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Practical Discrete Mathematics

You're reading from   Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838983147
Length 330 pages
Edition 1st Edition
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Authors (2):
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Ryan T. White Ryan T. White
Author Profile Icon Ryan T. White
Ryan T. White
Archana Tikayat Ray Archana Tikayat Ray
Author Profile Icon Archana Tikayat Ray
Archana Tikayat Ray
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Table of Contents (17) Chapters Close

Preface 1. Part I – Basic Concepts of Discrete Math
2. Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions FREE CHAPTER 3. Chapter 2: Formal Logic and Constructing Mathematical Proofs 4. Chapter 3: Computing with Base-n Numbers 5. Chapter 4: Combinatorics Using SciPy 6. Chapter 5: Elements of Discrete Probability 7. Part II – Implementing Discrete Mathematics in Data and Computer Science
8. Chapter 6: Computational Algorithms in Linear Algebra 9. Chapter 7: Computational Requirements for Algorithms 10. Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks 11. Chapter 9: Searching Data Structures and Finding Shortest Paths 12. Part III – Real-World Applications of Discrete Mathematics
13. Chapter 10: Regression Analysis with NumPy and Scikit-Learn 14. Chapter 11: Web Searches with PageRank 15. Chapter 12: Principal Component Analysis with Scikit-Learn 16. Other Books You May Enjoy

Common classes of computational complexity

In this section, we will learn about some other common classes of computational complexity other than the constant, linear, quadratic, and suchlike complexities that have been discussed in the previous sections.

"Pretty well everybody outside the area of computer science thinks that if your program is running too slowly, what you need is a faster machine."

– Rod Downey and Mike Fellows

However, this is not the case, since some problems might require a brute-force search through a large class of cases that exponentially increases the number of steps required to solve the problem. An important distinction is often made between a tractable and intractable problem:

  • Tractable problems make use of algorithms that take polynomial time (P) for their execution – time complexity is of the order O(nc), where c is any constant that belongs to the natural numbers.

    Feasibly decidable kinds of problems are problems...

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