This is the of the programming language-independent text that helped establish computer algorithms as a discipline of computer science. The text incorporates the latest research and state-of-the-art applications, bringing this classic to the forefront of modern computer science education. A major strength of this text is its focus on design techniques rather than on individual algorithms. This book is appropriate as a core text for upper-and graduate-level courses in algorithms. The second edition of Fundamentals of Computer Algorithms emphasizes: Design techniques: Divide and conquer, the greedy method, dynamic programming, backtracking and branch and bound are illustrated with several examples. Each algorithm is completely analyzed. Examples: A wide range of examples provides students with the actual implementation of correct design. The latest research: A thorough treatment of probabilistic and parallel algorithms is included. Full integration of randomized algorithms: Performance with nonrandomized algorithms is thoroughly compared.
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This is the of the programming language-independent text that helped establish computer algorithms as a discipline of computer science. The text incorporates the latest research and state-of-the-art applications, bringing this classic to the forefront of modern computer science education. A major strength of this text is its focus on design techniques rather than on individual algorithms. This book is appropriate as a core text for upper-and graduate-level courses in algorithms. The second edition of Fundamentals of Computer Algorithms emphasizes: Design techniques: Divide and conquer, the greedy method, dynamic programming, backtracking and branch and bound are illustrated with several examples. Each algorithm is completely analyzed. Examples: A wide range of examples provides students with the actual implementation of correct design. The latest research: A thorough treatment of probabilistic and parallel algorithms is included. Full integration of randomized algorithms: Performance with nonrandomized algorithms is thoroughly compared.
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