/// <summary>
/// Finds the splitup vector of the Target program using the programs of the Testbed as generators. Solves the Ax=Y equation using the simplex. Also finds the error vector E, defined as E=|Ax-Y|.
/// </summary>
/// <returns>true if the simplex found a solution</returns>
public bool FindSplitup(bool cone)
{
// do we have at least 1 program in the testbed?
if (Testbed.Length < 1)
return false;
int resources = Testbed[0].Measures.Length;
// does every program have the same number of measured resources?
foreach (var item in Testbed)
{
if (item.Measures == null || item.Measures.Length != resources)
return false;
}
// check target program
if (Target == null || Target.Measures == null)
return false;
if (Target.Measures.Length != resources)
return false;
// input looks fine.. let' s build the LP problem
// how many columns?
// 1 column for every program
// + 2 for every resource
// this is a trick to transform the absolute value (non linear function) into a linear function,
// so we can apply the simplex, see APPLIED MATHEMATICAL PROGRAMMING USING ALGEBRAIC SYSTEMS, CHAPTER IX: Linear Programming Modeling: non linearities and approximation, B. McCarl and T. Spreen
// URL: http://chentserver.uwaterloo.ca/aelkamel/che720/che725-process-optimization/GAMS-tutorials/Bruce/thebook.pdf
int programs = Testbed.Length;
int errorVariables = resources * 2;
int columns = programs + errorVariables;
// Normalize testbed and target
Program[] NormalizedTestbed = new Program[Testbed.Length];
Program NormTarget = new Program();
NormTarget.Measures = new double[resources];
for (int i = 0; i < Testbed.Length; i++)
{
NormalizedTestbed[i] = new Program();
NormalizedTestbed[i].Measures = new double[resources];
}
for (int i = 0; i < resources; i++)
{
double max = 0.0000001;
for (int j = 0; j < Testbed.Length; j++)
{
if (Testbed[j].Measures[i] > max)
max = Testbed[j].Measures[i];
}
for (int j = 0; j < Testbed.Length; j++)
{
NormalizedTestbed[j].Measures[i] = Testbed[j].Measures[i] / max;
}
NormTarget.Measures[i] = Target.Measures[i] / max;
}
List<int> ia = new List<int>();
List<int> ja = new List<int>();
List<double> ar = new List<double>();
// glpk counts from 1 to n, not from 0 to n-1.. silly boy
ia.Add(0); ja.Add(0); ar.Add(0);
for (int column = 0; column < Testbed.Length; column++)
{
for (int row = 0; row < resources; row++)
{
// glpk counts from 1 to n, not from 0 to n-1.. silly boy
ia.Add(row + 1);
ja.Add(column + 1);
ar.Add(NormalizedTestbed[column].Measures[row]);
}
}
// now add epsilon+ and epsilon-
for (int i = 0; i < resources; i++)
{
// epsilon+
ia.Add(i + 1);
ja.Add(programs + 2*i + 1);
ar.Add(1);
// epsilon-
ia.Add(i + 1);
ja.Add(programs + 2 * i + 1 + 1);
ar.Add(-1);
}
// the coefficient array, this does not start from 1
List<double> coeff = new List<double>();
for (int i = 0; i < Testbed.Length; i++)
{
coeff.Add(0);
}
for (int i = 0; i < errorVariables; i++)
{
coeff.Add(1);
}
// the Y array
List<Proxy.bound> rowBounds = new List<Proxy.bound>();
foreach (var item in NormTarget.Measures)
{
Proxy.bound b = new Proxy.bound();
b.BoundType = BOUNDSTYPE.Fixed;
b.lower = item;
b.upper = item;
rowBounds.Add(b);
}
List<Proxy.bound> colBounds = new List<Proxy.bound>();
int firstColumnBound = cone ? 0 : programs;
for (int i = firstColumnBound; i < columns; i++)
{
Proxy.bound b = new Proxy.bound();
b.BoundType = BOUNDSTYPE.Lower;
b.lower = 0;
b.upper = 0;
colBounds.Add(b);
}
Proxy p = new Proxy();
Proxy.result result = p.Simplex(
columns,
resources,
OptimisationDirection.MINIMISE,
rowBounds.ToArray(),
colBounds.ToArray(),
ia.ToArray(),
ja.ToArray(),
ar.ToArray(),
coeff.ToArray());
List<double> splitup = new List<double>();
for (int i = 0; i < Testbed.Length; i++)
{
splitup.Add(result.Columns[i]);
}
Splitup = splitup.ToArray();
TotalError = result.ObjResult;
List<double> errors = new List<double>();
for (int i = 0; i < resources; i++)
{
double epsilon_plus = result.Columns[programs + i * 2];
double epsilon_minus = result.Columns[programs + i * 2 + 1];
errors.Add(epsilon_plus - epsilon_minus);
}
Errors = errors.ToArray();
return true;
}
/// <summary>
/// Finds the splitup vector of the Target program using the programs of the Testbed as generators. Solves the Ax=Y equation using the simplex. Also finds the error vector E, defined as E=|Ax-Y|.
/// </summary>
/// <returns>true if the simplex found a solution</returns>
public bool FindSplitup(bool cone)
{
// do we have at least 1 program in the testbed?
if (Testbed.Length < 1)
{
return(false);
}
int resources = Testbed[0].Measures.Length;
// does every program have the same number of measured resources?
foreach (var item in Testbed)
{
if (item.Measures == null || item.Measures.Length != resources)
{
return(false);
}
}
// check target program
if (Target == null || Target.Measures == null)
{
return(false);
}
if (Target.Measures.Length != resources)
{
return(false);
}
// input looks fine.. let' s build the LP problem
// how many columns?
// 1 column for every program
// + 2 for every resource
// this is a trick to transform the absolute value (non linear function) into a linear function,
// so we can apply the simplex, see APPLIED MATHEMATICAL PROGRAMMING USING ALGEBRAIC SYSTEMS, CHAPTER IX: Linear Programming Modeling: non linearities and approximation, B. McCarl and T. Spreen
// URL: http://chentserver.uwaterloo.ca/aelkamel/che720/che725-process-optimization/GAMS-tutorials/Bruce/thebook.pdf
int programs = Testbed.Length;
int errorVariables = resources * 2;
int columns = programs + errorVariables;
// Normalize testbed and target
Program[] NormalizedTestbed = new Program[Testbed.Length];
Program NormTarget = new Program();
NormTarget.Measures = new double[resources];
for (int i = 0; i < Testbed.Length; i++)
{
NormalizedTestbed[i] = new Program();
NormalizedTestbed[i].Measures = new double[resources];
}
for (int i = 0; i < resources; i++)
{
double max = 0.0000001;
for (int j = 0; j < Testbed.Length; j++)
{
if (Testbed[j].Measures[i] > max)
{
max = Testbed[j].Measures[i];
}
}
for (int j = 0; j < Testbed.Length; j++)
{
NormalizedTestbed[j].Measures[i] = Testbed[j].Measures[i] / max;
}
NormTarget.Measures[i] = Target.Measures[i] / max;
}
List <int> ia = new List <int>();
List <int> ja = new List <int>();
List <double> ar = new List <double>();
// glpk counts from 1 to n, not from 0 to n-1.. silly boy
ia.Add(0); ja.Add(0); ar.Add(0);
for (int column = 0; column < Testbed.Length; column++)
{
for (int row = 0; row < resources; row++)
{
// glpk counts from 1 to n, not from 0 to n-1.. silly boy
ia.Add(row + 1);
ja.Add(column + 1);
ar.Add(NormalizedTestbed[column].Measures[row]);
}
}
// now add epsilon+ and epsilon-
for (int i = 0; i < resources; i++)
{
// epsilon+
ia.Add(i + 1);
ja.Add(programs + 2 * i + 1);
ar.Add(1);
// epsilon-
ia.Add(i + 1);
ja.Add(programs + 2 * i + 1 + 1);
ar.Add(-1);
}
// the coefficient array, this does not start from 1
List <double> coeff = new List <double>();
for (int i = 0; i < Testbed.Length; i++)
{
coeff.Add(0);
}
for (int i = 0; i < errorVariables; i++)
{
coeff.Add(1);
}
// the Y array
List <Proxy.bound> rowBounds = new List <Proxy.bound>();
foreach (var item in NormTarget.Measures)
{
Proxy.bound b = new Proxy.bound();
b.BoundType = BOUNDSTYPE.Fixed;
b.lower = item;
b.upper = item;
rowBounds.Add(b);
}
List <Proxy.bound> colBounds = new List <Proxy.bound>();
int firstColumnBound = cone ? 0 : programs;
for (int i = firstColumnBound; i < columns; i++)
{
Proxy.bound b = new Proxy.bound();
b.BoundType = BOUNDSTYPE.Lower;
b.lower = 0;
b.upper = 0;
colBounds.Add(b);
}
Proxy p = new Proxy();
Proxy.result result = p.Simplex(
columns,
resources,
OptimisationDirection.MINIMISE,
rowBounds.ToArray(),
colBounds.ToArray(),
ia.ToArray(),
ja.ToArray(),
ar.ToArray(),
coeff.ToArray());
List <double> splitup = new List <double>();
for (int i = 0; i < Testbed.Length; i++)
{
splitup.Add(result.Columns[i]);
}
Splitup = splitup.ToArray();
TotalError = result.ObjResult;
List <double> errors = new List <double>();
for (int i = 0; i < resources; i++)
{
double epsilon_plus = result.Columns[programs + i * 2];
double epsilon_minus = result.Columns[programs + i * 2 + 1];
errors.Add(epsilon_plus - epsilon_minus);
}
Errors = errors.ToArray();
return(true);
}
