public void CanCheckRankOfSquareSingular(int order) { var matrixA = new DenseMatrix(order, order); matrixA[0, 0] = 1; matrixA[order - 1, order - 1] = 1; for (var i = 1; i < order - 1; i++) { matrixA[i, i - 1] = 1; matrixA[i, i + 1] = 1; matrixA[i - 1, i] = 1; matrixA[i + 1, i] = 1; } var factorSvd = matrixA.Svd(); Assert.AreEqual(factorSvd.Determinant, 0); Assert.AreEqual(factorSvd.Rank, order - 1); }
/// <summary> /// Run example /// </summary> /// <seealso cref="http://en.wikipedia.org/wiki/Singular_value_decomposition">SVD decomposition</seealso> public void Run() { // Format matrix output to console var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone(); formatProvider.TextInfo.ListSeparator = " "; // Create square matrix var matrix = new DenseMatrix(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); Console.WriteLine(@"Perform full SVD decomposition"); // 1. Left singular vectors Console.WriteLine(@"1. Left singular vectors"); Console.WriteLine(svd.U().ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 2. Singular values as vector Console.WriteLine(@"2. Singular values as vector"); Console.WriteLine(svd.S().ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 3. Singular values as diagonal matrix Console.WriteLine(@"3. Singular values as diagonal matrix"); Console.WriteLine(svd.W().ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 4. Right singular vectors Console.WriteLine(@"4. Right singular vectors"); Console.WriteLine(svd.VT().ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 5. Multiply U matrix by its 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(); // 6. Multiply V matrix by its transpose identinty = svd.VT().TransposeAndMultiply(svd.VT()); Console.WriteLine(@"6. Multiply V matrix by its transpose"); Console.WriteLine(identinty.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 7. Reconstruct initial matrix: A = U*Σ*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(); // 8. Condition Number of the matrix Console.WriteLine(@"8. Condition Number of the matrix"); Console.WriteLine(svd.ConditionNumber); Console.WriteLine(); // 9. Determinant of the matrix Console.WriteLine(@"9. Determinant of the matrix"); Console.WriteLine(svd.Determinant); Console.WriteLine(); // 10. 2-norm of the matrix Console.WriteLine(@"10. 2-norm of the matrix"); Console.WriteLine(svd.Norm2); Console.WriteLine(); // 11. Rank of the matrix Console.WriteLine(@"11. Rank of the matrix"); Console.WriteLine(svd.Rank); Console.WriteLine(); // Perform partial SVD decomposition, without computing the singular U and VT vectors svd = matrix.Svd(false); Console.WriteLine(@"Perform partial SVD decomposition, without computing the singular U and VT vectors"); // 12. Singular values as vector Console.WriteLine(@"12. Singular values as vector"); Console.WriteLine(svd.S().ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 13. Singular values as diagonal matrix Console.WriteLine(@"13. Singular values as diagonal matrix"); Console.WriteLine(svd.W().ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 14. Access to left singular vectors when partial SVD decomposition was performed try { Console.WriteLine(@"14. Access to left singular vectors when partial SVD decomposition was performed"); Console.WriteLine(svd.U().ToString("#0.00\t", formatProvider)); } catch (Exception ex) { Console.WriteLine(ex.Message); Console.WriteLine(); } // 15. Access to right singular vectors when partial SVD decomposition was performed try { Console.WriteLine(@"15. Access to right singular vectors when partial SVD decomposition was performed"); Console.WriteLine(svd.VT().ToString("#0.00\t", formatProvider)); } catch (Exception ex) { Console.WriteLine(ex.Message); Console.WriteLine(); } }
/// <summary> /// Run example /// </summary> public void Run() { // Format matrix output to console var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone(); formatProvider.TextInfo.ListSeparator = " "; // Solve next system of linear equations (Ax=b): // 5*x + 2*y - 4*z = -7 // 3*x - 7*y + 6*z = 38 // 4*x + 1*y + 5*z = 43 // Create matrix "A" with coefficients var matrixA = new DenseMatrix(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 }); Console.WriteLine(@"Vector 'b' with the constant terms"); Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 1. Solve linear equations using LU decomposition var resultX = matrixA.LU().Solve(vectorB); Console.WriteLine(@"1. Solution using LU decomposition"); Console.WriteLine(resultX.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 2. Solve linear equations using QR decomposition resultX = matrixA.QR().Solve(vectorB); Console.WriteLine(@"2. Solution using QR decomposition"); Console.WriteLine(resultX.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 3. Solve linear equations using SVD decomposition matrixA.Svd(true).Solve(vectorB, resultX); Console.WriteLine(@"3. Solution using SVD decomposition"); Console.WriteLine(resultX.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 4. Solve linear equations using Gram-Shmidt decomposition matrixA.GramSchmidt().Solve(vectorB, resultX); Console.WriteLine(@"4. Solution using Gram-Shmidt decomposition"); Console.WriteLine(resultX.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 5. Verify result. Multiply coefficient matrix "A" by result vector "x" 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(); // To use Cholesky or Eigenvalue decomposition coefficient matrix must be // symmetric (for Evd and Cholesky) and positive definite (for Cholesky) // Multipy matrix "A" by its transpose - the result will be symmetric and positive definite matrix var newMatrixA = matrixA.TransposeAndMultiply(matrixA); Console.WriteLine(@"Symmetric positive definite matrix"); Console.WriteLine(newMatrixA.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 6. Solve linear equations using Cholesky decomposition newMatrixA.Cholesky().Solve(vectorB, resultX); Console.WriteLine(@"6. Solution using Cholesky decomposition"); Console.WriteLine(resultX.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 7. Solve linear equations using eigen value decomposition newMatrixA.Evd().Solve(vectorB, resultX); Console.WriteLine(@"7. Solution using eigen value decomposition"); Console.WriteLine(resultX.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 8. Verify result. Multiply new coefficient matrix "A" by result vector "x" 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(); }