Implementing a dijstra's shortest path algorithm for a Monte Carlo simulation of a graph. Dijkstra’s algorithm is the basis for many route calculations and optimizations programs.
Graph algorithms and graph representation are a critical tool in CS. We want to create a graph as an ADT (Abstract Data Type) using C++ classes. The basic problem is to write Dijkstra’s algorithm as a class member function (method in OO speak).
An edge will have a positive cost that is its distance. Have a procedure that produces a randomly generated set of edges with positive distances. Assume the graphs are undirected. The random graph procedure should have edge density as a parameter and distance range as a parameter. So a graph whose density is 0.1 would have 10% of its edges picked at random and its edge distance would be selected at random from the distance range. The procedure should run through all possible undirected edges, say (i,j) and place the edge in the graph if a random probability calculation is less than the density. Compute for a set of randomly generated graphs an average shortest path.
Just clone git clone https://github.com/AdrianVazquezMejia/Dijsktra_Algorithm.git
This program is developed under Cross GCC compiler.
Professor Ira Pohl, California University.
Professor Steven S. Skiena, State University of New York.
MIT License
Copyright (c) [2020] [Adrian Vazquez]
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