
10. AVL Trees
11. Heap Trees
12. Red Black Trees
13. Expression Trees
14. Ecological Simulation With Concurrency
15. Dynamic Programming
16. Graph Structures
17. Travelling Salesperson Problem
18. Branch and Bound Solution to TSP
19. Simulated Annealing Heuristic Solution to TSP
20. Genetic Algorithm for TSP
21. Neural Networks and Machine Learning.