By Robert Sedgewick

Textual content offers a device set for programmers to enforce, debug, and use graph algorithms throughout quite a lot of computing device purposes. Covers graph houses and kinds; digraphs and DAGs; minimal spanning timber; shortest paths; community flows; and diagrams, pattern Java code, and targeted set of rules descriptions. Softcover.

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**Additional resources for Algorithms in Java, Part 5: Graph Algorithms**

**Sample text**

2. 4 Graph-processing input/output interface This ADT interface illustrates how we might package related graph-processing methods together in a single class. 3). We will use these methods throughout the book. 2. class GraphIO { static void scanEZ(Graph) static void scan(Graph) static void show(Graph) } Generally, the graph-processing tasks that we consider in this book fall into one of three broad categories: • Compute the value of some measure of the graph. • Compute some subset of the edges of the graph.

Defining such an interface for use in practical applications involves making numerous tradeoffs among simplicity, efficiency, and generality. We consider a few of these tradeoffs next; we address many others in the context of implementations and applications throughout this book. The graph constructor takes the maximum possible number of vertices in the graph as an argument so that implementations can allocate memory accordingly. We adopt this convention solely to make the code compact and readable.

Such space-saving techniques are effective but come at the cost of extra overhead that may fall in the inner loop in time-critical applications. Many applications involve associating other information with each edge in such cases, we can generalize the adjacency matrix to hold any information whatever, not just booleans. Whatever data type that we use for the matrix elements, we need to include an indication whether the indicated edge is present or absent. In Chapters 20 and 21, we explore such representations.

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Categories: Structured Design