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C++ Data Structures and Algorithm Design Principles

You're reading from   C++ Data Structures and Algorithm Design Principles Leverage the power of modern C++ to build robust and scalable applications

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Product type Paperback
Published in Oct 2019
Publisher
ISBN-13 9781838828844
Length 626 pages
Edition 1st Edition
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Authors (4):
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Anil Achary Anil Achary
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Anil Achary
John Carey John Carey
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John Carey
Payas Rajan Payas Rajan
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Payas Rajan
Shreyans Doshi Shreyans Doshi
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Shreyans Doshi
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Table of Contents (11) Chapters Close

About the Book 1. Lists, Stacks, and Queues FREE CHAPTER 2. Trees, Heaps, and Graphs 3. Hash Tables and Bloom Filters 4. Divide and Conquer 5. Greedy Algorithms 6. Graph Algorithms I 7. Graph Algorithms II 8. Dynamic Programming I 9. Dynamic Programming II 1. Appendix

What Is Dynamic Programming?

The best way to answer this question is by example. To illustrate the purpose of dynamic programming, let's consider the Fibonacci sequence:

{ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, … }

By observing the preceding sequence, we can see that, beginning with the third element, each term is equal to the sum of the two preceding terms. This can be simply expressed with the following formula:

F(0) = 0

F(1) = 1

F(n) = F(n-1) + F(n-2)

As we can clearly see, the terms of this sequence have a recursive relationship – the current term, F(n), is based on the results of previous terms, F(n-1) and F(n-2), and thus the preceding equation, that is, F(n) = F(n-1) + F(n-2), is described as the recurrence relation of the sequence. The initial terms, F(0) and F(1), are described as the base cases, or the points in which a solution is produced without the need to recurse further. These operations are shown in the following figure:

Figure 8.1: Computing the nth term in the Fibonacci sequence
Figure 8.1:...
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