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Efficient Algorithm Design

Efficient Algorithm Design

By : Masoud Makrehchi
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Efficient Algorithm Design

Efficient Algorithm Design

By: Masoud Makrehchi

Overview of this book

Efficient Algorithm Design redefines algorithms, tracing the evolution of computer science as a discipline bridging natural science and mathematics. Author Masoud Makrehchi, PhD, with his extensive experience in delivering publications and presentations, explores the duality of computers as mortal hardware and immortal algorithms. The book guides you through essential aspects of algorithm design and analysis, including proving correctness and the importance of repetition and loops. This groundwork sets the stage for exploring algorithm complexity, with practical exercises in design and analysis using sorting and search as examples. Each chapter delves into critical topics such as recursion and dynamic programming, reinforced with practical examples and exercises that link theory with real-world applications. What sets this book apart is its focus on the practical application of algorithm design and analysis, equipping you to solve real programming challenges effectively. By the end of this book, you’ll have a deep understanding of algorithmic foundations and gain proficiency in designing efficient algorithms, empowering you to develop more robust and optimized software solutions.
Table of Contents (21 chapters)
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Part 1: Foundations of Algorithm Analysis
7
Part 2: Deep Dive in Algorithms
13
Part 3: Fundamental Data Structures
17
Part 4: Next Steps

Dynamic Programming

Dynamic programming is a powerful technique that can dramatically lower the computational costs of many complex algorithms, though it comes with some trade-offs. This chapter introduces dynamic programming and includes a review of greedy algorithms.

We will begin by revisiting the principles of divide-and-conquer strategies to contrast them with dynamic programming. Dynamic programming stands out in algorithm design due to its ability to solve problems that involve overlapping subproblems and optimal substructure. By storing the results of these subproblems, dynamic programming prevents redundant calculations, leading to significant efficiency gains.

Through various examples, we will explore how dynamic programming can be applied to solve classical problems such as the knapsack problem, the longest common subsequence, and the Traveling Salesman Problem. Each example will illustrate the step-by-step approach to breaking down a problem, defining the state space...

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Tech Concepts
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Programming languages
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Efficient Algorithm Design
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