Sinopsis
Standard algorithm texts are plagued by irrelevant problem sets, forcing instructors to find or compose additional problems. This supplementary text fills that gap, with 965 practical problems on design, analysis, and verification of algorithms. Suitable for undergraduates and graduate students, it is indispensable to aspiring PhD students preparing for the algorithms portion of the candidacy exam.
The author, a Professor in the College of Engineering at the University of North Texas, begins with an introductory chapter and five chapters of background on mathematical induction, Big-O and Big-Omega notation, recurrence relations, correctness proofs, and basic algorithm analysis. Subsequent chapters are organized according to algorithm design technique: divide and conquer, dynamic programming, greedy algorithms, and exhaustive search. Final chapters cover advanced data structures and NP-completeness. Each problem is ranked by level of difficulty; some hints and solutions appear at chapter ends.
The author, a Professor in the College of Engineering at the University of North Texas, begins with an introductory chapter and five chapters of background on mathematical induction, Big-O and Big-Omega notation, recurrence relations, correctness proofs, and basic algorithm analysis. Subsequent chapters are organized according to algorithm design technique: divide and conquer, dynamic programming, greedy algorithms, and exhaustive search. Final chapters cover advanced data structures and NP-completeness. Each problem is ranked by level of difficulty; some hints and solutions appear at chapter ends.
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Reseña del editor
Standard algorithm texts are plagued by irrelevant problem sets, forcing instructors to find or compose additional problems. This supplementary text fills that gap, with 965 practical problems on design, analysis, and verification of algorithms. Suitable for undergraduates and graduate students, it is indispensable to aspiring PhD students preparing for the algorithms portion of the candidacy exam.
The author, a Professor in the College of Engineering at the University of North Texas, begins with an introductory chapter and five chapters of background on mathematical induction, Big-O and Big-Omega notation, recurrence relations, correctness proofs, and basic algorithm analysis. Subsequent chapters are organized according to algorithm design technique: divide and conquer, dynamic programming, greedy algorithms, and exhaustive search. Final chapters cover advanced data structures and NP-completeness. Each problem is ranked by level of difficulty; some hints and solutions appear at chapter ends.
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