Recursive Data Structures
Read this page. In the previous unit of our course we studied recursive algorithms. Recursion is a concept that also applies to data. Here we look at recursive data structures - lists, trees, and sets. A list is a structure that consists of elements linked together. If an element is linked to more than one element, the structure is a tree. If each element is linked to two (sub) elements, it is called a binary tree. Trees can be implemented using lists, as shown in the resource for this unit. Several examples of the wide applicability of lists are presented. A link points to all the remaining links, i.e. the rest of the list or the rest of the tree; thus, a link points to a list or to a tree - this is data recursion.
The efficiency of the programming process includes both running time and size of data. This page discusses the latter for recursive lists and trees.
Lastly, why read the last section on sets? Sets are another recursive data structure and the last section 2.7.6, indicates their connection with trees, namely, a set data type can be implemented in several different ways using a list or a tree data type. Thus, the programming process includes implementation decisions, in addition, to design or algorithm decisions. Each of these types of decisions is constrained by the features of the programming language used. The decision choices, such as which data structure to use, will impact efficiency and effectiveness of the program's satisfaction of the program's requirements.
Note: You will notice an unusual use of C++ here. What the author is doing is showing how to pass a fixed-value data-structure as a calling argument.
So back to, "Why convert data into a structure that is isomorphic to our algorithm".
The first reason to do so, is that the code is clearer and easier to read if we convert, then perform operations on the data structure, and then convert it back (if need be).
The second reason do do so, is that if we want to do lots of different operations on the data structure, it is much more efficient to keep it in the form that is isomorphic to the operations we are going to perform on it.
The example we saw was that if we were building a hypothetical image processing application, we could convert an image into quad trees, then rotate or superimpose images at will. We would only need to convert our quadtrees when we need to save or display the image in a rasterized (i.e. array-like) format.
And third, we saw that once we embraced a data structure that was isomorphic to the form of the algorithm, we could employ elegant optimizations that are impossible (or ridiculously inconvenient) when the algorithm and data structure do not match.
Separating conversion from operation allows us to benefit from all three reasons for ensuring that our algorithms and data structures are isomorphic to each other.