Beispiel #1
0
        /// <summary>
        /// The main method that uses the Ilutp preconditioner.
        /// </summary>
        public void UseSolver()
        {
            // Create a sparse matrix. For now the size will be 10 x 10 elements
            Matrix matrix = CreateMatrix(10);

            // Create the right hand side vector. The size is the same as the matrix
            // and all values will be 2.0.
            Vector rightHandSideVector = new DenseVector(10, 2.0);

            // Create the Ilutp preconditioner
            Ilutp preconditioner = new Ilutp();

            // Set the drop tolerance. All entries with absolute values smaller than this value will be
            // removed from the preconditioner matrices.
            preconditioner.DropTolerance = 1e-5;
            // Set the relative fill level. This indicates how much additional fill we allow. In this case
            // about 200%
            preconditioner.FillLevel = 200;
            // Set the pivot tolerance. This indicates when pivoting is used. In this case we pivot if
            // the largest off-diagonal entry is twice as big as the diagonal entry.
            preconditioner.PivotTolerance = 0.5;

            // Create the actual preconditioner
            preconditioner.Initialize(matrix);

            // Now that all is set we can solve the matrix equation.
            Vector solutionVector = preconditioner.Approximate(rightHandSideVector);

            // Another way to get the values is by using the overloaded solve method
            // In this case the solution vector needs to be of the correct size.
            preconditioner.Approximate(rightHandSideVector, solutionVector);
        }
        public void CompareWithOriginalDenseMatrixWithoutPivoting()
        {
            var sparseMatrix = new SparseMatrix(3);

            sparseMatrix[0, 0] = -1;
            sparseMatrix[0, 1] = 5;
            sparseMatrix[0, 2] = 6;
            sparseMatrix[1, 0] = 3;
            sparseMatrix[1, 1] = -6;
            sparseMatrix[1, 2] = 1;
            sparseMatrix[2, 0] = 6;
            sparseMatrix[2, 1] = 8;
            sparseMatrix[2, 2] = 9;
            var ilu = new Ilutp
            {
                PivotTolerance = 0.0,
                DropTolerance  = 0,
                FillLevel      = 10
            };

            ilu.Initialize(sparseMatrix);
            var l = GetLowerTriangle(ilu);

            // Assert l is lower triagonal
            for (var i = 0; i < l.RowCount; i++)
            {
                for (var j = i + 1; j < l.RowCount; j++)
                {
                    Assert.IsTrue(0.0.AlmostEqual(l[i, j].Magnitude, -Epsilon.Magnitude()), "#01-" + i + "-" + j);
                }
            }

            var u = GetUpperTriangle(ilu);

            // Assert u is upper triagonal
            for (var i = 0; i < u.RowCount; i++)
            {
                for (var j = 0; j < i; j++)
                {
                    Assert.IsTrue(0.0.AlmostEqual(u[i, j].Magnitude, -Epsilon.Magnitude()), "#02-" + i + "-" + j);
                }
            }

            var original = l.Multiply(u);

            for (var i = 0; i < sparseMatrix.RowCount; i++)
            {
                for (var j = 0; j < sparseMatrix.ColumnCount; j++)
                {
                    Assert.IsTrue(sparseMatrix[i, j].Real.AlmostEqual(original[i, j].Real, -Epsilon.Magnitude()), "#03-" + i + "-" + j);
                    Assert.IsTrue(sparseMatrix[i, j].Imaginary.AlmostEqual(original[i, j].Imaginary, -Epsilon.Magnitude()), "#04-" + i + "-" + j);
                }
            }
        }
 public void SolveWithPivoting()
 {
     const int Size = 10;
     var newMatrix = CreateReverseUnitMatrix(Size);
     var vector = CreateStandardBcVector(Size);
     var preconditioner = new Ilutp
                          {
                              PivotTolerance = 1.0,
                              DropTolerance = 0,
                              FillLevel = 10
                          };
     preconditioner.Initialize(newMatrix);
     Vector result = new DenseVector(vector.Count);
     preconditioner.Approximate(vector, result);
     CheckResult(preconditioner, newMatrix, vector, result);
 }
        public void CompareWithOriginalDenseMatrixWithPivoting()
        {
            var sparseMatrix = new SparseMatrix(3);

            sparseMatrix[0, 0] = -1;
            sparseMatrix[0, 1] = 5;
            sparseMatrix[0, 2] = 6;
            sparseMatrix[1, 0] = 3;
            sparseMatrix[1, 1] = -6;
            sparseMatrix[1, 2] = 1;
            sparseMatrix[2, 0] = 6;
            sparseMatrix[2, 1] = 8;
            sparseMatrix[2, 2] = 9;
            var ilu = new Ilutp
            {
                PivotTolerance = 1.0,
                DropTolerance  = 0,
                FillLevel      = 10
            };

            ilu.Initialize(sparseMatrix);
            var l      = GetLowerTriangle(ilu);
            var u      = GetUpperTriangle(ilu);
            var pivots = GetPivots(ilu);
            var p      = new SparseMatrix(l.RowCount);

            for (var i = 0; i < p.RowCount; i++)
            {
                p[i, pivots[i]] = 1.0;
            }

            var temp     = l.Multiply(u);
            var original = temp.Multiply(p);

            for (var i = 0; i < sparseMatrix.RowCount; i++)
            {
                for (var j = 0; j < sparseMatrix.ColumnCount; j++)
                {
                    Assert.IsTrue(sparseMatrix[i, j].Real.AlmostEqual(original[i, j].Real, -Epsilon.Magnitude()), "#01-" + i + "-" + j);
                    Assert.IsTrue(sparseMatrix[i, j].Imaginary.AlmostEqual(original[i, j].Imaginary, -Epsilon.Magnitude()), "#02-" + i + "-" + j);
                }
            }
        }