First Commit

BPP/BPP Instance Generator.m

+% Instancias gaussianas centradas en 1/2 de C
+instance = [10,100,single_gaussian_packages(10,50,20,10,100)]';
+save_instance("instances\instance1.txt",instance)
+instance = [10,120,single_gaussian_packages(10,60,24,10,120)]';
+save_instance("instances\instance2.txt",instance)
+instance = [10,150,single_gaussian_packages(10,75,30,10,150)]';
+save_instance("instances\instance3.txt",instance)
+instance = [12,100,single_gaussian_packages(12,50,20,10,100)]';
+save_instance("instances\instance4.txt",instance)
+instance = [12,120,single_gaussian_packages(12,60,24,10,120)]';
+save_instance("instances\instance5.txt",instance)
+instance = [12,150,single_gaussian_packages(12,75,30,10,150)]';
+save_instance("instances\instance6.txt",instance)
+
+% Instancias de 2 gaussianas centradas en 1/3 y 2/3 de C
+instance = [10,100,two_gaussian_packages(10,33,10,66,10,0.5,10,100)]';
+save_instance("instances\instance7.txt",instance)
+instance = [10,120,two_gaussian_packages(10,40,12,80,12,0.5,10,120)]';
+save_instance("instances\instance8.txt",instance)
+instance = [10,150,two_gaussian_packages(10,50,15,100,15,0.5,10,150)]';
+save_instance("instances\instance9.txt",instance)
+instance = [12,100,two_gaussian_packages(12,33,10,66,10,0.5,10,100)]';
+save_instance("instances\instance10.txt",instance)
+instance = [12,120,two_gaussian_packages(12,40,12,80,12,0.5,10,120)]';
+save_instance("instances\instance11.txt",instance)
+instance = [12,150,two_gaussian_packages(12,50,15,100,15,0.5,10,150)]';
+save_instance("instances\instance12.txt",instance)
+
+% Instancias uniformes
+instance = [10,100,uniform_packages(10,10,100)]';
+save_instance("instances\instance13.txt",instance)
+instance = [10,120,uniform_packages(10,10,120)]';
+save_instance("instances\instance14.txt",instance)
+instance = [10,150,uniform_packages(10,10,150)]';
+save_instance("instances\instance15.txt",instance)
+instance = [12,100,uniform_packages(12,10,100)]';
+save_instance("instances\instance16.txt",instance)
+instance = [12,120,uniform_packages(12,10,120)]';
+save_instance("instances\instance17.txt",instance)
+instance = [12,150,uniform_packages(12,10,150)]';
+save_instance("instances\instance18.txt",instance)
+
+
+function save_instance(name,instance)
+fileID = fopen(name,'w');
+fprintf(fileID,"%d\r\n",instance);
+fclose(fileID);
+end
+
+function plotear(instance)
+inc = [];
+for i = 0:10:instance(2)
+    inc(end+1) = sum(instance(3:end)==i);
+end
+bar(0:10:instance(2),inc)
+end
+
+function res = single_gaussian_packages(n,centro,sigma,diffMin,C)
+% n         número de paquetes
+% centro    centro de la distribución
+% sigma     anchura de la distribución
+% diffMin   minima diferencia entre paquetes
+% C         capacidad del container
+
+res = [];
+while length(res) < n
+    aux = round(normrnd(centro,sigma));   
+    if mod(aux,diffMin) < diffMin/2
+        aux = aux - mod(aux,diffMin);
+    else
+        aux = aux + diffMin - mod(aux,diffMin);
+    end 
+    if aux > 0 && aux < C
+        res(end+1) = aux;    
+    end
+end
+end
+
+function res = two_gaussian_packages(n,centro1,sigma1,centro2,sigma2,prob1,diffMin,C)
+% n         número de paquetes
+% centro    centro de la distribución
+% sigma     anchura de la distribución
+% prob1     probabilidad de que un paquete caiga en la primera gaussiana
+% diffMin   minima diferencia entre paquetes
+% C         capacidad del container
+res = [];
+while length(res) < n
+    if rand() < prob1
+        aux = round(normrnd(centro1,sigma1));  
+    else
+        aux = round(normrnd(centro2,sigma2));
+    end
+    if mod(aux,diffMin) < diffMin/2
+        aux = aux - mod(aux,diffMin);
+    else
+        aux = aux + diffMin - mod(aux,diffMin);
+    end
+    if aux > 0 && aux < C
+        res(end+1) = aux;  
+    end
+end
+end
+
+function res = uniform_packages(n,diffMin,C)
+% n         número de paquetes
+% diffMin   minima diferencia entre paquetes
+% C         capacidad del container
+res = zeros(1,n);
+for i = 1:n
+    res(i) = randsample(diffMin:diffMin:C-1,1);
+end
+end
\ No newline at end of file

BPP/BPP_10.bpp

+10
+10
+5
+5
+5
+5
+2
+2
+5
+2
+2
+5
\ No newline at end of file

BPP/BPP_12.bpp

+12
+10
+5
+5
+2
+2
+2
+5
+5
+2
+5
+2
+5
+5
\ No newline at end of file

BPP/BPP_14.bpp

+14
+12
+5
+5
+5
+2
+2
+2
+5
+5
+5
+2
+2
+2
+2
+2
\ No newline at end of file

BPP/BPP_3.bpp

+3
+10
+5
+5
+2
\ No newline at end of file

BPP/BPP_4.bpp

+4
+10
+5
+2
+2
+2
\ No newline at end of file

BPP/BPP_5.bpp

+5
+12
+5
+5
+5
+2
+2
\ No newline at end of file

BPP/BPP_6.bpp

+6
+10
+5
+5
+2
+2
+5
+5
\ No newline at end of file

BPP/BPP_7.bpp

+7
+10
+5
+5
+2
+5
+5
+2
+2
\ No newline at end of file

BPP/BPP_8.bpp

+8
+12
+5
+5
+2
+2
+2
+2
+2
+2
\ No newline at end of file

BPP/BPP_9.bpp

+9
+12
+5
+5
+2
+2
+2
+5
+5
+2
+2
\ No newline at end of file

BPP/Solutions BPP.txt

+[[2], [], [0, 1]] - 2
+[[1, 2], [], [], [0, 3]] - 2
+[[], [], [0, 1], [], [2, 3, 4]] - 2
+[[], [1, 5], [], [2, 3], [], [0, 4]] - 3
+[[3, 4], [], [0, 1], [5], [2, 6], [], []] - 4
+[[], [1, 5], [4], [0, 3], [], [], [2, 6, 7], []] - 4
+[[], [2, 5, 8], [0, 1], [], [6], [3, 4, 7], [], [], []] - 4
+[[0, 1], [3, 5], [], [2], [6, 7], [], [8], [], [4, 9], []] - 6
+[[], [1, 3], [0, 6], [7], [], [4, 10], [2, 9, 11], [], [8], [], [], [5]] - 7
+[[], [], [], [], [3, 6], [], [2], [1], [], [0], [4, 9, 12], [], [8, 10, 11], [5, 7, 13]] - 7
\ No newline at end of file

MCP/MCPInstanceGenerator.py

+import numpy as np
+from pathlib import Path
+import networkx as nx
+import random
+import matplotlib.pyplot as plt
+
+instance = 'MaxCut_20'
+n_reduced_variables = 50
+edges = 0
+
+path = Path('generated_data', f'{instance}.mc')
+
+output = ""
+
+for i in range(n_reduced_variables):
+    for j in range(n_reduced_variables):
+        int = random.randint(0,9)
+        if i != j and int<4:
+            output = output + str(i+1) + " " + str(j+1) + " " + str(1)
+            output = output + "\n"
+            edges = edges+1
+
+f = open(path, "x")
+
+f.write(str(n_reduced_variables) + " " + str(edges))
+f.write("\n")
+f.write(output)
+f.close()
\ No newline at end of file

MCP/MaxCut_10.mc

+10 37
+1 7 1
+1 9 1
+1 10 1
+2 1 1
+2 4 1
+2 5 1
+2 7 1
+2 9 1
+2 10 1
+3 1 1
+3 2 1
+3 4 1
+3 7 1
+4 5 1
+4 7 1
+4 8 1
+5 3 1
+5 6 1
+5 9 1
+5 10 1
+6 1 1
+6 2 1
+6 5 1
+6 7 1
+6 9 1
+7 2 1
+7 3 1
+7 5 1
+7 8 1
+8 1 1
+8 2 1
+8 9 1
+8 10 1
+9 3 1
+9 6 1
+10 7 1
+10 8 1

MCP/MaxCut_20.mc

+20 148
+1 2 1
+1 9 1
+1 17 1
+1 18 1
+1 19 1
+2 3 1
+2 4 1
+2 5 1
+2 8 1
+2 9 1
+2 10 1
+2 11 1
+2 13 1
+2 14 1
+2 20 1
+3 1 1
+3 5 1
+3 7 1
+3 8 1
+3 9 1
+3 10 1
+3 16 1
+4 1 1
+4 6 1
+4 7 1
+4 8 1
+4 9 1
+4 11 1
+4 14 1
+4 15 1
+4 18 1
+4 20 1
+5 2 1
+5 3 1
+5 6 1
+5 7 1
+5 13 1
+5 14 1
+5 18 1
+5 20 1
+6 4 1
+6 8 1
+6 10 1
+6 12 1
+6 14 1
+6 19 1
+6 20 1
+7 1 1
+7 4 1
+7 10 1
+7 11 1
+7 16 1
+7 19 1
+8 1 1
+8 2 1
+8 9 1
+8 10 1
+8 13 1
+8 14 1
+8 15 1
+8 20 1
+9 2 1
+9 5 1
+9 11 1
+9 13 1
+9 15 1
+9 16 1
+9 17 1
+9 19 1
+9 20 1
+10 5 1
+10 7 1
+10 8 1
+10 9 1
+10 13 1
+10 20 1
+11 1 1
+11 3 1
+11 6 1
+11 12 1
+11 13 1
+12 2 1
+12 5 1
+12 9 1
+12 18 1
+12 20 1
+13 1 1
+13 3 1
+13 8 1
+13 9 1
+13 10 1
+13 12 1
+13 16 1
+13 17 1
+13 20 1
+14 3 1
+14 5 1
+14 6 1
+14 11 1
+14 12 1
+14 19 1
+15 1 1
+15 4 1
+15 5 1
+15 6 1
+15 9 1
+15 11 1
+15 13 1
+15 14 1
+15 20 1
+16 1 1
+16 2 1
+16 6 1
+16 7 1
+16 8 1
+16 10 1
+16 11 1
+16 13 1
+16 19 1
+17 3 1
+17 4 1
+17 9 1
+17 10 1
+17 11 1
+17 18 1
+17 20 1
+18 6 1
+18 7 1
+18 9 1
+18 12 1
+18 13 1
+18 15 1
+18 17 1
+18 19 1
+19 2 1
+19 7 1
+19 9 1
+19 10 1
+19 11 1
+19 12 1
+19 14 1
+19 18 1
+19 20 1
+20 1 1
+20 3 1
+20 10 1
+20 13 1
+20 14 1