The Dynamic Programming Manual
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The Dynamic Programming Manual

Mastering Efficient Solutions

About the Book

About the Book

Welcome to "The Dynamic Programming Manual: Mastering Efficient Solutions." This book is your comprehensive guide to mastering dynamic programming techniques through a collection of engaging exercises and their efficient solutions, accompanied by detailed explanations of how to approach similar problems.

Dynamic programming is a powerful tool not only for acing developer job interviews but also for optimizing the performance of your applications. Moreover, the problem-solving skills you acquire while tackling dynamic programming challenges will prove invaluable in solving a wide range of related problems.

Throughout the book, I will provide code examples in both Java and Python, offering my solutions to the problems at hand. However, I encourage you to explore your own approaches and compare them with mine, fostering a deeper understanding of dynamic programming concepts. By the end, you will be equipped with the knowledge and skills necessary to tackle diverse challenges with confidence.

Source Code

To enhance your learning experience, the source code for the solutions will be made available once the book is complete. This ensures that the code is organized meticulously, with each problem having its dedicated folder bearing the chapter name. This approach allows for easier navigation and avoids any inconvenience caused by potential reordering or exclusion of chapters.

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About the Author

Gábor László Hajba
Gábor László Hajba

Gábor László Hajba, a seasoned Senior Software Developer at ProLion GmbH in Wiener Neustadt, Austria, brings a wealth of expertise in Java and Python to his role. His primary responsibility involves crafting innovative solutions while also imparting valuable knowledge to his colleagues, ensuring they excel in their respective roles.

A testament to his proficiency, Gábor's book "Website Scraping with Python - Using BeautifulSoup and Scrapy" was published by Apress in 2018. This publication had its humble beginnings as a LeanPub book in 2014, reflecting his passion for sharing insights and empowering fellow developers.

Beyond his professional endeavors, Gábor finds immense joy in his personal life. He cherishes his role as a devoted husband and a proud father to a delightful little girl and an exuberant young boy. Additionally, he nurtures a keen interest in music and aspires to become a proficient bass player, demonstrating his relentless pursuit of growth and fulfillment.

Gábor's multifaceted journey showcases his dedication to both his craft and his loved ones, embodying the spirit of a well-rounded individual who excels in various domains.

Table of Contents

    • Preface: Unleash the Power of Dynamic Programming
    Part I:Theory
    • What is Dynamic Programming?
    • Fundamental Concepts
    • Advanced Dynamic Programming Techniques
    • Dynamic Programming in Practice
    • Advanced Topics in Dynamic Programming
    • Tips and Tricks for Efficient Dynamic Programming
    • Conclusion and Future Directions
    Part II:Dive into Dynamic Programming Challenges
    • Fibonacci Sequence
    • Fibonacci with Memoization
    • Last Digit of Fibonacci Number
    • Longest Subsequence with Equal Elements
    • Longest Subarray with Equal 0s and 1s
    • Longest Common Subsequence
    • Minimum Subset Sum Difference
    • Valid Parentheses
    • Minimum Number of Parentheses
    • Find the Element that Appears Once
    • Find the Missing Number
    • Count Set Bits (Brian Kernighan’s Algorithm)
    • Generate All Subsets
    • Power of Two
    • Scalar Multiplication Order
    • Topological Sorting
    • Breadth-First Search (BFS)
    • Depth-First Search (DFS)
    • Symmetric Tree
    • Lowest Common Ancestor
    • Trim a Binary Search Tree
    • Find Closest Value in BST
    • Binary Tree Level Order Traversal
    • Climbing Stairs
    • Best Time to Buy and Sell Stock
    • House Robber
    • Coin Change
    • Coin Change 2
    • Knapsack Problem (Bottom-Up)
    • 0/1 Knapsack
    • Minimum Spanning Tree (Prim’s Algorithm)
    • Minimum Spanning Tree (Kruskal’s Algorithm)
    • A* Algorithm
    • Palindrome Partitioning
    • Longest Palindromic Subsequence
    • Matrix Chain Multiplication
    • Longest Zig-Zag Subsequence
    • Viterbi Algorithm
    • K-Coloring
    • Word Wrap
    • Knapsack with Duplicate Items
    • Floyd-Warshall Algorithm
    • Hamiltonian Path and Circuit
    • N-Queens Problem
    • Sudoku Solver
    • Recover Binary Search Tree
    • Edit Distance
    • Dungeon Game
    • Cherry Pickup
    • Travelling Salesman Problem
    Part III:Appendix
    • Proof of Optimal Substructure Property
    • Derivation of Memoization and Tabulation Techniques
    • Problem Analysis
    • Selecting the Right Data Structures for Dynamic Programming Efficiency

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