Jon Pierre Fortney

Discrete Mathematics for Computer Science (eBook, PDF)

An Example-Based Introduction

• Format: PDF

Produktbeschreibung

This book is intended for a first or second-year discrete mathematics course for computer science majors. It covers many important mathematical topics essential for future computer science majors, such as algorithms, number representations, logic, set theory, Boolean algebra, functions, combinatorics etc.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 272
• Erscheinungstermin: 23. Dezember 2020
• Englisch
• ISBN-13: 9781000296648
• Artikelnr.: 60763815

Autorenporträt

Jon Pierre Fortney graduated from the University of Pennsylvania in 1996 with a B.A. in Mathematics and Actuarial Science and a B.S.E. in Chemical Engineering. Prior to returning to graduate school he worked as both an environmental engineer and as an actuarial analyst. He graduated from Arizona State University in 2008 with a Ph.D. in Mathematics, specializing in Geometric Mechanics. Since 2012 he has worked at Zayed University in Dubai. This is his second mathematics textbook.

Inhaltsangabe

1. Introduction to Algorithms. 1.1. What are Algorithms? 1.2. Control
Structures. 1.3. Tracing an Algorithm. 1.4. Algorithm Examples. 1.5.
Problems. 2. Number Representations. 2.1. Whole Numbers. 2.2. Fractional
Numbers. 2.3. The Relationship Between Binary, Octal, and Hexadecimal
Numbers. 2.4. Converting from Decimal Numbers. 2.5. Problems. 3. Logic.
3.1. Propositions and Connectives. 3.2. Connective Truth Tables. 3.3. Truth
Value of Compound Statements. 3.4. Tautologies and Contradictions. 3.5.
Logical Equivalence and The Laws of Logic. 3.6 Problems. 4. Set Theory.
4.1. Set Notation. 4.2. Set Operations. 4.3. Venn Diagrams. 4.4. The Laws
of Set Theory. 4.5. Binary Relations on Sets. 4.6. Problems. 5. Boolean
Algebra. 5.1. Definition of Boolean Algebra. 5.2. Logic and Set Theory as
Boolean Algebras. 5.3. Digital Circuits. 5.4. Sums-of-Products and
Products-of-Sums. 5.5. Problems. 6. Functions. 6.1. Introduction to
Functions. 6.2. Real-valued Functions. 6.3. Function Composition and
Inverses. 6.4. Problems. 7. Counting and Combinatorics. 7.1. Addition and
Multiplication Principles. 7.2. Counting Algorithm Loops. 7.3. Permutations
and Arrangements. 7.4. Combinations and Subsets. 7.5. Permutation and
Combination Examples. 7.6. Problems. 8. Algorithmic Complexity. 8.1.
Overview of Algorithmic Complexity. 8.2. Time-Complexity Functions. 8.3.
Finding Time-Complexity Functions. 8.4. Big-O Notation. 8.5. Ranking
Algorithms. 8.6. Problems. 9. Graph Theory. 9.1. Basic Definitions. 9.2.
Eulerian and Semi-Eulerian Graphs. 9.3. Matrix representation of Graphs.
9.4. Reachability for Directed Graphs. 9.5. Problems. 10. Trees. 10.1 Basic
Definitions. 10.2. Minimal Spanning Trees of Weighted Graphs. 10.3. Minimal
Distance Paths. 10.4. Problems. Appendix A: Basic Circuit Design. Appendix
B: Answers to Problems.