Dynamic programming

De: Koffka Khan

Narrado por: Virtual Voice

Duración: 18 h y 12 m

Resumen del Editor

Dynamic programming is a powerful algorithmic technique used to solve optimization problems that can be broken down into smaller subproblems. The key idea behind dynamic programming is to avoid redundant computations by storing the results of previously solved subproblems and reusing them when needed. This can lead to significant efficiency gains, particularly when the same subproblems are encountered multiple times.

Dynamic programming has a wide range of applications, from solving complex optimization problems in operations research and economics to computer science and artificial intelligence. Some well-known examples of dynamic programming algorithms include the Fibonacci sequence, the Knapsack problem, and the shortest path problem in graphs.

To apply dynamic programming, one typically needs to identify the optimal substructure of the problem and determine how to efficiently compute the solutions to the subproblems. Dynamic programming algorithms can be categorized as either top-down (memoization) or bottom-up (tabulation) approaches, depending on the order in which subproblems are solved.

Overall, dynamic programming is a powerful technique that has revolutionized the way we solve optimization problems in various fields, and continues to be an active area of research and development.

This book focuses on the Dynamic Programming and various algorithmic approaches integrated with it.

This book is an excellent resource for learning the Dynamic Programming. It contains a lot of theoretical knowledge and practical examples. It covers Tabular, Memoization, Bottom-up, Top-down, Divide-and-conquer, Multistage, Convex, Parallel, Online, and Stochastic approaches. Each topic is placed in a study-centric format with introductions, implementations and practical examples.