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Perspective

Recursion lies at the heart of early theoretical studies into the nature of computation. Recursive functions and programs play a central role in mathematical studies that attempt to separate problems that can be solved by a computer from problems that cannot be.

It is certainly impossible to do justice to topics as far-reaching as trees and recursion in so brief a discussion. Many of the best examples of recursive programs will be our focus throughout the tutorial—divide-and-conquer algorithms and recursive data structures that have been applied successfully to solve a wide variety of problems. For many apps, there is no reason to go beyond a simple, direct recursive implementation; for others, we will consider the derivation of alternate nonrecursive and bottom-up implementations.

In this tutorial, our interest lies in the practical aspects of recursive programs and data structures. Our goal is to exploit recursion to produce elegant and efficient implementations. To meet that goal, we need to have particular respect for the dangers of simple programs that lead to an exponential number of method invocations or impossibly deep nesting. Despite this pitfall, recursive programs and data structures are attractive because they often provide us with inductive arguments that can convince us that our programs are correct and efficient.

We use trees throughout the tutorial, both to help us understand the dynamic properties of programs and as dynamic data structures. Chapters 12 through 15 in particular are largely devoted to the manipulation of explicit tree structures. The properties described in this chapter provide us with the basic information that we need if we are to use explicit tree structures effectively.

Despite its central role in algorithm design, recursion is not a panacea. As we discovered in our study of tree- and graph-traversal algorithms, stack-based (inherently recursive) algorithms are not the only option when we have multiple computational tasks to manage. An effective algorithm-design technique for many problems is the use of generalized queue implementations other than stacks to give us the freedom to choose the next task according to some more subjective criteria than simply choosing the most recent. Data structures and algorithms that efficiently support such operations are a prime topic of , and we shall encounter many examples of their app when we consider graph algorithms in Part 5.


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