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 (AMPL a = new AMPL())
{
if (solver != null)
{
a.SetOption("solver", solver);
}
// Interpret the two files
a.Read(System.IO.Path.Combine(modelDirectory, "diet/diet.mod"));
a.ReadData(System.IO.Path.Combine(modelDirectory, "diet/diet.dat"));
// Solve
a.Solve();
// Get objective entity by AMPL name
Objective totalcost = a.GetObjective("Total_Cost");
// Print it
Console.WriteLine("ObjectiveInstance is: {0}", totalcost.Value);
// Reassign data - specific instances
Parameter cost = a.GetParameter("cost");
cost.SetValues(ampl.Tuple.FromArray("BEEF", "HAM"),
new double[] { 5.01, 4.55 });
Console.WriteLine("Increased costs of beef and ham.");
// ReSolve and display objective
a.Solve();
Console.WriteLine("Objective value: {0}", totalcost.Value);
// Reassign data - all instances
cost.SetValues(new double[] { 3, 5, 5, 6, 1, 2, 5.01, 4.55 });
Console.WriteLine("Updated all costs");
// ReSolve and display objective
a.Solve();
Console.WriteLine("New objective value: {0}", totalcost.Value);
// Get the values of the variable Buy in a dataframe object
Variable Buy = a.GetVariable("Buy");
// Access a specific instance (method 1)
Console.WriteLine(Buy.Get("FISH").ToString());
// Access a specific instance (method 2)
Console.WriteLine(Buy[new ampl.Tuple("FISH")].ToString());
DataFrame df = Buy.GetValues();
// Print them
Console.WriteLine(df);
// Get the values of an expression into a DataFrame object
DataFrame df2 = a.GetData("{j in FOOD} 100*Buy[j]/Buy[j].ub");
// Print them
Console.WriteLine(df2);
}
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);
}
public static int Main(string[] args)
{
string modelDirectory = ((args != null) && (args.Length > 0)) ? args[0]
: "../../models";
modelDirectory += "/qpmv";
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 (AMPL ampl = new AMPL())
{
// Number of steps of the efficient frontier
const int steps = 10;
if (solver != null)
{
ampl.SetOption("solver", solver);
}
ampl.SetOption("reset_initial_guesses", true);
ampl.SetOption("send_statuses", false);
ampl.SetOption("Solver", "cplex");
// Load the AMPL model from file
ampl.Read(modelDirectory + "/qpmv.mod");
ampl.Read(modelDirectory + "/qpmvbit.run");
// set tables directory (parameter used in the script above)
ampl.GetParameter("data_dir").Set(modelDirectory);
// Read tables
ampl.ReadTable("assetstable");
ampl.ReadTable("astrets");
Variable portfolioReturn = ampl.GetVariable("portret");
Parameter averageReturn = ampl.GetParameter("averret");
Parameter targetReturn = ampl.GetParameter("targetret");
Objective variance = ampl.GetObjective("cst");
// Relax the integrality
ampl.SetOption("relax_integrality", true);
// Solve the problem
ampl.Solve();
// Calibrate the efficient frontier range
double minret = portfolioReturn.Value;
DataFrame values = averageReturn.GetValues();
DataFrame.Column col = values.GetColumn("averret");
double maxret = col.Max().Dbl;
double stepsize = (maxret - minret) / steps;
double[] returns = new double[steps];
double[] variances = new double[steps];
for (int i = 0; i < steps; i++)
{
Console.WriteLine(string.Format("Solving for return = {0}",
maxret - (i - 1) * stepsize));
// set target return to the desired point
targetReturn.Set(maxret - (i - 1) * stepsize);
ampl.Eval("let stockopall:={};let stockrun:=stockall;");
// Relax integrality
ampl.SetOption("relax_integrality", true);
ampl.Solve();
Console.WriteLine(string.Format("QP result = {0}",
variance.Value));
// Adjust included stocks
ampl.Eval("let stockrun:={i in stockrun:weights[i]>0};");
ampl.Eval("let stockopall:={i in stockrun:weights[i]>0.5};");
// set integrality back
ampl.SetOption("relax_integrality", false);
ampl.Solve();
Console.WriteLine(string.Format("QMIP result = {0}",
variance.Value));
// Store data of corrent frontier point
returns[i] = maxret - (i - 1) * stepsize;
variances[i] = variance.Value;
}
// Display efficient frontier points
Console.WriteLine(" RETURN VARIANCE");
for (int i = 0; i < steps; i++)
{
Console.WriteLine(string.Format("{0,10:0.00000} {1,10:0.00000}", returns[i], variances[i]));
}
}
return(0);
}
public void Run(int iModel, string sModelPath, string sDataPath)
{
ResultsWindow resWindow = new ResultsWindow();
// Create an AMPL instance
using (AMPL a = new AMPL())
{
DataFrame Gens;
DataFrame P;
a.SetOption("solver", "gurobi");
a.Read(sModelPath);
a.ReadData(sDataPath);
// Solve
a.Solve();
// Get objective entity by AMPL name
string sTotalCost = a.GetObjective("Total_Cost").Value.ToString("0.00");
int iNumGenUnits = Convert.ToInt16(a.GetSet("GENERATORS").Size.ToString());
if (iModel == CTUC_ES | iModel == CTUC_NON_MARKET_ES)
{
int iNumESUnits = a.GetSet("ENERGY_ST").Size;
}
switch (iModel)
{
case DTUC:
Gens = a.GetParameter("GenData").GetValues();
P = a.GetVariable("P").GetValues();
DataTable dtP = generate_DT_2D_table(P);
DataTable dtPRamp = calculate_ramping(dtP, true);
DataTable dtI = generate_DT_2D_table(a.GetVariable("I").GetValues());
DataTable dtLMP = generate_1D_table(a.GetConstraint("Power_Balance").GetValues());
resWindow.ProcessDTUCResults(dtP, dtI, dtLMP, sTotalCost, dtPRamp);
break;
case CTUC:
DataTable CTUC_dtP = array2d_to_datatable(
CT_P_Post_Mortem(Dataframe_to_3d_array(a.GetVariable("G_H").GetValues(), iNumGenUnits, 24, 4),
iNumGenUnits)
);
DataTable CTUC_dtLamda = array1d_to_datatable(
CT_Price_Post_Mortem(Dataframe_to_2d_array(a.GetConstraint("Load_Balance1").GetValues(), 24, 4, true),
Dataframe_to_2d_array(a.GetConstraint("Load_Balance2").GetValues(), 24, 4, true),
iNumGenUnits)
);
DataTable CTUC_dtPRamp = calculate_ramping1(
CT_P_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("G_H").GetValues(), iNumGenUnits, 24, 4),
iNumGenUnits)
);
DataTable CTUC_dtI = generate_DT_2D_table(a.GetVariable("I").GetValues());
resWindow.ProcessCTUCResults(CTUC_dtP, CTUC_dtPRamp, CTUC_dtLamda, sTotalCost, CTUC_dtI);
break;
case CTUC_ES:
double[,] arr_G_cont = CT_P_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("G_H").GetValues(), iNumGenUnits, 24, 4),
iNumGenUnits);
double[] arr_Lambda_cont = CT_Price_Post_Mortem(
Dataframe_to_2d_array(a.GetConstraint("Load_Balance1").GetValues(), 24, 4, true),
Dataframe_to_2d_array(a.GetConstraint("Load_Balance2").GetValues(), 24, 4, true),
iNumGenUnits);
double[,] arr_ES_G_cont = CT_ES_CH_DIS_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("D_H_S").GetValues(), iNumGenUnits, 24, 4),
iNumESUnits);
double[,] arr_ES_D_cont = CT_ES_CH_DIS_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("G_H_S").GetValues(), iNumGenUnits, 24, 4),
iNumESUnits);
double[,] arr_ES_E_cont = CT_ES_Energy_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("E_B_S").GetValues(), iNumGenUnits, 24, 5),
iNumESUnits);
double[,] arr_Gamma_E_cont = CT_ES_Gamma_Post_Mortem(
Dataframe_to_3d_array(a.GetConstraint("Integral1_INI").GetValues(), iNumGenUnits, 24, 5),
Dataframe_to_3d_array(a.GetConstraint("ES_minE").GetValues(), iNumGenUnits, 24, 5),
Dataframe_to_3d_array(a.GetConstraint("ES_maxE").GetValues(), iNumGenUnits, 24, 5),
iNumESUnits);
DataTable dtCTUC_ES_P = array2d_to_datatable(arr_G_cont);
DataTable dtCTUC_ES_Lamda = array1d_to_datatable(arr_Lambda_cont);
DataTable dtCTUC_ES_PRamp = calculate_ramping1(arr_G_cont);
DataTable dtCTUC_ES_I = generate_DT_2D_table(a.GetVariable("I").GetValues());
DataTable dtCTUC_ES_CHP = array2d_to_datatable(arr_ES_G_cont);
DataTable dtCTUC_ES_CHPR = calculate_ramping1(arr_ES_G_cont);
DataTable dtCTUC_ES_DISP = array2d_to_datatable(arr_ES_D_cont);
DataTable dtCTUC_ES_DISPR = calculate_ramping1(arr_ES_D_cont);
DataTable dtCTUC_ES_E = array2d_to_datatable(arr_ES_E_cont);
DataTable dtCTUC_ES_NISSE = array2d_to_datatable(arr_Gamma_E_cont);
resWindow.ProcessCTUCwithES_MarketResults(dtCTUC_ES_P,
dtCTUC_ES_I,
dtCTUC_ES_Lamda,
sTotalCost,
dtCTUC_ES_PRamp,
dtCTUC_ES_CHP,
dtCTUC_ES_CHPR,
dtCTUC_ES_DISP,
dtCTUC_ES_DISPR,
dtCTUC_ES_NISSE,
dtCTUC_ES_E);
break;
case CTUC_NON_MARKET_ES:
double[,] arr_G_cont_NM = CT_P_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("G_H").GetValues(), iNumGenUnits, 24, 4),
iNumGenUnits);
double[] arr_Lambda_cont_NM = CT_Price_Post_Mortem(
Dataframe_to_2d_array(a.GetConstraint("Load_Balance1").GetValues(), 24, 4, true),
Dataframe_to_2d_array(a.GetConstraint("Load_Balance2").GetValues(), 24, 4, true),
iNumGenUnits);
double[,] arr_ES_G_cont_NM = CT_ES_CH_DIS_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("D_H_S").GetValues(), iNumGenUnits, 24, 4),
iNumESUnits);
double[,] arr_ES_D_cont_NM = CT_ES_CH_DIS_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("G_H_S").GetValues(), iNumGenUnits, 24, 4),
iNumESUnits);
double[,] arr_ES_E_cont_NM = CT_ES_Energy_Post_Mortem(
Dataframe_to_3d_array(a.GetVariable("E_B_S").GetValues(), iNumGenUnits, 24, 5),
iNumESUnits);
double[,] arr_Gamma_E_cont_NM = CT_ES_Gamma_Post_Mortem(
Dataframe_to_3d_array(a.GetConstraint("Integral1_INI").GetValues(), iNumGenUnits, 24, 5),
Dataframe_to_3d_array(a.GetConstraint("ES_minE").GetValues(), iNumGenUnits, 24, 5),
Dataframe_to_3d_array(a.GetConstraint("ES_maxE").GetValues(), iNumGenUnits, 24, 5),
iNumESUnits);
DataTable dtCTUC_ES_NM_P = array2d_to_datatable(arr_G_cont_NM);
DataTable dtCTUC_ES_NM_Lamda = array1d_to_datatable(arr_Lambda_cont_NM);
DataTable dtCTUC_ES_NM_PRamp = calculate_ramping1(arr_G_cont_NM);
DataTable dtCTUC_ES_NM_I = generate_DT_2D_table(a.GetVariable("I").GetValues());
DataTable dtCTUC_ES_NM_CHP = array2d_to_datatable(arr_ES_G_cont_NM);
DataTable dtCTUC_ES_NM_CHPR = calculate_ramping1(arr_ES_G_cont_NM);
DataTable dtCTUC_ES_NM_DISP = array2d_to_datatable(arr_ES_D_cont_NM);
DataTable dtCTUC_ES_NM_DISPR = calculate_ramping1(arr_ES_D_cont_NM);
DataTable dtCTUC_ES_NM_E = array2d_to_datatable(arr_ES_E_cont_NM);
DataTable dtCTUC_ES_NM_NISSE = array2d_to_datatable(arr_Gamma_E_cont_NM);
resWindow.ProcessCTUCwithES_MarketResults(dtCTUC_ES_NM_P,
dtCTUC_ES_NM_I,
dtCTUC_ES_NM_Lamda,
sTotalCost,
dtCTUC_ES_NM_PRamp,
dtCTUC_ES_NM_CHP,
dtCTUC_ES_NM_CHPR,
dtCTUC_ES_NM_DISP,
dtCTUC_ES_NM_DISPR,
dtCTUC_ES_NM_NISSE,
dtCTUC_ES_NM_E);
break;
default:
P = a.GetVariable("P").GetValues();
break;
}
}
}
/// <summary>
/// This example shows a simple AMPL iterative procedure implemented in AMPL.
/// Must be executed with a solver supporting the suffix dunbdd
/// </summary>
/// <param name="args">
/// The first string, if present, should point to the models directory
/// </param>
public static int Main(string[] args)
{
string modelDirectory = ((args != null) && (args.Length > 0)) ? args[0]
: "../../models";
/*
* // 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())
{
// Must be solved with a solver supporting the suffix dunbdd
ampl.SetOption("solver", "cplex");
modelDirectory += "/locationtransportation";
ampl.SetOption("presolve", false);
ampl.SetOption("omit_zero_rows", true);
// Read the model.
ampl.Read(modelDirectory + "/trnloc2.mod");
ampl.ReadData(modelDirectory + "/trnloc.dat"); // TODO: set data
// programmatically
// Get references to AMPL's model entities for easy access.
Objective shipCost = ampl.GetObjective("Ship_Cost");
Variable maxShipCost = ampl.GetVariable("Max_Ship_Cost");
Variable buildVar = ampl.GetVariable("Build");
Constraint supply = ampl.GetConstraint("Supply");
Constraint demand = ampl.GetConstraint("Demand");
Parameter numCutParam = ampl.GetParameter("nCUT");
Parameter cutType = ampl.GetParameter("cut_type");
Parameter buildParam = ampl.GetParameter("build");
Parameter supplyPrice = ampl.GetParameter("supply_price");
Parameter demandPrice = ampl.GetParameter("demand_price");
numCutParam.Set(0);
maxShipCost.Value = 0;
double[] initialBuild = new double[ampl.GetSet("ORIG").Size];
for (int i = 0; i < initialBuild.Length; i++)
{
initialBuild[i] = 1;
}
buildParam.SetValues(initialBuild);
int numCuts;
for (numCuts = 1; ; numCuts++)
{
Console.WriteLine("Iteration {0}", numCuts);
ampl.Display("build");
// Solve the subproblem.
ampl.Eval("solve Sub;");
string result = shipCost.Result;
if (result.Equals("infeasible"))
{
// Add a feasibility cut.
numCutParam.Set(numCuts);
cutType.Set(new ampl.Tuple(numCuts), "ray");
DataFrame dunbdd = supply.GetValues("dunbdd");
foreach (var row in dunbdd)
{
supplyPrice.Set(new ampl.Tuple(row[0], numCuts), row[1].Dbl);
}
dunbdd = demand.GetValues("dunbdd");
foreach (var row in dunbdd)
{
demandPrice.Set(new ampl.Tuple(row[0], numCuts), row[1].Dbl);
}
}
else if (shipCost.Value > maxShipCost.Value + 0.00001)
{
// Add an optimality cut.
numCutParam.Set(numCuts);
cutType.Set(new ampl.Tuple(numCuts), "point");
ampl.Display("Ship");
DataFrame duals = supply.GetValues();
foreach (var row in duals)
{
supplyPrice.Set(new ampl.Tuple(row[0], numCuts), row[1].Dbl);
}
duals = demand.GetValues();
foreach (var row in duals)
{
demandPrice.Set(new ampl.Tuple(row[0], numCuts), row[1].Dbl);
}
}
else
{
break;
}
// Re-solve the master problem.
Console.WriteLine("RE-SOLVING MASTER PROBLEM");
ampl.Eval("solve Master;");
// Copy the data from the Build variable used in the master problem
// to the build parameter used in the subproblem.
DataFrame data = buildVar.GetValues();
buildParam.SetValues(data);
}
ampl.Display("Ship");
}
return(0);
}