internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng)
{
// First define the variables, three continuous and one integer
double[] xlb = { 0.0, 0.0, 0.0, 2.0 };
double[] xub = { 40.0, System.Double.MaxValue,
System.Double.MaxValue, 3.0 };
NumVarType[] xt = { NumVarType.Float, NumVarType.Float,
NumVarType.Float, NumVarType.Int };
INumVar[] x = model.NumVarArray(4, xlb, xub, xt);
var[0] = x;
// Objective Function: maximize x0 + 2*x1 + 3*x2 + x3
double[] objvals = { 1.0, 2.0, 3.0, 1.0 };
model.AddMaximize(model.ScalProd(x, objvals));
// Three constraints
rng[0] = new IRange[3];
// - x0 + x1 + x2 + 10*x3 <= 20
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, x[0]),
model.Prod(1.0, x[1]),
model.Prod(1.0, x[2]),
model.Prod(10.0, x[3])), 20.0);
// x0 - 3*x1 + x2 <= 30
rng[0][1] = model.AddLe(model.Sum(model.Prod(1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod(1.0, x[2])), 30.0);
// x1 - 3.5*x3 = 0
rng[0][2] = model.AddEq(model.Sum(model.Prod(1.0, x[1]),
model.Prod(-3.5, x[3])), 0.0);
}
internal static void PopulateByColumn(IMPModeler model,
INumVar[][] var,
IRange[][] rng)
{
IObjective obj = model.AddMaximize();
rng[0] = new IRange[2];
rng[0][0] = model.AddRange(-System.Double.MaxValue, 20.0, "c1");
rng[0][1] = model.AddRange(-System.Double.MaxValue, 30.0, "c2");
IRange r0 = rng[0][0];
IRange r1 = rng[0][1];
var[0] = new INumVar[3];
var[0][0] = model.NumVar(model.Column(obj, 1.0).And(
model.Column(r0, -1.0).And(
model.Column(r1, 1.0))),
0.0, 40.0, "x1");
var[0][1] = model.NumVar(model.Column(obj, 2.0).And(
model.Column(r0, 1.0).And(
model.Column(r1, -3.0))),
0.0, System.Double.MaxValue, "x2");
var[0][2] = model.NumVar(model.Column(obj, 3.0).And(
model.Column(r0, 1.0).And(
model.Column(r1, 1.0))),
0.0, System.Double.MaxValue, "x3");
}
internal static void PopulateByRow (IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
// First define the variables, three continuous and one integer
double[] xlb = {0.0, 0.0, 0.0, 2.0};
double[] xub = {40.0, System.Double.MaxValue,
System.Double.MaxValue, 3.0};
NumVarType[] xt = {NumVarType.Float, NumVarType.Float,
NumVarType.Float, NumVarType.Int};
INumVar[] x = model.NumVarArray(4, xlb, xub, xt);
var[0] = x;
// Objective Function: maximize x0 + 2*x1 + 3*x2 + x3
double[] objvals = {1.0, 2.0, 3.0, 1.0};
model.AddMaximize(model.ScalProd(x, objvals));
// Three constraints
rng[0] = new IRange[3];
// - x0 + x1 + x2 + 10*x3 <= 20
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, x[0]),
model.Prod( 1.0, x[1]),
model.Prod( 1.0, x[2]),
model.Prod(10.0, x[3])), 20.0);
// x0 - 3*x1 + x2 <= 30
rng[0][1] = model.AddLe(model.Sum(model.Prod( 1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod( 1.0, x[2])), 30.0);
// x1 - 3.5*x3 = 0
rng[0][2] = model.AddEq(model.Sum(model.Prod( 1.0, x[1]),
model.Prod(-3.5, x[3])), 0.0);
}
internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0, System.Double.MaxValue, System.Double.MaxValue};
var[0] = model.NumVarArray(3, lb, ub);
double[] objvals = {1.0, 2.0, 3.0};
model.AddMaximize(model.ScalProd(var[0], objvals));
rng[0] = new IRange[2];
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, var[0][0]),
model.Prod( 1.0, var[0][1]),
model.Prod( 1.0, var[0][2])), 20.0);
rng[0][1] = model.AddLe(model.Sum(model.Prod( 1.0, var[0][0]),
model.Prod(-3.0, var[0][1]),
model.Prod( 1.0, var[0][2])), 30.0);
}
internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng)
{
double[] lb = { 0.0, 0.0, 0.0 };
double[] ub = { 40.0, System.Double.MaxValue, System.Double.MaxValue };
var[0] = model.NumVarArray(3, lb, ub);
double[] objvals = { 1.0, 2.0, 3.0 };
model.AddMaximize(model.ScalProd(var[0], objvals));
rng[0] = new IRange[2];
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, var[0][0]),
model.Prod(1.0, var[0][1]),
model.Prod(1.0, var[0][2])), 20.0);
rng[0][1] = model.AddLe(model.Sum(model.Prod(1.0, var[0][0]),
model.Prod(-3.0, var[0][1]),
model.Prod(1.0, var[0][2])), 30.0);
}
internal static ILPMatrix PopulateByRow(IMPModeler model)
{
ILPMatrix lp = model.AddLPMatrix();
double[] lb = { 0.0, 0.0, 0.0 };
double[] ub = { 40.0, System.Double.MaxValue, System.Double.MaxValue };
INumVar[] x = model.NumVarArray(model.ColumnArray(lp, 3), lb, ub);
double[] lhs = { -System.Double.MaxValue, -System.Double.MaxValue };
double[] rhs = { 20.0, 30.0 };
double[][] val = { new double[] { -1.0, 1.0, 1.0 },
new double[] { 1.0, -3.0, 1.0 } };
int[][] ind = { new int[] { 0, 1, 2 },
new int[] { 0, 1, 2 } };
lp.AddRows(lhs, rhs, ind, val);
double[] objvals = { 1.0, 2.0, 3.0 };
model.AddMaximize(model.ScalProd(x, objvals));
return(lp);
}
// The following methods all populate the problem with data for the following
// linear program:
//
// Maximize
// x1 + 2 x2 + 3 x3
// Subject To
// - x1 + x2 + x3 <= 20
// x1 - 3 x2 + x3 <= 30
// Bounds
// 0 <= x1 <= 40
// End
//
// using the IMPModeler API
internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng)
{
double[] lb = { 0.0, 0.0, 0.0 };
double[] ub = { 40.0, System.Double.MaxValue, System.Double.MaxValue };
string[] varname = { "x1", "x2", "x3" };
INumVar[] x = model.NumVarArray(3, lb, ub, varname);
var[0] = x;
double[] objvals = { 1.0, 2.0, 3.0 };
model.AddMaximize(model.ScalProd(x, objvals));
rng[0] = new IRange[2];
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, x[0]),
model.Prod(1.0, x[1]),
model.Prod(1.0, x[2])), 20.0, "c1");
rng[0][1] = model.AddLe(model.Sum(model.Prod(1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod(1.0, x[2])), 30.0, "c2");
}
internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng)
{
// Define the variables one-by-one
INumVar[] x = new INumVar[4];
x[0] = model.NumVar(0.0, 40.0, "x0");
x[1] = model.IntVar(0, System.Int32.MaxValue, "x1");
x[2] = model.IntVar(0, System.Int32.MaxValue, "x2");
x[3] = model.IntVar(2, 3, "x3");
var[0] = x;
// Objective Function
model.AddMaximize(model.Sum(model.Prod(1.0, x[0]),
model.Prod(2.0, x[1]),
model.Prod(3.0, x[2]),
model.Prod(1.0, x[3])));
// Define three constraints one-by-one
rng[0] = new IRange[3];
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, x[0]),
model.Prod(1.0, x[1]),
model.Prod(1.0, x[2]),
model.Prod(10.0, x[3])),
20.0, "rng0");
rng[0][1] = model.AddLe(model.Sum(model.Prod(1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod(1.0, x[2])),
30.0, "rng1");
rng[0][2] = model.AddEq(model.Sum(model.Prod(1.0, x[1]),
model.Prod(-3.5, x[3])),
0, "rng2");
// add special ordered set of type 1
INumVar[] sosvars = { x[2], x[3] };
double[] sosweights = { 25.0, 18.0 };
model.AddSOS1(sosvars, sosweights);
}
internal static ILPMatrix PopulateByRow(IMPModeler model) {
ILPMatrix lp = model.AddLPMatrix();
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0, System.Double.MaxValue, System.Double.MaxValue};
INumVar[] x = model.NumVarArray(model.ColumnArray(lp, 3), lb, ub);
double[] lhs = {-System.Double.MaxValue, -System.Double.MaxValue};
double[] rhs = {20.0, 30.0};
double[][] val = {new double[] {-1.0, 1.0, 1.0},
new double[] { 1.0, -3.0, 1.0}};
int[][] ind = {new int[] {0, 1, 2},
new int[] {0, 1, 2}};
lp.AddRows(lhs, rhs, ind, val);
double[] objvals = {1.0, 2.0, 3.0};
model.AddMaximize(model.ScalProd(x, objvals));
return (lp);
}
internal static void PopulateByColumn(IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
IObjective obj = model.AddMaximize();
rng[0] = new IRange[2];
rng[0][0] = model.AddRange(-System.Double.MaxValue, 20.0, "c1");
rng[0][1] = model.AddRange(-System.Double.MaxValue, 30.0, "c2");
IRange r0 = rng[0][0];
IRange r1 = rng[0][1];
var[0] = new INumVar[3];
var[0][0] = model.NumVar(model.Column(obj, 1.0).And(
model.Column(r0, -1.0).And(
model.Column(r1, 1.0))),
0.0, 40.0, "x1");
var[0][1] = model.NumVar(model.Column(obj, 2.0).And(
model.Column(r0, 1.0).And(
model.Column(r1, -3.0))),
0.0, System.Double.MaxValue, "x2");
var[0][2] = model.NumVar(model.Column(obj, 3.0).And(
model.Column(r0, 1.0).And(
model.Column(r1, 1.0))),
0.0, System.Double.MaxValue, "x3");
}
// The following methods all populate the problem with data for the following
// linear program:
//
// Maximize
// x1 + 2 x2 + 3 x3
// Subject To
// - x1 + x2 + x3 <= 20
// x1 - 3 x2 + x3 <= 30
// Bounds
// 0 <= x1 <= 40
// End
//
// using the IMPModeler API
internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0, System.Double.MaxValue, System.Double.MaxValue};
string[] varname = {"x1", "x2", "x3"};
INumVar[] x = model.NumVarArray(3, lb, ub, varname);
var[0] = x;
double[] objvals = {1.0, 2.0, 3.0};
model.AddMaximize(model.ScalProd(x, objvals));
rng[0] = new IRange[2];
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, x[0]),
model.Prod( 1.0, x[1]),
model.Prod( 1.0, x[2])), 20.0, "c1");
rng[0][1] = model.AddLe(model.Sum(model.Prod( 1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod( 1.0, x[2])), 30.0, "c2");
}
internal static void PopulateByRow (IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
// Define the variables one-by-one
INumVar[] x = new INumVar[4];
x[0] = model.NumVar(0.0, 40.0, "x0");
x[1] = model.IntVar(0, System.Int32.MaxValue, "x1");
x[2] = model.IntVar(0, System.Int32.MaxValue, "x2");
x[3] = model.IntVar(2, 3, "x3");
var[0] = x;
// Objective Function
model.AddMaximize(model.Sum(model.Prod( 1.0, x[0]),
model.Prod( 2.0, x[1]),
model.Prod( 3.0, x[2]),
model.Prod( 1.0, x[3])));
// Define three constraints one-by-one
rng[0] = new IRange[3];
rng[0][0] = model.AddLe(model.Sum(model.Prod(-1.0, x[0]),
model.Prod( 1.0, x[1]),
model.Prod( 1.0, x[2]),
model.Prod(10.0, x[3])),
20.0, "rng0");
rng[0][1] = model.AddLe(model.Sum(model.Prod( 1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod( 1.0, x[2])),
30.0, "rng1");
rng[0][2] = model.AddEq(model.Sum(model.Prod( 1.0, x[1]),
model.Prod(-3.5, x[3])),
0, "rng2");
// add special ordered set of type 1
INumVar[] sosvars = {x[2], x[3]};
double[] sosweights = {25.0, 18.0};
model.AddSOS1(sosvars, sosweights);
}
