The Dynamic Programming Manual
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.
About the Author
Table of Contents
- Preface: Unleash the Power of Dynamic Programming
- 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
- 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
- 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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