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)
{
/*
* // 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())
{
ampl.Eval("var x{1..3};");
ampl.Eval("maximize z: sum{i in 1..3} x[i];");
// *** Output redirection *** Enable output redirection
ampl.EnableOutputRouting();
// Assign handler: Method 1: using method
ampl.Output += HandleOutput;
ampl.Eval("display x;");
ampl.Eval("let {i in 1..3} x[i] := i;");
ampl.Eval("display x;");
// Unassign output handler
ampl.Output -= HandleOutput;
// print all outputs
foreach (var t in outputs)
{
Console.Write("{0} - Kind: {1} - Msg:\n{2}", t.Date,
t.Kind, t.Msg);
}
// Method 2: Using lambda expression
ampl.Output += (kind, message) => Console.Write("Got AMPL message:\n{0}\n", message);
// Test it
ampl.Eval("display x,x;");
// *** Error redirection *** Enable error and warnings redirection
ampl.EnableErrorAndWarningRouting();
// Register handlers
ampl.Error += HandleError;
ampl.Warning += HandleWarning;
// Normally throws exception, will just be printed on screen
ampl.Eval("var x;");
// Create an obvious infeasibility
ampl.Eval("c1: x[1]>=1; c2: x[1]<=0;");
// Solve the model, issuing a warning
ampl.Solve();
}
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);
}
/// <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);
}