diff --git a/src/Examples/LinearAlgebra/DirectSolvers.cs b/src/Examples/LinearAlgebra/DirectSolvers.cs
index 30d10565..210a33b6 100644
--- a/src/Examples/LinearAlgebra/DirectSolvers.cs
+++ b/src/Examples/LinearAlgebra/DirectSolvers.cs
@@ -64,7 +64,7 @@ namespace Examples.LinearAlgebraExamples
         public void Run()
         {
             // Format matrix output to console
-            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            var formatProvider = (CultureInfo) CultureInfo.InvariantCulture.Clone();
             formatProvider.TextInfo.ListSeparator = " ";
 
             // Solve next system of linear equations (Ax=b):
@@ -73,13 +73,13 @@ namespace Examples.LinearAlgebraExamples
             // 4*x + 1*y + 5*z = 43
 
             // Create matrix "A" with coefficients 
-            var matrixA = DenseMatrix.OfArray(new[,] { { 5.00, 2.00, -4.00 }, { 3.00, -7.00, 6.00 }, { 4.00, 1.00, 5.00 } });
+            var matrixA = DenseMatrix.OfArray(new[,] {{5.00, 2.00, -4.00}, {3.00, -7.00, 6.00}, {4.00, 1.00, 5.00}});
             Console.WriteLine(@"Matrix 'A' with coefficients");
             Console.WriteLine(matrixA.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
 
             // Create vector "b" with the constant terms.
-            var vectorB = new DenseVector(new[] { -7.0, 38.0, 43.0 });
+            var vectorB = new DenseVector(new[] {-7.0, 38.0, 43.0});
             Console.WriteLine(@"Vector 'b' with the constant terms");
             Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
@@ -97,7 +97,7 @@ namespace Examples.LinearAlgebraExamples
             Console.WriteLine();
 
             // 3. Solve linear equations using SVD decomposition
-            matrixA.Svd(true).Solve(vectorB, resultX);
+            matrixA.Svd().Solve(vectorB, resultX);
             Console.WriteLine(@"3. Solution using SVD decomposition");
             Console.WriteLine(resultX.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
@@ -109,7 +109,7 @@ namespace Examples.LinearAlgebraExamples
             Console.WriteLine();
 
             // 5. Verify result. Multiply coefficient matrix "A" by result vector "x"
-            var reconstructVecorB = matrixA * resultX;
+            var reconstructVecorB = matrixA*resultX;
             Console.WriteLine(@"5. Multiply coefficient matrix 'A' by result vector 'x'");
             Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
@@ -135,7 +135,7 @@ namespace Examples.LinearAlgebraExamples
             Console.WriteLine();
 
             // 8. Verify result. Multiply new coefficient matrix "A" by result vector "x"
-            reconstructVecorB = newMatrixA * resultX;
+            reconstructVecorB = newMatrixA*resultX;
             Console.WriteLine(@"8. Multiply new coefficient matrix 'A' by result vector 'x'");
             Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
diff --git a/src/Examples/LinearAlgebra/Factorization/Svd.cs b/src/Examples/LinearAlgebra/Factorization/Svd.cs
index dd8e65f7..bb856732 100644
--- a/src/Examples/LinearAlgebra/Factorization/Svd.cs
+++ b/src/Examples/LinearAlgebra/Factorization/Svd.cs
@@ -26,7 +26,6 @@
 
 using System;
 using System.Globalization;
-using MathNet.Numerics.LinearAlgebra;
 using MathNet.Numerics.LinearAlgebra.Double;
 
 namespace Examples.LinearAlgebra.FactorizationExamples
@@ -73,17 +72,17 @@ namespace Examples.LinearAlgebra.FactorizationExamples
         public void Run()
         {
             // Format matrix output to console
-            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            var formatProvider = (CultureInfo) CultureInfo.InvariantCulture.Clone();
             formatProvider.TextInfo.ListSeparator = " ";
 
             // Create square matrix
-            var matrix = DenseMatrix.OfArray(new[,] { { 4.0, 1.0 }, { 3.0, 2.0 } });
+            var matrix = DenseMatrix.OfArray(new[,] {{4.0, 1.0}, {3.0, 2.0}});
             Console.WriteLine(@"Initial square matrix");
             Console.WriteLine(matrix.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
 
             // Perform full SVD decomposition
-            var svd = matrix.Svd(true);
+            var svd = matrix.Svd();
             Console.WriteLine(@"Perform full SVD decomposition");
 
             // 1. Left singular vectors
@@ -107,7 +106,7 @@ namespace Examples.LinearAlgebra.FactorizationExamples
             Console.WriteLine();
 
             // 5. Multiply U matrix by its transpose
-            var identinty = svd.U * svd.U.Transpose();
+            var identinty = svd.U*svd.U.Transpose();
             Console.WriteLine(@"5. Multiply U matrix by its transpose");
             Console.WriteLine(identinty.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
@@ -119,7 +118,7 @@ namespace Examples.LinearAlgebra.FactorizationExamples
             Console.WriteLine();
 
             // 7. Reconstruct initial matrix: A = U*Σ*VT
-            var reconstruct = svd.U * svd.W * svd.VT;
+            var reconstruct = svd.U*svd.W*svd.VT;
             Console.WriteLine(@"7. Reconstruct initial matrix: A = U*S*VT");
             Console.WriteLine(reconstruct.ToString("#0.00\t", formatProvider));
             Console.WriteLine();
diff --git a/src/Numerics/LinearAlgebra/Complex/DenseMatrix.cs b/src/Numerics/LinearAlgebra/Complex/DenseMatrix.cs
index c1e591b4..e1305b4f 100644
--- a/src/Numerics/LinearAlgebra/Complex/DenseMatrix.cs
+++ b/src/Numerics/LinearAlgebra/Complex/DenseMatrix.cs
@@ -1029,7 +1029,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex
             return DenseGramSchmidt.Create(this);
         }
 
-        public override Svd<Complex> Svd(bool computeVectors)
+        public override Svd<Complex> Svd(bool computeVectors = true)
         {
             return DenseSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Complex/Matrix.cs b/src/Numerics/LinearAlgebra/Complex/Matrix.cs
index 01ac95ad..cd29db07 100644
--- a/src/Numerics/LinearAlgebra/Complex/Matrix.cs
+++ b/src/Numerics/LinearAlgebra/Complex/Matrix.cs
@@ -478,7 +478,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex
             return UserGramSchmidt.Create(this);
         }
 
-        public override Svd<Complex> Svd(bool computeVectors)
+        public override Svd<Complex> Svd(bool computeVectors = true)
         {
             return UserSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Complex32/DenseMatrix.cs b/src/Numerics/LinearAlgebra/Complex32/DenseMatrix.cs
index 1ec044b3..5811a2c6 100644
--- a/src/Numerics/LinearAlgebra/Complex32/DenseMatrix.cs
+++ b/src/Numerics/LinearAlgebra/Complex32/DenseMatrix.cs
@@ -1024,7 +1024,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex32
             return DenseGramSchmidt.Create(this);
         }
 
-        public override Svd<Complex32> Svd(bool computeVectors)
+        public override Svd<Complex32> Svd(bool computeVectors = true)
         {
             return DenseSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Complex32/Matrix.cs b/src/Numerics/LinearAlgebra/Complex32/Matrix.cs
index 2f39afcb..1dd2ab2b 100644
--- a/src/Numerics/LinearAlgebra/Complex32/Matrix.cs
+++ b/src/Numerics/LinearAlgebra/Complex32/Matrix.cs
@@ -473,7 +473,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex32
             return UserGramSchmidt.Create(this);
         }
 
-        public override Svd<Complex32> Svd(bool computeVectors)
+        public override Svd<Complex32> Svd(bool computeVectors = true)
         {
             return UserSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Double/DenseMatrix.cs b/src/Numerics/LinearAlgebra/Double/DenseMatrix.cs
index 85bfa7e5..d5c51408 100644
--- a/src/Numerics/LinearAlgebra/Double/DenseMatrix.cs
+++ b/src/Numerics/LinearAlgebra/Double/DenseMatrix.cs
@@ -1055,7 +1055,7 @@ namespace MathNet.Numerics.LinearAlgebra.Double
             return DenseGramSchmidt.Create(this);
         }
 
-        public override Svd<double> Svd(bool computeVectors)
+        public override Svd<double> Svd(bool computeVectors = true)
         {
             return DenseSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Double/Matrix.cs b/src/Numerics/LinearAlgebra/Double/Matrix.cs
index bb0b4fdc..210b8ada 100644
--- a/src/Numerics/LinearAlgebra/Double/Matrix.cs
+++ b/src/Numerics/LinearAlgebra/Double/Matrix.cs
@@ -479,7 +479,7 @@ namespace MathNet.Numerics.LinearAlgebra.Double
             return UserGramSchmidt.Create(this);
         }
 
-        public override Svd<double> Svd(bool computeVectors)
+        public override Svd<double> Svd(bool computeVectors = true)
         {
             return UserSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Matrix.Solve.cs b/src/Numerics/LinearAlgebra/Matrix.Solve.cs
index 8ed75110..3154276b 100644
--- a/src/Numerics/LinearAlgebra/Matrix.Solve.cs
+++ b/src/Numerics/LinearAlgebra/Matrix.Solve.cs
@@ -72,7 +72,7 @@ namespace MathNet.Numerics.LinearAlgebra
         /// </summary>
         /// <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
         /// <returns>The SVD decomposition object.</returns>
-        public abstract Svd<T> Svd(bool computeVectors);
+        public abstract Svd<T> Svd(bool computeVectors = true);
 
         /// <summary>
         /// Computes the EVD decomposition for a matrix.
@@ -214,24 +214,55 @@ namespace MathNet.Numerics.LinearAlgebra
             return iterator.Status;
         }
 
+
+        /// <summary>
+        /// Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
+        /// </summary>
+        /// <param name="input">The solution vector <c>b</c>.</param>
+        /// <param name="result">The result vector <c>x</c>.</param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
+        /// <param name="preconditioner">The preconditioner to use for approximations.</param>
         public IterationStatus TrySolveIterative(Vector<T> input, Vector<T> result, IIterativeSolver<T> solver, IPreconditioner<T> preconditioner, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var iterator = new Iterator<T>(stopCriteria.Length == 0 ? Builder.IterativeSolverStopCriteria() : stopCriteria);
             return TrySolveIterative(input, result, solver, iterator, preconditioner);
         }
 
+        /// <summary>
+        /// Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
+        /// </summary>
+        /// <param name="input">The solution matrix <c>B</c>.</param>
+        /// <param name="result">The result matrix <c>X</c></param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
+        /// <param name="preconditioner">The preconditioner to use for approximations.</param>
         public IterationStatus TrySolveIterative(Matrix<T> input, Matrix<T> result, IIterativeSolver<T> solver, IPreconditioner<T> preconditioner, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var iterator = new Iterator<T>(stopCriteria.Length == 0 ? Builder.IterativeSolverStopCriteria() : stopCriteria);
             return TrySolveIterative(input, result, solver, iterator, preconditioner);
         }
 
+        /// <summary>
+        /// Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
+        /// </summary>
+        /// <param name="input">The solution vector <c>b</c>.</param>
+        /// <param name="result">The result vector <c>x</c>.</param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
         public IterationStatus TrySolveIterative(Vector<T> input, Vector<T> result, IIterativeSolver<T> solver, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var iterator = new Iterator<T>(stopCriteria.Length == 0 ? Builder.IterativeSolverStopCriteria() : stopCriteria);
             return TrySolveIterative(input, result, solver, iterator);
         }
 
+        /// <summary>
+        /// Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
+        /// </summary>
+        /// <param name="input">The solution matrix <c>B</c>.</param>
+        /// <param name="result">The result matrix <c>X</c></param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
         public IterationStatus TrySolveIterative(Matrix<T> input, Matrix<T> result, IIterativeSolver<T> solver, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var iterator = new Iterator<T>(stopCriteria.Length == 0 ? Builder.IterativeSolverStopCriteria() : stopCriteria);
@@ -272,6 +303,14 @@ namespace MathNet.Numerics.LinearAlgebra
             return result;
         }
 
+        /// <summary>
+        /// Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
+        /// </summary>
+        /// <param name="input">The solution vector <c>b</c>.</param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
+        /// <param name="preconditioner">The preconditioner to use for approximations.</param>
+        /// <returns>The result vector <c>x</c>.</returns>
         public Vector<T> SolveIterative(Vector<T> input, IIterativeSolver<T> solver, IPreconditioner<T> preconditioner, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var result = Builder.DenseVector(RowCount);
@@ -279,6 +318,14 @@ namespace MathNet.Numerics.LinearAlgebra
             return result;
         }
 
+        /// <summary>
+        /// Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
+        /// </summary>
+        /// <param name="input">The solution matrix <c>B</c>.</param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
+        /// <param name="preconditioner">The preconditioner to use for approximations.</param>
+        /// <returns>The result matrix <c>X</c>.</returns>
         public Matrix<T> SolveIterative(Matrix<T> input, IIterativeSolver<T> solver, IPreconditioner<T> preconditioner, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var result = Builder.DenseMatrix(input.RowCount, input.ColumnCount);
@@ -286,6 +333,13 @@ namespace MathNet.Numerics.LinearAlgebra
             return result;
         }
 
+        /// <summary>
+        /// Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
+        /// </summary>
+        /// <param name="input">The solution vector <c>b</c>.</param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
+        /// <returns>The result vector <c>x</c>.</returns>
         public Vector<T> SolveIterative(Vector<T> input, IIterativeSolver<T> solver, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var result = Builder.DenseVector(RowCount);
@@ -293,6 +347,13 @@ namespace MathNet.Numerics.LinearAlgebra
             return result;
         }
 
+        /// <summary>
+        /// Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
+        /// </summary>
+        /// <param name="input">The solution matrix <c>B</c>.</param>
+        /// <param name="solver">The iterative solver to use.</param>
+        /// <param name="stopCriteria">Criteria to control when to stop iterating.</param>
+        /// <returns>The result matrix <c>X</c>.</returns>
         public Matrix<T> SolveIterative(Matrix<T> input, IIterativeSolver<T> solver, params IIterationStopCriterium<T>[] stopCriteria)
         {
             var result = Builder.DenseMatrix(input.RowCount, input.ColumnCount);
diff --git a/src/Numerics/LinearAlgebra/Single/DenseMatrix.cs b/src/Numerics/LinearAlgebra/Single/DenseMatrix.cs
index 6d9ae7dd..995b3ce5 100644
--- a/src/Numerics/LinearAlgebra/Single/DenseMatrix.cs
+++ b/src/Numerics/LinearAlgebra/Single/DenseMatrix.cs
@@ -1055,7 +1055,7 @@ namespace MathNet.Numerics.LinearAlgebra.Single
             return DenseGramSchmidt.Create(this);
         }
 
-        public override Svd<float> Svd(bool computeVectors)
+        public override Svd<float> Svd(bool computeVectors = true)
         {
             return DenseSvd.Create(this, computeVectors);
         }
diff --git a/src/Numerics/LinearAlgebra/Single/Matrix.cs b/src/Numerics/LinearAlgebra/Single/Matrix.cs
index cce9f85d..2b081a82 100644
--- a/src/Numerics/LinearAlgebra/Single/Matrix.cs
+++ b/src/Numerics/LinearAlgebra/Single/Matrix.cs
@@ -479,7 +479,7 @@ namespace MathNet.Numerics.LinearAlgebra.Single
             return UserGramSchmidt.Create(this);
         }
 
-        public override Svd<float> Svd(bool computeVectors)
+        public override Svd<float> Svd(bool computeVectors = true)
         {
             return UserSvd.Create(this, computeVectors);
         }
diff --git a/src/UnitTests/LinearAlgebraTests/Complex/Factorization/SvdTests.cs b/src/UnitTests/LinearAlgebraTests/Complex/Factorization/SvdTests.cs
index 1405c1fc..afb87d8c 100644
--- a/src/UnitTests/LinearAlgebraTests/Complex/Factorization/SvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Complex/Factorization/SvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using MathNet.Numerics.LinearAlgebra.Complex;
 using NUnit.Framework;
 
@@ -48,7 +47,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = DenseMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -85,7 +84,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -103,7 +102,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -124,7 +123,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -143,7 +142,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -175,7 +174,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, Complex.Zero);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -222,14 +221,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -262,7 +261,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -273,7 +272,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -309,13 +308,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new DenseVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -354,7 +353,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -368,7 +367,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Complex/Factorization/UserSvdTests.cs b/src/UnitTests/LinearAlgebraTests/Complex/Factorization/UserSvdTests.cs
index 29635d10..5b5943a1 100644
--- a/src/UnitTests/LinearAlgebraTests/Complex/Factorization/UserSvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Complex/Factorization/UserSvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
@@ -47,7 +46,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = UserDefinedMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -84,7 +83,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -102,7 +101,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -123,7 +122,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -142,7 +141,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -174,7 +173,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, Complex.Zero);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -221,14 +220,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -261,7 +260,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -272,7 +271,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -308,13 +307,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new UserDefinedVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -353,7 +352,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -367,7 +366,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/SvdTests.cs b/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/SvdTests.cs
index 114a39dc..b9a22b4b 100644
--- a/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/SvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/SvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using MathNet.Numerics.LinearAlgebra.Complex32;
 using NUnit.Framework;
 
@@ -48,7 +47,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = DenseMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -85,7 +84,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -103,7 +102,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -125,7 +124,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -144,7 +143,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -176,7 +175,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, Complex32.Zero);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -223,14 +222,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -264,7 +263,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -275,7 +274,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -312,13 +311,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new DenseVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -358,7 +357,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -372,7 +371,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/UserSvdTests.cs b/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/UserSvdTests.cs
index 2206b465..95f99498 100644
--- a/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/UserSvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Complex32/Factorization/UserSvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
@@ -47,7 +46,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = UserDefinedMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -84,7 +83,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -102,7 +101,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -124,7 +123,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -143,7 +142,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -175,7 +174,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, Complex32.Zero);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -222,14 +221,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -263,7 +262,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -274,7 +273,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -311,13 +310,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new UserDefinedVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -357,7 +356,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -371,7 +370,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Double/Factorization/SvdTests.cs b/src/UnitTests/LinearAlgebraTests/Double/Factorization/SvdTests.cs
index ea52f329..5da7d912 100644
--- a/src/UnitTests/LinearAlgebraTests/Double/Factorization/SvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Double/Factorization/SvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using MathNet.Numerics.LinearAlgebra.Double;
 using NUnit.Framework;
 
@@ -46,7 +45,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = DenseMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -83,7 +82,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -101,7 +100,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -122,7 +121,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -141,7 +140,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -173,7 +172,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, 0);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -220,14 +219,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -260,7 +259,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -271,7 +270,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -307,13 +306,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new DenseVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -352,7 +351,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -366,7 +365,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Double/Factorization/UserSvdTests.cs b/src/UnitTests/LinearAlgebraTests/Double/Factorization/UserSvdTests.cs
index 7f8b7adc..1c02a9a7 100644
--- a/src/UnitTests/LinearAlgebraTests/Double/Factorization/UserSvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Double/Factorization/UserSvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
@@ -45,7 +44,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = UserDefinedMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -82,7 +81,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -100,7 +99,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -121,7 +120,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -140,7 +139,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -172,7 +171,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, 0);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -219,14 +218,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -259,7 +258,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -270,7 +269,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -306,13 +305,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new UserDefinedVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -351,7 +350,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -365,7 +364,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Single/Factorization/SvdTests.cs b/src/UnitTests/LinearAlgebraTests/Single/Factorization/SvdTests.cs
index 2d32f7d0..12d74b45 100644
--- a/src/UnitTests/LinearAlgebraTests/Single/Factorization/SvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Single/Factorization/SvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using MathNet.Numerics.LinearAlgebra.Single;
 using NUnit.Framework;
 
@@ -46,7 +45,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = DenseMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -83,7 +82,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -101,7 +100,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -122,7 +121,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -141,7 +140,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -173,7 +172,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, 0);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -220,14 +219,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -260,7 +259,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -271,7 +270,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -307,13 +306,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomDenseVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new DenseVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -352,7 +351,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomDenseMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -366,7 +365,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
diff --git a/src/UnitTests/LinearAlgebraTests/Single/Factorization/UserSvdTests.cs b/src/UnitTests/LinearAlgebraTests/Single/Factorization/UserSvdTests.cs
index 3eb3dba9..f89e576f 100644
--- a/src/UnitTests/LinearAlgebraTests/Single/Factorization/UserSvdTests.cs
+++ b/src/UnitTests/LinearAlgebraTests/Single/Factorization/UserSvdTests.cs
@@ -25,7 +25,6 @@
 // </copyright>
 
 using System;
-using MathNet.Numerics.LinearAlgebra;
 using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
@@ -45,7 +44,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanFactorizeIdentity(int order)
         {
             var matrixI = UserDefinedMatrix.Identity(order);
-            var factorSvd = matrixI.Svd(true);
+            var factorSvd = matrixI.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -82,7 +81,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanFactorizeRandomMatrix(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var u = factorSvd.U;
             var vt = factorSvd.VT;
             var w = factorSvd.W;
@@ -100,7 +99,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
             Assert.AreEqual(column, w.ColumnCount);
 
             // Make sure the U*W*VT is the original matrix.
-            var matrix = u * w * vt;
+            var matrix = u*w*vt;
             for (var i = 0; i < matrix.RowCount; i++)
             {
                 for (var j = 0; j < matrix.ColumnCount; j++)
@@ -121,7 +120,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanCheckRankOfNonSquare(int row, int column)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var mn = Math.Min(row, column);
             Assert.AreEqual(factorSvd.Rank, mn);
@@ -140,7 +139,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         public void CanCheckRankSquare(int order)
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(order, order);
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             if (factorSvd.Determinant != 0)
             {
@@ -172,7 +171,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
                 matrixA[i + 1, i] = 1;
             }
 
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             Assert.AreEqual(factorSvd.Determinant, 0);
             Assert.AreEqual(factorSvd.Rank, order - 1);
@@ -219,14 +218,14 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var resultx = factorSvd.Solve(vectorb);
 
             Assert.AreEqual(matrixA.ColumnCount, resultx.Count);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -259,7 +258,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixX = factorSvd.Solve(matrixB);
@@ -270,7 +269,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)
@@ -306,13 +305,13 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
             var vectorb = MatrixLoader.GenerateRandomUserDefinedVector(row);
             var vectorbCopy = vectorb.Clone();
             var resultx = new UserDefinedVector(column);
             factorSvd.Solve(vectorb, resultx);
 
-            var matrixBReconstruct = matrixA * resultx;
+            var matrixBReconstruct = matrixA*resultx;
 
             // Check the reconstruction.
             for (var i = 0; i < vectorb.Count; i++)
@@ -351,7 +350,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
         {
             var matrixA = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixACopy = matrixA.Clone();
-            var factorSvd = matrixA.Svd(true);
+            var factorSvd = matrixA.Svd();
 
             var matrixB = MatrixLoader.GenerateRandomUserDefinedMatrix(row, column);
             var matrixBCopy = matrixB.Clone();
@@ -365,7 +364,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single.Factorization
             // The solution X has the same number of columns as B
             Assert.AreEqual(matrixB.ColumnCount, matrixX.ColumnCount);
 
-            var matrixBReconstruct = matrixA * matrixX;
+            var matrixBReconstruct = matrixA*matrixX;
 
             // Check the reconstruction.
             for (var i = 0; i < matrixB.RowCount; i++)