Algorithm and Design Complexity

Computational complexity is critical in analysis of algorithms and is important to be able to select algorithms for efficiency and solvability. Algorithm and Design Complexity initiates with discussion of algorithm analysis, time space trade-off, symptotic notations and so forth.

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Computational complexity is critical in analysis of algorithms and is important to be able to select algorithms for efficiency and solvability. Algorithm and Design Complexity initiates with discussion of algorithm analysis, time-space trade-off, symptotic notations, and so forth. It further includes algorithms that are definite and effective, known as computational procedures. Further topics explored include divide-and-conquer, dynamic programming, and backtracking.

Features:






Includes complete coverage of basics and design of algorithms



Discusses algorithm analysis techniques like divide-and-conquer, dynamic programming, and greedy heuristics



Provides time and space complexity tutorials



Reviews combinatorial optimization of Knapsack problem



Simplifies recurrence relation for time complexity

This book is aimed at graduate students and researchers in computers science, information technology, and electrical engineering.