public bool HasConverged(int currentIteration, double currentObjectiveFunction, IVectorView nextDesignVariables)
{
bool result;
if (currentIteration == 0)
{
result = false;
}
else
{
result = nextDesignVariables.Subtract(currentDesignVariables).MaxAbsolute() <= changeTolerance;
}
currentDesignVariables = nextDesignVariables.Copy(); //TODO: Perhaps this copy can be avoided
return(result);
}
public override IterativeStatistics Solve(ILinearTransformation matrix, IPreconditioner preconditioner, IVectorView rhs,
IVector solution, bool initialGuessIsZero, Func <IVector> zeroVectorInitializer)
{
//TODO: find a better way to handle optimizations for the case x0=0, than using an initialGuessIsZero flag
Preconditions.CheckMultiplicationDimensions(matrix.NumColumns, solution.Length);
Preconditions.CheckSystemSolutionDimensions(matrix.NumRows, rhs.Length);
this.Matrix = matrix;
this.Preconditioner = preconditioner;
this.Rhs = rhs;
// Initial solution and rhs (r = b - A * x)
this.solution = solution;
if (ReorthoCache.Directions.Count > 0)
{
if (!initialGuessIsZero)
{
solution.Clear();
}
CalculateInitialSolutionFromStoredDirections(rhs, solution);
residual = ExactResidual.Calculate(matrix, rhs, solution);
}
else // preferably call base method
{
// r = b - A * x
if (initialGuessIsZero)
{
residual = rhs.Copy();
}
else
{
residual = ExactResidual.Calculate(matrix, rhs, solution);
}
}
// Initialize vectors
//TODO: Pehaps I can just clear them from previous iterations
precondResidual = zeroVectorInitializer();
direction = zeroVectorInitializer();
matrixTimesDirection = zeroVectorInitializer();
int maxIterations = MaxIterationsProvider.GetMaxIterations(matrix.NumColumns);
return(SolveInternal(maxIterations, zeroVectorInitializer));
}
/// <summary>
/// Solves the linear system A * x = b, where A = <paramref name="matrix"/> and b = <paramref name="rhs"/>.
/// Initially x = <paramref name="initialGuess"/> and then it converges to the solution.
/// </summary>
/// <param name="matrix">
/// Represents the matrix A of the linear system A * x = b, which must be symmetric positive definite.
/// </param>
/// <param name="rhs">
/// The right hand side vector b of the linear system A * x = b. Constraints:
/// <paramref name="rhs"/>.<see cref="IIndexable1D.Length"/>
/// == <paramref name="matrix"/>.<see cref="IIndexable2D.NumRows"/>.
/// </param>
/// <param name="solution">
/// The vector from which to start refining the solution vector x. Constraints:
/// <paramref name="solution"/>.<see cref="IIndexable1D.Length"/>
/// == <paramref name="matrix"/>.<see cref="IIndexable2D.NumColumns"/>.
/// </param>
/// <param name="initialGuessIsZero">
/// If <paramref name="solution"/> is 0, then set <paramref name="initialGuessIsZero"/> to true to avoid performing the
/// operation b-A*0 before starting.
/// </param>
/// <exception cref="NonMatchingDimensionsException">
/// Thrown if <paramref name="rhs"/> or <paramref name="solution"/> violate the described constraints.
/// </exception>
public IterativeStatistics Solve(ILinearTransformation matrix, IVectorView rhs, IVector solution,
bool initialGuessIsZero) //TODO: find a better way to handle the case x0=0
{
//TODO: these will also be checked by the matrix vector multiplication.
Preconditions.CheckMultiplicationDimensions(matrix.NumColumns, solution.Length);
Preconditions.CheckSystemSolutionDimensions(matrix.NumRows, rhs.Length);
this.Matrix = matrix;
this.Rhs = rhs;
this.solution = solution;
// r = b - A * x
if (initialGuessIsZero)
{
residual = rhs.Copy();
}
else
{
residual = ExactResidual.Calculate(matrix, rhs, solution);
}
return(SolveInternal(maxIterationsProvider.GetMaxIterations(matrix.NumColumns)));
}
/// <summary>
/// Solves the linear system A * x = b by solving the preconditioned system inv(P) * A * inv(P)^T * y = inv(P) * b,
/// where A = <paramref name="matrix"/>, b = <paramref name="rhsVector"/>, x is the solution, y = P^T * x,
/// P*P^T = <paramref name="preconditioner"/>.
/// Initially x = <paramref name="initialGuess"/> and then it converges to the solution.
/// </summary>
/// <param name="matrix">
/// Represents the matrix A of the linear system A * x = b, which must be symmetric positive definite.
/// </param>
/// <param name="rhs">
/// The right hand side vector b of the linear system A * x = b. Constraints:
/// <paramref name="rhs"/>.<see cref="IIndexable1D.Length"/>
/// == <paramref name="matrix"/>.<see cref="IIndexable2D.NumRows"/>.
/// </param>
/// <param name="preconditioner">
/// A preconditioner matrix that is also symmetric positive definite and has the same dimensions as A.
/// </param>
/// <param name="solution">
/// The vector from which to start refining the solution vector x. Constraints:
/// <paramref name="solution"/>.<see cref="IIndexable1D.Length"/>
/// == <paramref name="matrix"/>.<see cref="IIndexable2D.NumColumns"/>.
/// </param>
/// <param name="initialGuessIsZero">
/// If <paramref name="solution"/> is 0, then set <paramref name="initialGuessIsZero"/> to true to avoid performing the
/// operation b-A*0 before starting.
/// </param>
/// <exception cref="NonMatchingDimensionsException">
/// Thrown if <paramref name="rhs"/> or <paramref name="solution"/> violate the described constraints.
/// </exception>
public virtual IterativeStatistics Solve(ILinearTransformation matrix, IPreconditioner preconditioner, IVectorView rhs,
IVector solution, bool initialGuessIsZero, Func <IVector> zeroVectorInitializer)
{
//TODO: find a better way to handle optimizations for the case x0=0, than using an initialGuessIsZero flag
Preconditions.CheckMultiplicationDimensions(matrix.NumColumns, solution.Length);
Preconditions.CheckSystemSolutionDimensions(matrix.NumRows, rhs.Length);
this.Matrix = matrix;
this.Preconditioner = preconditioner;
this.Rhs = rhs;
this.solution = solution;
// r = b - A * x
if (initialGuessIsZero)
{
residual = rhs.Copy();
}
else
{
residual = ExactResidual.Calculate(matrix, rhs, solution);
}
return(SolveInternal(MaxIterationsProvider.GetMaxIterations(matrix.NumColumns), zeroVectorInitializer));
}
public IterativeStatistics Solve(ILinearTransformation matrix, IPreconditioner preconditioner, IVectorView rhs, IVector solution,
bool initialGuessIsZero, Func <IVector> zeroVectorInitializer)
{
Preconditions.CheckMultiplicationDimensions(matrix.NumColumns, solution.Length);
Preconditions.CheckSystemSolutionDimensions(matrix.NumRows, rhs.Length);
var innerIterations = innerIterationsProvider.GetMaxIterations(matrix.NumRows);
IVector[] v = new Vector[innerIterations + 1];
var y = Vector.CreateZero(innerIterations + 1);
var c = Vector.CreateZero(innerIterations + 1);
var s = Vector.CreateZero(innerIterations + 1);
var delta = 0.001;
double residualNorm = double.MaxValue;
var usedIterations = 0;
if (initialGuessIsZero)
{
residual = rhs.Copy();
}
else
{
residual = ExactResidual.Calculate(matrix, rhs, solution);
}
for (var iteration = 0; iteration < maximumIterations; iteration++)
{
preconditioner.SolveLinearSystem(residual, residual);
//var residual = ExactResidual.Calculate(matrix, rhs, solution);
residualNorm = residual.Norm2();
double residualTolerance;
if (iteration == 0)
{
residualTolerance = residualNorm * relativeTolerance;
}
v[0] = residual.Scale(1 / residualNorm);
var g = Vector.CreateZero(innerIterations + 1);
g[0] = residualNorm;
var hessenbergMatrix = Matrix.CreateZero(innerIterations + 1, innerIterations);
var indexIteration = 0;
for (int innerIteration = 0; innerIteration < innerIterations; innerIteration++)
{
indexIteration = innerIteration;
v[innerIteration + 1] = Vector.CreateZero(v[innerIteration].Length);
matrix.Multiply(v[innerIteration], v[innerIteration + 1]);
preconditioner.SolveLinearSystem(v[innerIteration + 1], v[innerIteration + 1]);
var av = v[innerIteration + 1].Norm2();
for (var j = 0; j <= innerIteration; j++)
{
hessenbergMatrix[j, innerIteration] = v[j].DotProduct(v[innerIteration + 1]);
v[innerIteration + 1] = v[innerIteration + 1].Subtract(v[j].Scale(hessenbergMatrix[j, innerIteration]));
}
hessenbergMatrix[innerIteration + 1, innerIteration] = v[innerIteration + 1].Norm2();
if (Math.Abs(av + delta * hessenbergMatrix[innerIteration + 1, innerIteration] - av) < 10e-9)
{
for (int j = 0; j <= innerIteration; j++)
{
var htmp = v[j].DotProduct(v[innerIteration + 1]);
hessenbergMatrix[j, innerIteration] += htmp;
v[innerIteration + 1].LinearCombinationIntoThis(1.0, v[j], -htmp);
}
hessenbergMatrix[innerIteration + 1, innerIteration] = v[innerIteration + 1].Norm2();
}
if (Math.Abs(hessenbergMatrix[innerIteration + 1, innerIteration]) > 10e-17)
{
v[innerIteration + 1].ScaleIntoThis(1 / hessenbergMatrix[innerIteration + 1, innerIteration]);
}
if (innerIteration > 0)
{
y = hessenbergMatrix.GetColumn(innerIteration).GetSubvector(0, innerIteration + 2);
for (int i = 0; i <= innerIteration - 1; i++)
{
y = CalculateGivensRotation(c[i], s[i], i, y);
}
hessenbergMatrix.SetSubcolumn(innerIteration, y);
}
var mu = Math.Sqrt(hessenbergMatrix[innerIteration, innerIteration] *
hessenbergMatrix[innerIteration, innerIteration] +
hessenbergMatrix[innerIteration + 1, innerIteration] *
hessenbergMatrix[innerIteration + 1, innerIteration]);
c[innerIteration] = hessenbergMatrix[innerIteration, innerIteration] / mu;
s[innerIteration] = -hessenbergMatrix[innerIteration + 1, innerIteration] / mu;
hessenbergMatrix[innerIteration, innerIteration] =
c[innerIteration] * hessenbergMatrix[innerIteration, innerIteration] -
s[innerIteration] * hessenbergMatrix[innerIteration + 1, innerIteration];
hessenbergMatrix[innerIteration + 1, innerIteration] = 0.0;
g = CalculateGivensRotation(c[innerIteration], s[innerIteration], innerIteration, g);
residualNorm = Math.Abs(g[innerIteration + 1]);
usedIterations++;
if (residualNorm <= relativeTolerance && residualNorm <= absoluteTolerance)
{
break;
}
}
indexIteration = indexIteration - 1;
y[indexIteration + 1] = g[indexIteration + 1] / hessenbergMatrix[indexIteration + 1, indexIteration + 1];
for (int i = indexIteration; i >= 0; i--)
{
y[i] = (g[i] - (hessenbergMatrix.GetRow(i).GetSubvector(i + 1, indexIteration + 2)
.DotProduct(y.GetSubvector(i + 1, indexIteration + 2)))) / hessenbergMatrix[i, i];
}
for (int i = 0; i < matrix.NumRows; i++)
{
var subV = Vector.CreateZero(indexIteration + 2);
for (int j = 0; j < indexIteration + 2; j++)
{
subV[j] = v[j][i];
}
solution.Set(i, solution[i] + subV.DotProduct(y.GetSubvector(0, indexIteration + 2)));
}
if (residualNorm <= relativeTolerance && residualNorm <= absoluteTolerance)
{
break;
}
}
return(new IterativeStatistics()
{
HasConverged = residualNorm <= relativeTolerance && residualNorm <= absoluteTolerance,
AlgorithmName = name,
NumIterationsRequired = usedIterations,
ResidualNormRatioEstimation = residualNorm
});
}