""" * Author: Manuel Di Lullo (https://github.com/manueldilullo) * Description: Approximization algorithm for minimum vertex cover problem. Matching Approach. Uses graphs represented with an adjacency list URL: https://mathworld.wolfram.com/MinimumVertexCover.html URL: https://www.princeton.edu/~aaa/Public/Teaching/ORF523/ORF523_Lec6.pdf """ def matching_min_vertex_cover(graph: dict) -> set: """ APX Algorithm for min Vertex Cover using Matching Approach @input: graph (graph stored in an adjacency list where each vertex is represented as an integer) @example: >>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} >>> matching_min_vertex_cover(graph) {0, 1, 2, 4} """ # chosen_vertices = set of chosen vertices chosen_vertices = set() # edges = list of graph's edges edges = get_edges(graph) # While there are still elements in edges list, take an arbitrary edge # (from_node, to_node) and add his extremity to chosen_vertices and then # remove all arcs adjacent to the from_node and to_node while edges: from_node, to_node = edges.pop() chosen_vertices.add(from_node) chosen_vertices.add(to_node) for edge in edges.copy(): if from_node in edge or to_node in edge: edges.discard(edge) return chosen_vertices def get_edges(graph: dict) -> set: """ Return a set of couples that represents all of the edges. @input: graph (graph stored in an adjacency list where each vertex is represented as an integer) @example: >>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3], 3: [0, 1, 2]} >>> get_edges(graph) {(0, 1), (3, 1), (0, 3), (2, 0), (3, 0), (2, 3), (1, 0), (3, 2), (1, 3)} """ edges = set() for from_node, to_nodes in graph.items(): for to_node in to_nodes: edges.add((from_node, to_node)) return edges if __name__ == "__main__": import doctest doctest.testmod() # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")