private void readInProblemDefinition(ProblemDefinition pd)
{
foreach (var f0 in pd.f)
{
Add(f0);
}
foreach (var ineq in pd.g)
{
Add(ineq);
}
foreach (var eq in pd.h)
{
Add(eq);
}
NumConvergeCriteriaNeeded = pd.NumConvergeCriteriaNeeded;
if (pd.ConvergenceMethods != null)
{
foreach (var cM in pd.ConvergenceMethods)
{
Add(cM);
}
}
if (pd.SpaceDescriptor != null)
{
Add(pd.SpaceDescriptor);
}
if ((pd.xStart != null) && (pd.xStart.GetLength(0) > 0))
{
xStart = (double[])pd.xStart.Clone();
n = xStart.GetLength(0);
}
}
/// <summary>
/// Creates a problem definition object from the details loaded in the
/// optimization routine. For use in saving the data to XML, etc.
/// </summary>
/// <returns>the problem definition</returns>
internal ProblemDefinition createProblemDefinition()
{
var pd = new ProblemDefinition
{
ConvergenceMethods = ConvergenceMethods,
xStart = xStart,
NumConvergeCriteriaNeeded = NumConvergeCriteriaNeeded,
};
foreach (IObjectiveFunction f0 in f)
{
pd.f.Add(f0);
}
foreach (IEquality eq in h)
{
pd.h.Add(eq);
}
foreach (IInequality ineq in g)
{
if (ineq.GetType() == typeof(lessThanConstant))
{
var ub = ((lessThanConstant)ineq).constant;
var varIndex = ((lessThanConstant)ineq).index;
pd.SpaceDescriptor[varIndex].UpperBound = ub;
}
else if (ineq.GetType() == typeof(greaterThanConstant))
{
var lb = ((greaterThanConstant)ineq).constant;
var varIndex = ((greaterThanConstant)ineq).index;
pd.SpaceDescriptor[varIndex].UpperBound = lb;
}
else
{
pd.g.Add(ineq);
}
}
return(pd);
}
