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This book introduces the essential concepts of algorithm analysis
required by core undergraduate and graduate computer science
courses, in addition to providing a review of the fundamental
mathematical notions necessary to understand these concepts.
Features: includes numerous fully-worked examples and step-by-step
proofs, assuming no strong mathematical background; describes the
foundation of the analysis of algorithms theory in terms of the
big-Oh, Omega, and Theta notations; examines recurrence relations;
discusses the concepts of basic operation, traditional loop
counting, and best case and worst case complexities; reviews
various algorithms of a probabilistic nature, and uses elements of
probability theory to compute the average complexity of algorithms
such as Quicksort; introduces a variety of classical finite graph
algorithms, together with an analysis of their complexity; provides
an appendix on probability theory, reviewing the major definitions
and theorems used in the book.
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