public static int Main(string[] args)
{
/*
* // If the AMPL installation directory is not in the system search path:
* ampl.Environment env = new ampl.Environment(
* "full path to the AMPL installation directory");
* // Create an AMPL instance
* using (AMPL a = new AMPL(env)) {}
*/
// Create an AMPL instance
using (AMPL ampl = new AMPL())
{
ampl.Eval("set CITIES; set LINKS within (CITIES cross CITIES);");
ampl.Eval("param cost {LINKS} >= 0; param capacity {LINKS} >= 0;");
ampl.Eval("data; set CITIES := PITT NE SE BOS EWR BWI ATL MCO;");
double[] cost = { 2.5, 3.5, 1.7, 0.7, 1.3, 1.3, 0.8, 0.2, 2.1 };
double[] capacity = { 250, 250, 100, 100, 100, 100, 100, 100, 100 };
string[] LinksFrom = { "PITT", "PITT", "NE", "NE", "NE", "SE", "SE", "SE", "SE" };
string[] LinksTo = { "NE", "SE", "BOS", "EWR", "BWI", "EWR", "BWI", "ATL", "MCO" };
DataFrame df = new DataFrame(2, "LINKSFrom", "LINKSTo", "cost", "capacity");
df.SetColumn("LINKSFrom", LinksFrom);
df.SetColumn("LINKSTo", LinksTo);
df.SetColumn("cost", cost);
df.SetColumn("capacity", capacity);
Console.WriteLine(df.ToString());
ampl.SetData(df, "LINKS");
}
return(0);
}
public static int Main(string[] args)
{
string modelDirectory = ((args != null) && (args.Length > 0)) ? args[0]
: "../../models";
string solver = ((args != null) && (args.Length > 1)) ? args[1] : null;
// Create first dataframe (for data indexed over NUTR) Add data row by row
DataFrame df1 = new DataFrame(1, "NUTR", "n_min", "n_max");
df1.AddRow("A", 700, 20000);
df1.AddRow("B1", 700, 20000);
df1.AddRow("B2", 700, 20000);
df1.AddRow("C", 700, 20000);
df1.AddRow("CAL", 16000, 24000);
df1.AddRow("NA", 0.0, 50000);
// Create second dataframe (for data indexed over FOOD) Add column by column
DataFrame df2 = new DataFrame(1, "FOOD");
string[] foods = { "BEEF", "CHK", "FISH", "HAM",
"MCH", "MTL", "SPG", "TUR" };
df2.SetColumn("FOOD", foods);
double[] contents = new double[8];
for (int j = 0; j < 8; j++)
{
contents[j] = 2;
}
df2.AddColumn("f_min", contents);
for (int j = 0; j < 8; j++)
{
contents[j] = 10;
}
df2.AddColumn("f_max", contents);
double[] costs = { 3.19, 2.59, 2.29, 2.89, 1.89,
1.99, 1.99, 2.49 };
df2.AddColumn("cost", costs);
// Create third dataframe, to assign data to the AMPL entity param amt{NUTR, FOOD};
DataFrame df3 = new DataFrame(2, "NUTR", "FOOD");
// Populate the set columns
string[] nutrWithMultiplicity = new string[48];
string[] foodWithMultiplicity = new string[48];
int i = 0;
for (int n = 0; n < 6; n++)
{
for (int f = 0; f < 8; f++)
{
nutrWithMultiplicity[i] = df1.GetRowByIndex(n)[0].Str;
foodWithMultiplicity[i++] = foods[f];
}
}
df3.SetColumn("NUTR", nutrWithMultiplicity);
df3.SetColumn("FOOD", foodWithMultiplicity);
// Populate with all these values
double[] values = { 60, 8, 8, 40, 15, 70, 25, 60, 10, 20, 15,
35, 15, 15, 25, 15, 15, 20, 10, 10, 15, 15, 15, 10, 20, 0, 10,
40, 35, 30, 50, 20, 295, 770, 440, 430, 315, 400, 370, 450,
968, 2180, 945, 278, 1182, 896, 1329, 1397 };
df3.AddColumn("amt", values);
// Create an AMPL instance
using (AMPL ampl = new AMPL())
{
if (solver != null)
{
ampl.SetOption("solver", solver);
}
// Read model only
ampl.Read(modelDirectory + "/diet/diet.mod");
// Assign data to NUTR, n_min and n_max
ampl.SetData(df1, "NUTR");
// Assign data to FOOD, f_min, f_max and cost
ampl.SetData(df2, "FOOD");
// Assign data to amt
ampl.SetData(df3);
// Solve the model
ampl.Solve();
// Print out the result
Console.Write("Objective function value: {0}\n",
ampl.GetObjective("Total_Cost").Value);
// Get the values of the variable Buy in a dataframe
DataFrame results = ampl.GetVariable("Buy").GetValues();
// Print
Console.WriteLine(results.ToString());
}
return(0);
}
public static int Main(string[] args)
{
string modelDirectory = ((args != null) && (args.Length > 0)) ? args[0]
: "../../models";
string solver = ((args != null) && (args.Length > 1)) ? args[1] : null;
/*
* // If the AMPL installation directory is not in the system search path:
* ampl.Environment env = new ampl.Environment(
* "full path to the AMPL installation directory");
* // Create an AMPL instance
* using (AMPL a = new AMPL(env)) {}
*/
// Create an AMPL instance
using (var ampl = new AMPL())
{
if (solver != null)
{
ampl.SetOption("solver", solver);
}
// Read the model file
ampl.Read(modelDirectory + "/diet/diet.mod");
string[] foods = { "BEEF", "CHK", "FISH", "HAM",
"MCH", "MTL", "SPG", "TUR" };
double[] costs = { 3.59, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49 };
double[] fmin = { 2, 2, 2, 2, 2, 2, 2, 2 };
double[] fmax = { 10, 10, 10, 10, 10, 10, 10, 10 };
DataFrame df = new DataFrame(1, "FOOD");
df.SetColumn("FOOD", foods);
df.AddColumn("cost", costs);
df.AddColumn("f_min", fmin);
df.AddColumn("f_max", fmax);
ampl.SetData(df, "FOOD");
string[] nutrients = { "A", "C", "B1", "B2", "NA", "CAL" };
double[] nmin = { 700, 700, 700, 700, 0, 16000 };
double[] nmax = { 20000, 20000, 20000, 20000, 50000, 24000 };
df = new DataFrame(1, "NUTR");
df.SetColumn("NUTR", nutrients);
df.AddColumn("n_min", nmin);
df.AddColumn("n_max", nmax);
ampl.SetData(df, "NUTR");
double[,] amounts =
{
{ 60, 8, 8, 40, 15, 70, 25, 60 },
{ 20, 0, 10, 40, 35, 30, 50, 20 },
{ 10, 20, 15, 35, 15, 15, 25, 15 },
{ 15, 20, 10, 10, 15, 15, 15, 10 },
{ 928, 2180, 945, 278, 1182, 896, 1329, 1397 },
{ 295, 770, 440, 430, 315, 400, 379, 450 }
};
df = new DataFrame(2, "NUTR", "FOOD", "amt");
df.SetMatrix(nutrients, foods, amounts);
ampl.SetData(df);
ampl.Solve();
Console.WriteLine(string.Format("Objective: {0}",
ampl.GetObjective("Total_Cost").Value));
}
return(0);
}