#### Overview of this book

Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You’ll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you’ll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.
Preface
Free Chapter
Python Data Types and Structures
Introduction to Algorithm Design
Algorithm Design Techniques and Strategies
Stacks and Queues
Trees
Heaps and Priority Queues
Hash Tables
Graphs and Algorithms
Searching
Sorting
Selection Algorithms
String Matching Algorithms
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Index

# Dynamic programming

Dynamic programming is the most powerful design technique for solving optimization problems. Such problems generally have many possible solutions. The basic idea of dynamic programming is based on the intuition of the divide-and-conquer technique. Here, essentially, we explore the space of all the possible solutions by decomposing the problem into a series of sub-problems and then combining the results to compute the correct solution for the large problem. The divide-and-conquer algorithm is used to solve a problem by combining the solutions of the non-overlapping (disjoint) sub-problems, whereas dynamic programming is used when the sub-problems are overlapping, meaning that the sub-problems share sub-sub-problems. The dynamic programming technique is similar to divide and conquer in that a problem is broken down into smaller problems. However, in divide and conquer, each sub-problem has to be solved before its results can be used to solve bigger problems. In contrast...