public void CanSolveForRandomMatrix(int order)
        {
            var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
            var matrixB = MatrixLoader.GenerateRandomDenseMatrix(order, order);

            var monitor = new Iterator<double>(new IIterationStopCriterium<double>[]
                {
                    new IterationCountStopCriterium<double>(1000),
                    new ResidualStopCriterium(1e-10)
                });
            var solver = new TFQMR(monitor);
            var matrixX = solver.Solve(matrixA, matrixB);

            // The solution X row dimension is equal to the column dimension of A
            Assert.AreEqual(matrixA.ColumnCount, matrixX.RowCount);

            // The solution X has the same number of columns as B
            Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);

            var matrixBReconstruct = matrixA*matrixX;

            // Check the reconstruction.
            for (var i = 0; i < matrixB.RowCount; i++)
            {
                for (var j = 0; j < matrixB.ColumnCount; j++)
                {
                    Assert.AreEqual(matrixB[i, j], matrixBReconstruct[i, j], 1.0e-7);
                }
            }
        }

        public void SolveLongMatrixThrowsArgumentException()
        {
            var matrix = new SparseMatrix(3, 2);
            Vector input = new DenseVector(3);

            var solver = new TFQMR();
            Assert.Throws<ArgumentException>(() => solver.Solve(matrix, input));
        }

        public void SolveLongMatrixThrowsArgumentException()
        {
            var matrix = new SparseMatrix(3, 2);
            var input = new DenseVector(3);

            var solver = new TFQMR();
            Assert.Throws<ArgumentException>(() => matrix.SolveIterative(input, solver));
        }

        public void SolveWideMatrixThrowsArgumentException()
        {
            var matrix = new SparseMatrix(2, 3);
            var input = new DenseVector(2);

            var solver = new TFQMR();
            Assert.That(() => matrix.SolveIterative(input, solver), Throws.ArgumentException);
        }

        /// <summary>
        /// The main method that runs the TFQMR iterative solver.
        /// </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 a preconditioner. The possibilities are:
            // 1) No preconditioner - Simply do not provide the solver with a preconditioner.
            // 2) A simple diagonal preconditioner - Create an instance of the Diagonal class.
            // 3) A ILU preconditioner - Create an instance of the IncompleteLu class.
            // 4) A ILU preconditioner with pivoting and drop tolerances - Create an instance of the Ilutp class.

            // Here we'll use the simple diagonal preconditioner.
            // We need a link to the matrix so the pre-conditioner can do it's work.
            IPreConditioner preconditioner = new Diagonal();

            // Create a new iterator. This checks for convergence of the results of the
            // iterative matrix solver.
            // In this case we'll create the default iterator
            IIterator iterator = Iterator.CreateDefault();

            // Create the solver
            TFQMR solver = new TFQMR(preconditioner, iterator);

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

            // Finally you can check the reason the solver finished the iterative process
            // by calling the SolutionStatus property on the iterator
            ICalculationStatus status = iterator.Status;
            if (status is CalculationCancelled)
                Console.WriteLine("The user cancelled the calculation.");

            if (status is CalculationIndetermined)
                Console.WriteLine("Oh oh, something went wrong. The iterative process was never started.");

            if (status is CalculationConverged)
                Console.WriteLine("Yippee, the iterative process converged.");

            if (status is CalculationDiverged)
                Console.WriteLine("I'm sorry the iterative process diverged.");

            if (status is CalculationFailure)
                Console.WriteLine("Oh dear, the iterative process failed.");

            if (status is CalculationStoppedWithoutConvergence)
                Console.WriteLine("Oh dear, the iterative process did not converge.");
        }

        public void CanSolveForRandomMatrix(int order)
        {
            for (var iteration = 5; iteration > 3; iteration--)
            {
                var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
                var matrixB = MatrixLoader.GenerateRandomDenseMatrix(order, order);

                var monitor = new Iterator(new IIterationStopCriterium[]
                                           {
                                               new IterationCountStopCriterium(1000),
                                               new ResidualStopCriterium((float)Math.Pow(1.0 / 10.0, iteration))
                                           });
                var solver = new TFQMR(monitor);
                var matrixX = solver.Solve(matrixA, matrixB);

                if (!(monitor.Status is CalculationConverged))
                {
                    // Solution was not found, try again downgrading convergence boundary
                    continue;
                }

                // The solution X row dimension is equal to the column dimension of A
                Assert.AreEqual(matrixA.ColumnCount, matrixX.RowCount);

                // The solution X has the same number of columns as B
                Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);

                var matrixBReconstruct = matrixA * matrixX;

                // Check the reconstruction.
                for (var i = 0; i < matrixB.RowCount; i++)
                {
                    for (var j = 0; j < matrixB.ColumnCount; j++)
                    {
                        Assert.AreEqual(matrixB[i, j].Real, matrixBReconstruct[i, j].Real, (float)Math.Pow(1.0 / 10.0, iteration - 3));
                        Assert.AreEqual(matrixB[i, j].Imaginary, matrixBReconstruct[i, j].Imaginary, (float)Math.Pow(1.0 / 10.0, iteration - 3));
                    }
                }

                return;
            }

            Assert.Fail("Solution was not found in 3 tries");
        }

        public void CanSolveForRandomMatrix(int order)
        {
            // Due to datatype "float" it can happen that solution will not converge for specific random matrix
            // That's why we will do 4 tries and downgrade stop criterium each time
            for (var iteration = 6; iteration > 3; iteration--)
            {
                var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
                var matrixB = MatrixLoader.GenerateRandomDenseMatrix(order, order);

                var monitor = new Iterator(new IIterationStopCriterium<float>[]
                                           {
                                               new IterationCountStopCriterium(MaximumIterations),
                                               new ResidualStopCriterium((float)Math.Pow(1.0/10.0, iteration))
                                           });
                var solver = new TFQMR(monitor);
                var matrixX = solver.Solve(matrixA, matrixB);

                if (!(monitor.Status is CalculationConverged))
                {
                    // Solution was not found, try again downgrading convergence boundary
                    continue;
                }

                // The solution X row dimension is equal to the column dimension of A
                Assert.AreEqual(matrixA.ColumnCount, matrixX.RowCount);
                // The solution X has the same number of columns as B
                Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);

                var matrixBReconstruct = matrixA * matrixX;

                // Check the reconstruction.
                for (var i = 0; i < matrixB.RowCount; i++)
                {
                    for (var j = 0; j < matrixB.ColumnCount; j++)
                    {
                        Assert.AreApproximatelyEqual(matrixB[i, j], matrixBReconstruct[i, j], (float)Math.Pow(1.0 / 10.0, iteration - 3));
                    }
                }

                return;
            }

            Assert.Fail("Solution was not found in 3 tries");
        }

        public void CanSolveForRandomMatrix(int order)
        {
            // Due to datatype "float" it can happen that solution will not converge for specific random matrix
            // That's why we will do 4 tries and downgrade stop criterion each time
            for (var iteration = 6; iteration > 3; iteration--)
            {
                var matrixA = Matrix<float>.Build.Random(order, order, 1);
                var matrixB = Matrix<float>.Build.Random(order, order, 1);

                var monitor = new Iterator<float>(
                    new IterationCountStopCriterion<float>(MaximumIterations),
                    new ResidualStopCriterion<float>(Math.Pow(1.0/10.0, iteration)));

                var solver = new TFQMR();
                var matrixX = matrixA.SolveIterative(matrixB, solver, monitor);

                if (monitor.Status != IterationStatus.Converged)
                {
                    // Solution was not found, try again downgrading convergence boundary
                    continue;
                }

                // The solution X row dimension is equal to the column dimension of A
                Assert.AreEqual(matrixA.ColumnCount, matrixX.RowCount);

                // The solution X has the same number of columns as B
                Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);

                var matrixBReconstruct = matrixA*matrixX;

                // Check the reconstruction.
                for (var i = 0; i < matrixB.RowCount; i++)
                {
                    for (var j = 0; j < matrixB.ColumnCount; j++)
                    {
                        Assert.AreEqual(matrixB[i, j], matrixBReconstruct[i, j], (float)Math.Pow(1.0/10.0, iteration - 4));
                    }
                }

                return;
            }
        }

        public void CanSolveForRandomVector(int order)
        {
            var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
            var vectorb = MatrixLoader.GenerateRandomDenseVector(order);

            var monitor = new Iterator(new IIterationStopCriterium<double>[]
                                       {
                                           new IterationCountStopCriterium(1000),
                                           new ResidualStopCriterium(1e-10),
                                       });
            var solver = new TFQMR(monitor);

            var resultx = solver.Solve(matrixA, vectorb);
            Assert.AreEqual(matrixA.ColumnCount, resultx.Count);

            var bReconstruct = matrixA * resultx;

            // Check the reconstruction.
            for (var i = 0; i < order; i++)
            {
                Assert.AreApproximatelyEqual(vectorb[i], bReconstruct[i], 1e-7);
            }
        }

        public void SolveScaledUnitMatrixAndBackMultiply()
        {
            // Create the identity matrix
            var matrix = SparseMatrix.Identity(100);

            // Scale it with a funny number
            matrix.Multiply((float) Math.PI, matrix);

            // Create the y vector
            var y = DenseVector.Create(matrix.RowCount, i => 1);

            // Create an iteration monitor which will keep track of iterative convergence
            var monitor = new Iterator<Complex32>(new IIterationStopCriterium<Complex32>[]
                {
                    new IterationCountStopCriterium<Complex32>(MaximumIterations),
                    new ResidualStopCriterium(ConvergenceBoundary),
                    new DivergenceStopCriterium(),
                    new FailureStopCriterium()
                });
            var solver = new TFQMR(monitor);

            // Solve equation Ax = y
            var x = solver.Solve(matrix, y);

            // Now compare the results
            Assert.IsNotNull(x, "#02");
            Assert.AreEqual(y.Count, x.Count, "#03");

            // Back multiply the vector
            var z = matrix.Multiply(x);

            // Check that the solution converged
            Assert.IsTrue(monitor.HasConverged, "#04");

            // Now compare the vectors
            for (var i = 0; i < y.Count; i++)
            {
                Assert.IsTrue((y[i] - z[i]).Magnitude.IsSmaller(ConvergenceBoundary, 1), "#05-" + i);
            }
        }

        public void CanSolveForRandomVector(int order)
        {
            for (var iteration = 5; iteration > 3; iteration--)
            {
                var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
                var vectorb = MatrixLoader.GenerateRandomDenseVector(order);

                var monitor = new Iterator<Complex32>(new IIterationStopCriterium<Complex32>[]
                    {
                        new IterationCountStopCriterium<Complex32>(1000),
                        new ResidualStopCriterium((float) Math.Pow(1.0/10.0, iteration))
                    });
                var solver = new TFQMR(monitor);

                var resultx = solver.Solve(matrixA, vectorb);

                if (!monitor.HasConverged)
                {
                    // Solution was not found, try again downgrading convergence boundary
                    continue;
                }

                Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
                var matrixBReconstruct = matrixA*resultx;

                // Check the reconstruction.
                for (var i = 0; i < order; i++)
                {
                    Assert.AreEqual(vectorb[i].Real, matrixBReconstruct[i].Real, (float) Math.Pow(1.0/10.0, iteration - 3));
                    Assert.AreEqual(vectorb[i].Imaginary, matrixBReconstruct[i].Imaginary, (float) Math.Pow(1.0/10.0, iteration - 3));
                }

                return;
            }

            Assert.Fail("Solution was not found in 3 tries");
        }

        public void CanSolveForRandomMatrix(int order)
        {
            var matrixA = Matrix<Complex>.Build.Random(order, order, 1);
            var matrixB = Matrix<Complex>.Build.Random(order, order, 1);

            var monitor = new Iterator<Complex>(
                new IterationCountStopCriterium<Complex>(1000),
                new ResidualStopCriterium<Complex>(1e-10));

            var solver = new TFQMR();
            var matrixX = matrixA.SolveIterative(matrixB, solver, monitor);

            // The solution X row dimension is equal to the column dimension of A
            Assert.AreEqual(matrixA.ColumnCount, matrixX.RowCount);

            // The solution X has the same number of columns as B
            Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);

            var matrixBReconstruct = matrixA*matrixX;

            // Check the reconstruction.
            for (var i = 0; i < matrixB.RowCount; i++)
            {
                for (var j = 0; j < matrixB.ColumnCount; j++)
                {
                    Assert.AreEqual(matrixB[i, j].Real, matrixBReconstruct[i, j].Real, 1.0e-5);
                    Assert.AreEqual(matrixB[i, j].Imaginary, matrixBReconstruct[i, j].Imaginary, 1.0e-5);
                }
            }
        }

        public void SolveUnitMatrixAndBackMultiply()
        {
            // Create the identity matrix
            var matrix = SparseMatrix.CreateIdentity(100);

            // Create the y vector
            var y = Vector<Complex>.Build.Dense(matrix.RowCount, 1);

            // Create an iteration monitor which will keep track of iterative convergence
            var monitor = new Iterator<Complex>(
                new IterationCountStopCriterium<Complex>(MaximumIterations),
                new ResidualStopCriterium<Complex>(ConvergenceBoundary),
                new DivergenceStopCriterium<Complex>(),
                new FailureStopCriterium<Complex>());

            var solver = new TFQMR();

            // Solve equation Ax = y
            var x = matrix.SolveIterative(y, solver, monitor);

            // Now compare the results
            Assert.IsNotNull(x, "#02");
            Assert.AreEqual(y.Count, x.Count, "#03");

            // Back multiply the vector
            var z = matrix.Multiply(x);

            // Check that the solution converged
            Assert.IsTrue(monitor.Status == IterationStatus.Converged, "#04");

            // Now compare the vectors
            Assert.LessOrEqual(Distance.Chebyshev(y, z), 2*ConvergenceBoundary);
        }

        public void CanSolveForRandomVector(int order)
        {
            var matrixA = Matrix<Complex>.Build.Random(order, order, 1);
            var vectorb = Vector<Complex>.Build.Random(order, 1);

            var monitor = new Iterator<Complex>(
                new IterationCountStopCriterium<Complex>(1000),
                new ResidualStopCriterium<Complex>(1e-10));

            var solver = new TFQMR();

            var resultx = matrixA.SolveIterative(vectorb, solver, monitor);
            Assert.AreEqual(matrixA.ColumnCount, resultx.Count);

            var matrixBReconstruct = matrixA*resultx;

            // Check the reconstruction.
            for (var i = 0; i < order; i++)
            {
                Assert.AreEqual(vectorb[i].Real, matrixBReconstruct[i].Real, 1e-5);
                Assert.AreEqual(vectorb[i].Imaginary, matrixBReconstruct[i].Imaginary, 1e-5);
            }
        }

        public void CanSolveForRandomVector(int order)
        {
            for (var iteration = 5; iteration > 3; iteration--)
            {
                var matrixA = Matrix<Complex32>.Build.Random(order, order, 1);
                var vectorb = Vector<Complex32>.Build.Random(order, 1);

                var monitor = new Iterator<Complex32>(
                    new IterationCountStopCriterion<Complex32>(1000),
                    new ResidualStopCriterion<Complex32>(Math.Pow(1.0/10.0, iteration)));

                var solver = new TFQMR();

                var resultx = matrixA.SolveIterative(vectorb, solver, monitor);

                if (monitor.Status != IterationStatus.Converged)
                {
                    // Solution was not found, try again downgrading convergence boundary
                    continue;
                }

                Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
                var matrixBReconstruct = matrixA*resultx;

                // Check the reconstruction.
                for (var i = 0; i < order; i++)
                {
                    Assert.AreEqual(vectorb[i].Real, matrixBReconstruct[i].Real, (float)Math.Pow(1.0/10.0, iteration - 3));
                    Assert.AreEqual(vectorb[i].Imaginary, matrixBReconstruct[i].Imaginary, (float)Math.Pow(1.0/10.0, iteration - 3));
                }

                return;
            }

            Assert.Fail("Solution was not found in 3 tries");
        }

        public void SolveUnitMatrixAndBackMultiply()
        {
            // Create the identity matrix
            var matrix = SparseMatrix.Identity(100);

            // Create the y vector
            var y = DenseVector.Create(matrix.RowCount, i => 1);

            // Create an iteration monitor which will keep track of iterative convergence
            var monitor = new Iterator<float>(
                new IterationCountStopCriterium<float>(MaximumIterations),
                new ResidualStopCriterium(ConvergenceBoundary),
                new DivergenceStopCriterium(),
                new FailureStopCriterium());

            var solver = new TFQMR();

            // Solve equation Ax = y
            var x = matrix.SolveIterative(y, solver, monitor);

            // Now compare the results
            Assert.IsNotNull(x, "#02");
            Assert.AreEqual(y.Count, x.Count, "#03");

            // Back multiply the vector
            var z = matrix.Multiply(x);

            // Check that the solution converged
            Assert.IsTrue(monitor.Status == IterationStatus.Converged, "#04");

            // Now compare the vectors
            for (var i = 0; i < y.Count; i++)
            {
                Assert.IsTrue((y[i] - z[i]).IsSmaller(ConvergenceBoundary, 1), "#05-" + i);
            }
        }

        public void CanSolveForRandomVector(int order)
        {
            // Due to datatype "float" it can happen that solution will not converge for specific random matrix
            // That's why we will do 3 tries and downgrade stop criterium each time
            for (var iteration = 6; iteration > 3; iteration--)
            {
                var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
                var vectorb = MatrixLoader.GenerateRandomDenseVector(order);

                var monitor = new Iterator<float>(
                    new IterationCountStopCriterium<float>(MaximumIterations),
                    new ResidualStopCriterium((float) Math.Pow(1.0/10.0, iteration)));

                var solver = new TFQMR();
                var resultx = matrixA.SolveIterative(vectorb, solver, monitor);

                if (monitor.Status != IterationStatus.Converged)
                {
                    // Solution was not found, try again downgrading convergence boundary
                    continue;
                }

                Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
                var matrixBReconstruct = matrixA*resultx;

                // Check the reconstruction.
                for (var i = 0; i < order; i++)
                {
                    Assert.AreEqual(vectorb[i], matrixBReconstruct[i], (float) Math.Pow(1.0/10.0, iteration - 3));
                }

                return;
            }
        }

        public void SolveLongMatrix()
        {
            var matrix = new SparseMatrix(3, 2);
            Vector<float> input = new DenseVector(3);

            var solver = new TFQMR();
            solver.Solve(matrix, input);
        }

        public void SolvePoissonMatrixAndBackMultiply()
        {
            // Create the matrix
            var matrix = Matrix<double>.Build.Sparse(100, 100);

            // Assemble the matrix. We assume we're solving the Poisson equation
            // on a rectangular 10 x 10 grid
            const int GridSize = 10;

            // The pattern is:
            // 0 .... 0 -1 0 0 0 0 0 0 0 0 -1 4 -1 0 0 0 0 0 0 0 0 -1 0 0 ... 0
            for (var i = 0; i < matrix.RowCount; i++)
            {
                // Insert the first set of -1's
                if (i > (GridSize - 1))
                {
                    matrix[i, i - GridSize] = -1;
                }

                // Insert the second set of -1's
                if (i > 0)
                {
                    matrix[i, i - 1] = -1;
                }

                // Insert the centerline values
                matrix[i, i] = 4;

                // Insert the first trailing set of -1's
                if (i < matrix.RowCount - 1)
                {
                    matrix[i, i + 1] = -1;
                }

                // Insert the second trailing set of -1's
                if (i < matrix.RowCount - GridSize)
                {
                    matrix[i, i + GridSize] = -1;
                }
            }

            // Create the y vector
            var y = Vector<double>.Build.Dense(matrix.RowCount, 1);

            // Create an iteration monitor which will keep track of iterative convergence
            var monitor = new Iterator<double>(
                new IterationCountStopCriterion<double>(MaximumIterations),
                new ResidualStopCriterion<double>(ConvergenceBoundary),
                new DivergenceStopCriterion<double>(),
                new FailureStopCriterion<double>());

            var solver = new TFQMR();

            // Solve equation Ax = y
            var x = matrix.SolveIterative(y, solver, monitor);

            // Now compare the results
            Assert.IsNotNull(x, "#02");
            Assert.AreEqual(y.Count, x.Count, "#03");

            // Back multiply the vector
            var z = matrix.Multiply(x);

            // Check that the solution converged
            Assert.IsTrue(monitor.Status == IterationStatus.Converged, "#04");

            // Now compare the vectors
            Assert.LessOrEqual(Distance.Chebyshev(y, z), 2*ConvergenceBoundary);
        }

        public void SolvePoissonMatrixAndBackMultiply()
        {
            // Create the matrix
            var matrix = new SparseMatrix(25);

            // Assemble the matrix. We assume we're solving the Poisson equation
            // on a rectangular 5 x 5 grid
            const int GridSize = 5;

            // The pattern is:
            // 0 .... 0 -1 0 0 0 0 0 0 0 0 -1 4 -1 0 0 0 0 0 0 0 0 -1 0 0 ... 0
            for (var i = 0; i < matrix.RowCount; i++)
            {
                // Insert the first set of -1's
                if (i > (GridSize - 1))
                {
                    matrix[i, i - GridSize] = -1;
                }

                // Insert the second set of -1's
                if (i > 0)
                {
                    matrix[i, i - 1] = -1;
                }

                // Insert the centerline values
                matrix[i, i] = 4;

                // Insert the first trailing set of -1's
                if (i < matrix.RowCount - 1)
                {
                    matrix[i, i + 1] = -1;
                }

                // Insert the second trailing set of -1's
                if (i < matrix.RowCount - GridSize)
                {
                    matrix[i, i + GridSize] = -1;
                }
            }

            // Create the y vector
            var y = DenseVector.Create(matrix.RowCount, i => 1);

            // Create an iteration monitor which will keep track of iterative convergence
            var monitor = new Iterator<float>(
                new IterationCountStopCriterium<float>(MaximumIterations),
                new ResidualStopCriterium(ConvergenceBoundary),
                new DivergenceStopCriterium(),
                new FailureStopCriterium());

            var solver = new TFQMR();

            // Solve equation Ax = y
            var x = matrix.SolveIterative(y, solver, monitor);

            // Now compare the results
            Assert.IsNotNull(x, "#02");
            Assert.AreEqual(y.Count, x.Count, "#03");

            // Back multiply the vector
            var z = matrix.Multiply(x);

            // Check that the solution converged
            Assert.IsTrue(monitor.Status == IterationStatus.Converged, "#04");

            // Now compare the vectors
            for (var i = 0; i < y.Count; i++)
            {
                Assert.IsTrue(Math.Abs(y[i] - z[i]).IsSmaller(ConvergenceBoundary, 1), "#05-" + i);
            }
        }