smaller examples in documentation (#655)

* smaller examples in documentation

* Update docs/src/start/pricing.jl

docs/src/start/cuts.jl

@@ -5,12 +5,12 @@
 
 # Consider the following instance:
 
-J = 1:15
-M = 1:6
-c = [10.13 15.6 15.54 13.41 17.08 17.78 16.36; 19.58 16.83 10.75 15.8 14.89 11.91 15.01; 14.23 17.36 16.05 14.49 18.96 13.58 12.52; 16.47 16.38 18.14 15.46 11.64 10.56 12.65; 17.87 18.25 13.12 19.16 16.33 11.28 17.71; 11.09 16.76 15.5 12.08 13.06 19.03 14.89; 15.19 13.86 16.08 19.47 15.79 13.05 11.06; 10.79 18.96 16.11 19.78 15.57 12.68 14.76; 12.03 19.03 16.01 14.46 12.77 19.57 19.83; 14.48 11.75 16.97 19.95 18.32 13.43 12.7; 10.01 10.8 16.22 16.69 17.26 11.73 13.55; 17.84 11.91 10.47 10.91 17.87 14.63 11.52; 17.93 11.24 12.54 10.48 18.69 19.58 17.67; 16.05 15.83 10.31 19.81 18.72 10.2 14.95; 17.1 15.53 12.34 18.34 13.64 12.3 12.79];
-w = [5, 4, 5, 6, 8, 9, 5, 8, 10, 7, 7, 7, 7, 7, 8];
-Q = [ 25,  24,  31,  28,  24,  30];
-f = [105, 103, 109, 112, 100, 104];
+J = 1:10
+M = 1:5
+c = [10.13 15.6 15.54 13.41 17.08;19.58 16.83 10.75 15.8 14.89;14.23 17.36 16.05 14.49 18.96;16.47 16.38 18.14 15.46 11.64;17.87 18.25 13.12 19.16 16.33;11.09 16.76 15.5 12.08 13.06;15.19 13.86 16.08 19.47 15.79;10.79 18.96 16.11 19.78 15.55;12.03 19.03 16.01 14.46 12.77;14.48 11.75 16.97 19.95 18.32];
+w = [5, 4, 5, 6, 8, 9, 5, 8, 10, 7];
+Q = [25,  24,  31,  28,  24];
+f = [105, 103, 109, 112, 100];
 
 # We define the dependencies:
 

docs/src/start/pricing.jl

@@ -13,11 +13,11 @@ const MOI = MathOptInterface;
 
 # Let us see an example with the following generalized assignment problem :
 
-M = 1:4;
-J = 1:30;
-c = [12.7 22.5 8.9 20.8 13.6 12.4 24.8 19.1 11.5 17.4 24.7 6.8 21.7 14.3 10.5 15.2 14.3 12.6 9.2 20.8 11.7 17.3 9.2 20.3 11.4 6.2 13.8 10.0 20.9 20.6;  19.1 24.8 24.4 23.6 16.1 20.6 15.0 9.5 7.9 11.3 22.6 8.0 21.5 14.7 23.2 19.7 19.5 7.2 6.4 23.2 8.1 13.6 24.6 15.6 22.3 8.8 19.1 18.4 22.9 8.0;  18.6 14.1 22.7 9.9 24.2 24.5 20.8 12.9 17.7 11.9 18.7 10.1 9.1 8.9 7.7 16.6 8.3 15.9 24.3 18.6 21.1 7.5 16.8 20.9 8.9 15.2 15.7 12.7 20.8 10.4;  13.1 16.2 16.8 16.7 9.0 16.9 17.9 12.1 17.5 22.0 19.9 14.6 18.2 19.6 24.2 12.9 11.3 7.5 6.5 11.3 7.8 13.8 20.7 16.8 23.6 19.1 16.8 19.3 12.5 11.0];
-w = [61 70 57 82 51 74 98 64 86 80 69 79 60 76 78 71 50 99 92 83 53 91 68 61 63 97 91 77 68 80; 50 57 61 83 81 79 63 99 82 59 83 91 59 99 91 75 66 100 69 60 87 98 78 62 90 89 67 87 65 100; 91 81 66 63 59 81 87 90 65 55 57 68 92 91 86 74 80 89 95 57 55 96 77 60 55 57 56 67 81 52;  62 79 73 60 75 66 68 99 69 60 56 100 67 68 54 66 50 56 70 56 72 62 85 70 100 57 96 69 65 50];
-Q = [1020 1460 1530 1190];
+M = 1:3;
+J = 1:15;
+c = [12.7 22.5 8.9 20.8 13.6 12.4 24.8 19.1 11.5 17.4 24.7 6.8 21.7 14.3 10.5; 19.1 24.8 24.4 23.6 16.1 20.6 15.0 9.5 7.9 11.3 22.6 8.0 21.5 14.7 23.2;  18.6 14.1 22.7 9.9 24.2 24.5 20.8 12.9 17.7 11.9 18.7 10.1 9.1 8.9 7.7; 13.1 16.2 16.8 16.7 9.0 16.9 17.9 12.1 17.5 22.0 19.9 14.6 18.2 19.6 24.2];
+w = [61 70 57 82 51 74 98 64 86 80 69 79 60 76 78; 50 57 61 83 81 79 63 99 82 59 83 91 59 99 91;91 81 66 63 59 81 87 90 65 55 57 68 92 91 86; 62 79 73 60 75 66 68 99 69 60 56 100 67 68 54];
+Q = [1020 1460 1530];
 
 # with the following Coluna configuration
 
@@ -68,7 +68,9 @@ end
 function my_pricing_callback(cbdata)
     ## Retrieve the index of the subproblem (it will be one of the values in M_axis)
     cur_machine = BD.callback_spid(cbdata, model)
-    println("Pricing callback for machine $(cur_machine).")
+
+    ## Uncomment to see that the pricing callback is called.
+    ## println("Pricing callback for machine $(cur_machine).")
 
     ## Retrieve reduced costs of subproblem variables
     red_costs = [BD.callback_reduced_cost(cbdata, x[cur_machine, j]) for j in J]

docs/src/start/start.jl

@@ -26,11 +26,11 @@
 
 # Let us consider the following instance. 
 
-M = 1:4;
-J = 1:30;
-c = [12.7 22.5 8.9 20.8 13.6 12.4 24.8 19.1 11.5 17.4 24.7 6.8 21.7 14.3 10.5 15.2 14.3 12.6 9.2 20.8 11.7 17.3 9.2 20.3 11.4 6.2 13.8 10.0 20.9 20.6;  19.1 24.8 24.4 23.6 16.1 20.6 15.0 9.5 7.9 11.3 22.6 8.0 21.5 14.7 23.2 19.7 19.5 7.2 6.4 23.2 8.1 13.6 24.6 15.6 22.3 8.8 19.1 18.4 22.9 8.0;  18.6 14.1 22.7 9.9 24.2 24.5 20.8 12.9 17.7 11.9 18.7 10.1 9.1 8.9 7.7 16.6 8.3 15.9 24.3 18.6 21.1 7.5 16.8 20.9 8.9 15.2 15.7 12.7 20.8 10.4;  13.1 16.2 16.8 16.7 9.0 16.9 17.9 12.1 17.5 22.0 19.9 14.6 18.2 19.6 24.2 12.9 11.3 7.5 6.5 11.3 7.8 13.8 20.7 16.8 23.6 19.1 16.8 19.3 12.5 11.0];
-w = [61 70 57 82 51 74 98 64 86 80 69 79 60 76 78 71 50 99 92 83 53 91 68 61 63 97 91 77 68 80; 50 57 61 83 81 79 63 99 82 59 83 91 59 99 91 75 66 100 69 60 87 98 78 62 90 89 67 87 65 100; 91 81 66 63 59 81 87 90 65 55 57 68 92 91 86 74 80 89 95 57 55 96 77 60 55 57 56 67 81 52;  62 79 73 60 75 66 68 99 69 60 56 100 67 68 54 66 50 56 70 56 72 62 85 70 100 57 96 69 65 50];
-Q = [1020 1460 1530 1190];
+M = 1:3;
+J = 1:15;
+c = [12.7 22.5 8.9 20.8 13.6 12.4 24.8 19.1 11.5 17.4 24.7 6.8 21.7 14.3 10.5; 19.1 24.8 24.4 23.6 16.1 20.6 15.0 9.5 7.9 11.3 22.6 8.0 21.5 14.7 23.2;  18.6 14.1 22.7 9.9 24.2 24.5 20.8 12.9 17.7 11.9 18.7 10.1 9.1 8.9 7.7; 13.1 16.2 16.8 16.7 9.0 16.9 17.9 12.1 17.5 22.0 19.9 14.6 18.2 19.6 24.2];
+w = [61 70 57 82 51 74 98 64 86 80 69 79 60 76 78; 50 57 61 83 81 79 63 99 82 59 83 91 59 99 91;91 81 66 63 59 81 87 90 65 55 57 68 92 91 86; 62 79 73 60 75 66 68 99 69 60 56 100 67 68 54];
+Q = [1020 1460 1530];
 
 # We write the model with [JuMP](https://github.com/jump-dev/JuMP.jl), a domain-specific modeling
 # language for mathematical optimization embedded in Julia. We optimize with GLPK.