gcp_fixed_k.py

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"""
Copyright (c) by Joao Pedro PEDROSO and Mikio KUBO, 2012
"""
 
from pyscipopt import Model, quicksum
 
 
def gcp_fixed_k(V, E, K):
    """gcp_fixed_k -- model for minimizing number of bad edges in coloring a graph
    Parameters:
        - V: set/list of nodes in the graph
        - E: set/list of edges in the graph
        - K: number of colors to be used
    Returns a model, ready to be solved.
    """
    model = Model("gcp - fixed k")
 
    x, z = {}, {}
    for i in V:
        for k in range(K):
            x[i, k] = model.addVar(vtype="B", name="x(%s,%s)" % (i, k))
    for (i, j) in E:
        z[i, j] = model.addVar(vtype="B", name="z(%s,%s)" % (i, j))
 
    for i in V:
        model.addCons(quicksum(x[i, k] for k in range(K)) == 1, "AssignColor(%s)" % i)
 
    for (i, j) in E:
        for k in range(K):
            model.addCons(x[i, k] + x[j, k] <= 1 + z[i, j], "BadEdge(%s,%s,%s)" % (i, j, k))
 
    model.setObjective(quicksum(z[i, j] for (i, j) in E), "minimize")
 
    model.data = x, z
    return model
 
 
def solve_gcp(V, E):
    """solve_gcp -- solve the graph coloring problem with bisection and fixed-k model
    Parameters:
        - V: set/list of nodes in the graph
        - E: set/list of edges in the graph
    Returns tuple with number of colors used, and dictionary mapping colors to vertices
    """
    LB = 0
    UB = len(V)
    color = {}
    while UB - LB > 1:
        K = int((UB + LB) / 2)
        gcp = gcp_fixed_k(V, E, K)
        # gcp.Params.OutputFlag = 0 # silent mode
        # gcp.Params.Cutoff = .1
        gcp.setObjlimit(0.1)
        gcp.optimize()
        status = gcp.getStatus()
        if status == "optimal":
            x, z = gcp.data
            for i in V:
                for k in range(K):
                    if gcp.getVal(x[i, k]) > 0.5:
                        color[i] = k
                        break
                # else:
                #     raise "undefined color for", i
            UB = K
        else:
            LB = K
 
    return UB, color
 
 
import random
 
 
def make_data(n, prob):
    """make_data: prepare data for a random graph
    Parameters:
        - n: number of vertices
        - prob: probability of existence of an edge, for each pair of vertices
    Returns a tuple with a list of vertices and a list edges.
    """
    V = range(1, n + 1)
    E = [(i, j) for i in V for j in V if i < j and random.random() < prob]
    return V, E
 
 
if __name__ == "__main__":
    random.seed(1)
    V, E = make_data(75, .25)
 
    K, color = solve_gcp(V, E)
    print("minimum number of colors:", K)
    print("solution:", color)