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Chapter
Explore common algorithmic design paradigms such as divide and conquer, greedy algorithms, and dynamic programming. Learn how these high-level strategies provide frameworks for designing efficient solutions to a wide range of problems.
Introduction: The Algorithmic Landscape and the Need for Paradigms
Divide and Conquer: Recursion and the Power of Breaking Down Problems
Greedy Algorithms: Making Locally Optimal Choices for Global Solutions
Dynamic Programming: Storing Subproblem Solutions to Avoid Redundancy
Backtracking and Branch and Bound: Exploring Solution Spaces Systematically
Brute Force: The Simpler, Though Often Inefficient, Starting Point
Choosing the Right Paradigm: A Framework for Decision-Making
Conclusion: Mastering Paradigms for Algorithmic Mastery