// End Initialize() in MDSLinearAlgebra
// MaxIndicator = 0 Find Maximum Eigenvalue of Matrix
// MaxIndicator = 1 Find Maximum Eigenvalue of (Maximizr - Matrix)
public static int PowerIterate(Desertwind Solution, int MaxIndicator, double Maximizer,
out double PowerEigenvalue)
{
if (DistributedNewIteratedVector == null)
{
Initialize();
}
Hotsun.UseDiagonalScaling = true;
Hotsun.AddMarquardtQDynamically = false;
// Set up Initial Power Vectors
SetInitialPowerVector(DistributedNewIteratedVector);
double AdotA = SALSABLAS.VectorScalarProduct(DistributedNewIteratedVector, DistributedNewIteratedVector);
int somethingtodo = 0;
int PowerIterationCount = 0;
PowerEigenvalue = -1.0;
while (true)
{
// Iterate over Power Multiplications by Chisq Matrix
// Normalize Current A and move from New to Old
double OldNorm = 1.0 / Math.Sqrt(AdotA);
SALSABLAS.LinearCombineVector(DistributedOldIteratedVector, OldNorm,
DistributedNewIteratedVector, 0.0, DistributedNewIteratedVector);
// Make a Global Vector of DistributedOldIteratedVector
ManxcatCentral.MakeVectorGlobal(DistributedOldIteratedVector, GlobalOldIteratedVector);
// Form Chisq Matrix Product with Old Vector
if (Hotsun.FullSecondDerivative)
{
UserRoutines.GlobalMatrixVectorProduct(DistributedNewIteratedVector, Solution, true,
Hotsun.GlobalParameter, GlobalOldIteratedVector);
}
else
{
UserRoutines.GlobalMatrixVectorProduct(DistributedNewIteratedVector, Solution, false,
Hotsun.GlobalParameter, GlobalOldIteratedVector);
}
// Correct case MaxIndicator = 1
if (MaxIndicator > 0)
{
SALSABLAS.LinearCombineVector(DistributedNewIteratedVector, -1.0, DistributedNewIteratedVector,
Maximizer, DistributedOldIteratedVector);
}
SALSABLAS.LinearCombineVector(DistributedNewIteratedVector, 1.0, DistributedNewIteratedVector,
Hotsun.addonforQcomputation, DistributedOldIteratedVector);
// Form Scalar Products
double NewEigenvalue = SALSABLAS.VectorScalarProduct(DistributedNewIteratedVector,
DistributedOldIteratedVector);
AdotA = SALSABLAS.VectorScalarProduct(DistributedNewIteratedVector, DistributedNewIteratedVector);
++PowerIterationCount;
somethingtodo = -1;
if (SALSAUtility.MPI_Rank == 0)
{
if (PowerIterationCount > 10 && (NewEigenvalue > 0.0))
{
// Arbitary criteria for starting +tests
somethingtodo = 0;
double scaleit = 1.0;
if (MaxIndicator > 0)
{
scaleit = Hotsun.extraprecision;
}
if (Math.Abs(NewEigenvalue - PowerEigenvalue) > PowerEigenvalue * scaleit * Hotsun.eigenvaluechange)
{
++somethingtodo;
}
double delta = AdotA - NewEigenvalue * NewEigenvalue; // (Ax- Eigenvalue*Axold)**2
if (Math.Abs(delta) > NewEigenvalue * NewEigenvalue * scaleit * Hotsun.eigenvectorchange)
{
++somethingtodo;
}
}
}
PowerEigenvalue = NewEigenvalue;
if (SALSAUtility.MPI_Size > 1)
{
SALSAUtility.StartSubTimer(SALSAUtility.MPIBROADCASTTiming);
SALSAUtility.MPI_communicator.Broadcast(ref somethingtodo, 0);
SALSAUtility.StopSubTimer(SALSAUtility.MPIBROADCASTTiming);
}
if (PowerIterationCount >= Hotsun.PowerIterationLimit)
{
somethingtodo = -2;
break;
}
if (somethingtodo == 0)
{
somethingtodo = PowerIterationCount;
break;
}
} // End while over PowerIterationCounts
return(somethingtodo);
}
// End PowerIterate()
// Solve for Distributed Answer = (Matrix-1) First
// Note RealMatrixSize takes account of forced zero parameters which does not crop up in explicit
// algorithm as implemented in Matrix Vector Multiplier
public static bool ConjugateGradientSolver(double[][] Answer, Desertwind Solution, bool useexact,
double[][] GlobalxVector, double[][] DistributedRHS,
ref int NumberofIterations, int RealMatrixSize, double LimitonNormofR)
{
bool matrixsuccess = true;
// Initialize
SALSABLAS.zrword(Answer); // Zero Solution x called xshift in Manxcat and stored in Answer
SALSABLAS.CopyVector(DistributedCGVector_R, DistributedRHS, 0, SALSAUtility.PointCount_Process);
// Set R(0) = RHS
double InitialRNorm = SALSABLAS.VectorScalarProduct(DistributedCGVector_R, DistributedCGVector_R);
double RNormTest = InitialRNorm * LimitonNormofR; // Limit for Test on Iteration of Norm of R
var SaveRNorm = new double[RealMatrixSize + 1];
SaveRNorm[0] = InitialRNorm;
int CountSteps = 0;
double lastrho = 1.0;
double currentrho = 1.0;
// Loop over Conjugate Gradient Steps
while (true)
{
// Set value of rho
lastrho = currentrho;
currentrho = SALSABLAS.VectorScalarProduct(DistributedCGVector_R, DistributedCGVector_R);
// Set Vector P
++CountSteps;
if (CountSteps == 1)
{
SALSABLAS.CopyVector(DistributedCGVector_P, DistributedCGVector_R, 0,
SALSAUtility.PointCount_Process);
}
else
{
SALSABLAS.LinearCombineVector(DistributedCGVector_P, currentrho / lastrho, DistributedCGVector_P,
1.0, DistributedCGVector_R);
}
// Make a Global Vector of DistributedCGVector_P
ManxcatCentral.MakeVectorGlobal(DistributedCGVector_P, GlobalCGVector_P);
// Distributed Q = Matrix . Global P
UserRoutines.GlobalMatrixVectorProduct(DistributedCGVector_Q, Solution, useexact,
Hotsun.GlobalParameter, GlobalCGVector_P);
// New Answer is Old answer + (Current Rho / (P dot Q)) Vector P
double PdotQ = SALSABLAS.VectorScalarProduct(DistributedCGVector_P, DistributedCGVector_Q);
double alpha = currentrho / PdotQ;
SALSABLAS.LinearCombineVector(Answer, alpha, DistributedCGVector_P, 1.0, Answer);
// New residual R = Old Residual - (Current Rho / (P dot Q)) Vector Q
SALSABLAS.LinearCombineVector(DistributedCGVector_R, -alpha, DistributedCGVector_Q, 1.0,
DistributedCGVector_R);
// See if we can or should End
double CurrentRNorm = SALSABLAS.VectorScalarProduct(DistributedCGVector_R, DistributedCGVector_R);
SaveRNorm[CountSteps] = CurrentRNorm;
bool TestRNorm = CurrentRNorm <= RNormTest;
SALSAUtility.SynchronizeMPIvariable(ref TestRNorm);
if (TestRNorm)
{
matrixsuccess = true;
break;
}
// Singular Matrix
if (CountSteps >= RealMatrixSize)
{
matrixsuccess = false;
ManxcatCentral.MakeVectorGlobal(DistributedCGVector_R, GlobalCGVector_R);
if (SALSAUtility.MPI_Rank == 0)
{
SALSAUtility.SALSAPrint(0, " CG Failure after " + RealMatrixSize.ToString() + " Steps");
string ListofNorms = "";
int Normindex = CountSteps;
for (int inorm = 0; inorm < 10; inorm++)
{
ListofNorms += " " + SaveRNorm[Normindex].ToString("E4");
--Normindex;
if (Normindex < 0)
{
break;
}
}
SALSAUtility.SALSAPrint(0, "Last 10 Norms " + ListofNorms);
string fname = ManxcatCentral.ResultDirectoryName + "\\BadCGVector" +
Hotsun.TotalCGFailures.ToString();
var sw = new StreamWriter(fname, false, Encoding.UTF8);
double fractionnorm = 1.0 / Math.Sqrt(CurrentRNorm);
try
{
for (int GlobalPointIndex = 0;
GlobalPointIndex < SALSAUtility.PointCount_Global;
GlobalPointIndex++)
{
string Coordinates = "";
int UsedPointIndex = SALSAUtility.NaivetoActualUsedOrder[GlobalPointIndex];
double pointsize = 0.0;
for (int LocalVectorIndex = 0;
LocalVectorIndex < Hotsun.ParameterVectorDimension;
LocalVectorIndex++)
{
Coordinates += GlobalCGVector_R[UsedPointIndex][LocalVectorIndex].ToString("E4") +
"\t";
pointsize += GlobalCGVector_R[UsedPointIndex][LocalVectorIndex] *
GlobalCGVector_R[UsedPointIndex][LocalVectorIndex];
}
pointsize = Math.Sqrt(pointsize) * fractionnorm;
sw.WriteLine(
String.Format((GlobalPointIndex).ToString() + "\t" + Coordinates + " " +
pointsize.ToString("E4")));
}
sw.Flush();
sw.Close();
}
catch (Exception e)
{
Console.WriteLine("Failed writing data in CG Solver " + e);
throw (e);
}
}
break;
}
} // End Loop over Conjugate Gradient Steps
NumberofIterations = CountSteps;
return(matrixsuccess);
}