/// <summary>
/// Outer product of two vectors; Sets <i>A[i,j] = x[i] * y[j]</i>.
/// </summary>
/// <param name="x">the first source vector.</param>
/// <param name="y">the second source vector.</param>
/// <param name="A">the matrix to hold the resultsd Set this parameter to <i>null</i> to indicate that a new result matrix shall be constructed.</param>
/// <returns>A (for convenience only).</returns>
public static DoubleMatrix2D MultOuter(DoubleMatrix1D x, DoubleMatrix1D y, DoubleMatrix2D A)
{
int rows = x.Size;
int columns = y.Size;
if (A == null)
{
A = x.Like2D(rows, columns);
}
if (A.Rows != rows || A.Columns != columns)
{
throw new ArgumentException();
}
for (int row = rows; --row >= 0;)
{
A.ViewRow(row).Assign(y);
}
for (int column = columns; --column >= 0;)
{
A.ViewColumn(column).Assign(x, BinaryFunctions.Mult);
}
return(A);
}
/// <summary>
/// Returns a string representation of the given matrix.
/// </summary>
/// <param name="matrix">
/// The matrix to convert.
/// </param>
/// <returns>
/// A string representation of the given matrix.
/// </returns>
public string ToString(DoubleMatrix1D matrix)
{
DoubleMatrix2D easy = matrix.Like2D(1, matrix.Size);
easy.ViewRow(0).Assign(matrix);
return(ToString(easy));
}
public void Dger(double alpha, DoubleMatrix1D x, DoubleMatrix1D y, DoubleMatrix2D A)
{
Cern.Jet.Math.PlusMult fun = new Cern.Jet.Math.PlusMult(0);
for (int i = A.Rows; --i >= 0;)
{
fun.Multiplicator = alpha * x[i];
A.ViewRow(i).Assign(y, fun);
}
}
public void Dswap(DoubleMatrix2D A, DoubleMatrix2D B)
{
//B.Swap(A); not yet implemented
A.CheckShape(B);
for (int i = A.Rows; --i >= 0;)
{
A.ViewRow(i).Swap(B.ViewRow(i));
}
}
/// <summary>
/// Returns a string representations of all cells; no alignment considered.
/// </summary>
/// <param name="matrix">
/// The matrix.
/// </param>
/// <returns>
/// A string representation of all cells.
/// </returns>
public string[][] Format(DoubleMatrix2D matrix)
{
var strings = new string[matrix.Rows][];
for (int row = matrix.Rows; --row >= 0;)
{
strings[row] = FormatRow(matrix.ViewRow(row));
}
return(strings);
}
/// <summary>
/// Returns the infinity norm of matrix <i>A</i>, which is the maximum absolute row sum.
/// </summary>
/// <param name="A">The matrix A.</param>
/// <returns>The infinity norm of matrix <i>A</i></returns>
public static double NormInfinity(DoubleMatrix2D A)
{
double max = 0;
for (int row = A.Rows; --row >= 0;)
{
//max = System.Math.Max(max, normInfinity(A.ViewRow(row)));
max = System.Math.Max(max, Norm1(A.ViewRow(row)));
}
return(max);
}
/// <summary>
/// Constructs and returns a new Cholesky decomposition object for a symmetric and positive definite matrix;
/// The decomposed matrices can be retrieved via instance methods of the returned decomposition object.
///
/// Return a structure to access <i>L</i> and <i>isSymmetricPositiveDefinite</i> flag.
/// </summary>
/// <param name="A">Square, symmetric matrix.</param>
/// <exception cref="ArgumentException">if <i>A</i> is not square.</exception>
public CholeskyDecomposition(DoubleMatrix2D A)
{
Property.DEFAULT.CheckSquare(A);
// Initialize.
//double[][] A = Arg.getArray();
n = A.Rows;
//L = new double[n][n];
mL = A.Like(n, n);
isSymmetricPositiveDefinite = (A.Columns == n);
//precompute and cache some views to avoid regenerating them time and again
DoubleMatrix1D[] Lrows = new DoubleMatrix1D[n];
for (int j = 0; j < n; j++)
{
Lrows[j] = mL.ViewRow(j);
}
// Main loop.
for (int j = 0; j < n; j++)
{
//double[] Lrowj = L[j];
//DoubleMatrix1D Lrowj = L.ViewRow(j);
double d = 0.0;
for (int k = 0; k < j; k++)
{
//double[] Lrowk = L[k];
double s = Lrows[k].ZDotProduct(Lrows[j], 0, k);
/*
* DoubleMatrix1D Lrowk = L.ViewRow(k);
* double s = 0.0;
* for (int i = 0; i < k; i++) {
* s += Lrowk.getQuick(i)*Lrowj.getQuick(i);
* }
*/
s = (A[j, k] - s) / mL[k, k];
Lrows[j][k] = s;
d = d + s * s;
isSymmetricPositiveDefinite = isSymmetricPositiveDefinite && (A[k, j] == A[j, k]);
}
d = A[j, j] - d;
isSymmetricPositiveDefinite = isSymmetricPositiveDefinite && (d > 0.0);
mL[j, j] = System.Math.Sqrt(System.Math.Max(d, 0.0));
for (int k = j + 1; k < n; k++)
{
mL[j, k] = 0.0;
}
}
}
public static DoubleMatrix2D PermuteRows(DoubleMatrix2D A, int[] indexes, int[] work)
{
// check validity
int size = A.Rows;
if (indexes.Length != size)
{
throw new IndexOutOfRangeException("invalid permutation");
}
/*
* int i=size;
* int a;
* while (--i >= 0 && (a=indexes[i])==i) if (a < 0 || a >= size) throw new IndexOutOfRangeException("invalid permutation");
* if (i<0) return; // nothing to permute
*/
int columns = A.Columns;
if (columns < size / 10)
{ // quicker
double[] doubleWork = new double[size];
for (int j = A.Columns; --j >= 0;)
{
Permute(A.ViewColumn(j), indexes, doubleWork);
}
return(A);
}
var swapper = new Cern.Colt.Swapper((a, b) =>
{
A.ViewRow(a).Swap(A.ViewRow(b));
}
);
Cern.Colt.GenericPermuting.permute(indexes, swapper, work, null);
return(A);
}
/// <summary>
/// Solves <i>A*X = B</i>; returns <i>X</i>.
/// </summary>
/// <param name="B">A Matrix with as many rows as <i>A</i> and any number of columns.</param>
/// <returns><i>X</i> so that <i>L*L'*X = B</i>.</returns>
/// <exception cref="ArgumentException">if <i>B.Rows != A.Rows</i>.</exception>
/// <exception cref="ArgumentException">if <i>!isSymmetricPositiveDefinite()</i>.</exception>
private DoubleMatrix2D XXXsolveBuggy(DoubleMatrix2D B)
{
var F = Cern.Jet.Math.Functions.functions;
if (B.Rows != n)
{
throw new ArgumentException(Cern.LocalizedResources.Instance().Exception_MatrixRowDimensionsMustAgree);
}
if (!isSymmetricPositiveDefinite)
{
throw new ArgumentException(Cern.LocalizedResources.Instance().Exception_MatrixIsNotSymmetricPositiveDefinite);
}
// Copy right hand side.
DoubleMatrix2D X = B.Copy();
int nx = B.Columns;
// precompute and cache some views to avoid regenerating them time and again
DoubleMatrix1D[] Xrows = new DoubleMatrix1D[n];
for (int k = 0; k < n; k++)
{
Xrows[k] = X.ViewRow(k);
}
// Solve L*Y = B;
for (int k = 0; k < n; k++)
{
for (int i = k + 1; i < n; i++)
{
// X[i,j] -= X[k,j]*L[i,k]
Xrows[i].Assign(Xrows[k], F2.MinusMult(mL[i, k]));
}
Xrows[k].Assign(F1.Div(mL[k, k]));
}
// Solve L'*X = Y;
for (int k = n - 1; k >= 0; k--)
{
Xrows[k].Assign(F1.Div(mL[k, k]));
for (int i = 0; i < k; i++)
{
// X[i,j] -= X[k,j]*L[k,i]
Xrows[i].Assign(Xrows[k], F2.MinusMult(mL[k, i]));
}
}
return(X);
}
/// <summary>
/// A matrix <i>A</i> is <i>diagonally dominant by row</i> if the absolute value of each diagonal element is larger than the sum of the absolute values of the off-diagonal elements in the corresponding row.
/// <i>returns true if for all i: abs(A[i,i]) > Sum(abs(A[i,j])); j != i.</i>
/// Matrix may but need not be square.
/// <p>
/// Note: Ignores tolerance.
/// <summary>
public Boolean IsDiagonallyDominantByRow(DoubleMatrix2D A)
{
Cern.Jet.Math.Functions F = Cern.Jet.Math.Functions.functions;
double epsilon = Tolerance;
int min = System.Math.Min(A.Rows, A.Columns);
for (int i = min; --i >= 0;)
{
double diag = System.Math.Abs(A[i, i]);
diag += diag;
if (diag <= A.ViewRow(i).Aggregate(F2.Plus, F1.Abs))
{
return(false);
}
}
return(true);
}
/// <summary>
/// Modifies the given matrix square matrix<i>A</i> such that it is diagonally dominant by row and column, hence non-singular, hence invertible.
/// For testing purposes only.
/// <summary>
///<param name = "A" > the square matrix to modify.</param>
///<exception cref = "ArgumentException" >if <i>!isSquare(A)</i>. </exception>
public void GenerateNonSingular(DoubleMatrix2D A)
{
CheckSquare(A);
var F = Cern.Jet.Math.Functions.functions;
int min = System.Math.Min(A.Rows, A.Columns);
for (int i = min; --i >= 0;)
{
A[i, i] = 0;
}
for (int i = min; --i >= 0;)
{
double rowSum = A.ViewRow(i).Aggregate(F2.Plus, F1.Abs);
double colSum = A.ViewColumn(i).Aggregate(F2.Plus, F1.Abs);
A[i, i] = System.Math.Max(rowSum, colSum) + i + 1;
}
}
/// <summary>
/// Copies the columns of the indicated rows into a new sub matrix.
/// <i>sub[0..rowIndexes.Length-1,0..columnTo-columnFrom] = A[rowIndexes(:),columnFrom..columnTo]</i>;
/// The returned matrix is <i>not backed</i> by this matrix, so changes in the returned matrix are <i>not reflected</i> in this matrix, and vice-versa.
/// </summary>
/// <param name="A">the source matrix to copy from.</param>
/// <param name="rowIndexes">the indexes of the rows to copyd May be unsorted.</param>
/// <param name="columnFrom">the index of the first column to copy (inclusive).</param>
/// <param name="columnTo">the index of the last column to copy (inclusive).</param>
/// <returns>a new sub matrix; with <i>sub.Rows==rowIndexes.Length; sub.Columns==columnTo-columnFrom+1</i>.</returns>
/// <exception cref="IndexOutOfRangeException">if <i>columnFrom < 0 || columnTo-columnFrom+1 < 0 || columnTo+1>matrix.Columns || for any row=rowIndexes[i]: row < 0 || row >= matrix.Rows</i>.</exception>
private static DoubleMatrix2D SubMatrix(DoubleMatrix2D A, int[] rowIndexes, int columnFrom, int columnTo)
{
int width = columnTo - columnFrom + 1;
int rows = A.Rows;
A = A.ViewPart(0, columnFrom, rows, width);
DoubleMatrix2D sub = A.Like(rowIndexes.Length, width);
for (int r = rowIndexes.Length; --r >= 0;)
{
int row = rowIndexes[r];
if (row < 0 || row >= rows)
{
throw new IndexOutOfRangeException("Illegal Index");
}
sub.ViewRow(r).Assign(A.ViewRow(row));
}
return(sub);
}
/// <summary>
/// Sorts the matrix rows according to the order induced by the specified comparator.
/// </summary>
/// <param name="matrix">
/// The matrix to be sorted.
/// </param>
/// <param name="c">
/// The comparator to determine the order.
/// </param>
/// <returns>
/// A new matrix view having rows sorted as specified.
/// </returns>
public DoubleMatrix2D Sort(DoubleMatrix2D matrix, DoubleMatrix1DComparator c)
{
var rowIndexes = new int[matrix.Rows]; // row indexes to reorder instead of matrix itself
for (int i = rowIndexes.Length; --i >= 0;)
{
rowIndexes[i] = i;
}
var views = new DoubleMatrix1D[matrix.Rows]; // precompute views for speed
for (int i = views.Length; --i >= 0;)
{
views[i] = matrix.ViewRow(i);
}
RunSort(rowIndexes, 0, rowIndexes.Length, (a, b) => c(views[a], views[b]));
// view the matrix according to the reordered row indexes
// take all columns in the original order
return(matrix.ViewSelection(rowIndexes, null));
}
public void Decompose(DoubleMatrix2D A)
{
int CUT_OFF = 10;
// setup
LU = A;
int m = A.Rows;
int n = A.Columns;
// setup pivot vector
if (this.piv == null || this.piv.Length != m)
{
this.piv = new int[m];
}
for (int i = m; --i >= 0;)
{
piv[i] = i;
}
pivsign = 1;
if (m * n == 0)
{
LU = LU;
return; // nothing to do
}
//precompute and cache some views to avoid regenerating them time and again
DoubleMatrix1D[] LUrows = new DoubleMatrix1D[m];
for (int i = 0; i < m; i++)
{
LUrows[i] = LU.ViewRow(i);
}
IntArrayList nonZeroIndexes = new IntArrayList(); // sparsity
DoubleMatrix1D LUcolj = LU.ViewColumn(0).Like(); // blocked column j
Cern.Jet.Math.Mult multFunction = Cern.Jet.Math.Mult.CreateInstance(0);
// Outer loop.
for (int j = 0; j < n; j++)
{
// blocking (make copy of j-th column to localize references)
LUcolj.Assign(LU.ViewColumn(j));
// sparsity detection
int maxCardinality = m / CUT_OFF; // == heuristic depending on speedup
LUcolj.GetNonZeros(nonZeroIndexes, null, maxCardinality);
int cardinality = nonZeroIndexes.Count;
Boolean sparse = (cardinality < maxCardinality);
// Apply previous transformations.
for (int i = 0; i < m; i++)
{
int kmax = System.Math.Min(i, j);
double s;
if (sparse)
{
s = LUrows[i].ZDotProduct(LUcolj, 0, kmax, nonZeroIndexes);
}
else
{
s = LUrows[i].ZDotProduct(LUcolj, 0, kmax);
}
double before = LUcolj[i];
double after = before - s;
LUcolj[i] = after; // LUcolj is a copy
LU[i, j] = after; // this is the original
if (sparse)
{
if (before == 0 && after != 0)
{ // nasty bug fixed!
int pos = nonZeroIndexes.BinarySearch(i);
pos = -pos - 1;
nonZeroIndexes.Insert(pos, i);
}
if (before != 0 && after == 0)
{
nonZeroIndexes.Remove(nonZeroIndexes.BinarySearch(i));
}
}
}
// Find pivot and exchange if necessary.
int p = j;
if (p < m)
{
double max = System.Math.Abs(LUcolj[p]);
for (int i = j + 1; i < m; i++)
{
double v = System.Math.Abs(LUcolj[i]);
if (v > max)
{
p = i;
max = v;
}
}
}
if (p != j)
{
LUrows[p].Swap(LUrows[j]);
int k = piv[p]; piv[p] = piv[j]; piv[j] = k;
pivsign = -pivsign;
}
// Compute multipliers.
double jj;
if (j < m && (jj = LU[j, j]) != 0.0)
{
multFunction.Multiplicator = 1 / jj;
LU.ViewColumn(j).ViewPart(j + 1, m - (j + 1)).Assign(multFunction);
}
}
LU = LU;
}
public void Solve(DoubleMatrix2D B)
{
int CUT_OFF = 10;
//algebra.property().checkRectangular(LU);
int m = M;
int n = N;
if (B.Rows != m)
{
throw new ArgumentException("Matrix row dimensions must agree.");
}
if (!this.IsNonsingular)
{
throw new ArgumentException("Matrix is singular.");
}
// right hand side with pivoting
// Matrix Xmat = B.getMatrix(piv,0,nx-1);
if (this.work1 == null || this.work1.Length < m)
{
this.work1 = new int[m];
}
//if (this.work2 == null || this.work2.Length < m) this.work2 = new int[m];
Algebra.PermuteRows(B, this.piv, this.work1);
if (m * n == 0)
{
return; // nothing to do
}
int nx = B.Columns;
//precompute and cache some views to avoid regenerating them time and again
DoubleMatrix1D[] Brows = new DoubleMatrix1D[n];
for (int k = 0; k < n; k++)
{
Brows[k] = B.ViewRow(k);
}
// transformations
Cern.Jet.Math.Mult div = Cern.Jet.Math.Mult.Div(0);
Cern.Jet.Math.PlusMult minusMult = Cern.Jet.Math.PlusMult.MinusMult(0);
IntArrayList nonZeroIndexes = new IntArrayList(); // sparsity
DoubleMatrix1D Browk = Cern.Colt.Matrix.DoubleFactory1D.Dense.Make(nx); // blocked row k
// Solve L*Y = B(piv,:)
for (int k = 0; k < n; k++)
{
// blocking (make copy of k-th row to localize references)
Browk.Assign(Brows[k]);
// sparsity detection
int maxCardinality = nx / CUT_OFF; // == heuristic depending on speedup
Browk.GetNonZeros(nonZeroIndexes, null, maxCardinality);
int cardinality = nonZeroIndexes.Count;
Boolean sparse = (cardinality < maxCardinality);
for (int i = k + 1; i < n; i++)
{
//for (int j = 0; j < nx; j++) B[i][j] -= B[k][j]*LU[i][k];
//for (int j = 0; j < nx; j++) B.set(i,j, B.Get(i,j) - B.Get(k,j)*LU.Get(i,k));
minusMult.Multiplicator = -LU[i, k];
if (minusMult.Multiplicator != 0)
{
if (sparse)
{
Brows[i].Assign(Browk, minusMult, nonZeroIndexes);
}
else
{
Brows[i].Assign(Browk, minusMult);
}
}
}
}
// Solve U*B = Y;
for (int k = n - 1; k >= 0; k--)
{
// for (int j = 0; j < nx; j++) B[k][j] /= LU[k][k];
// for (int j = 0; j < nx; j++) B.set(k,j, B.Get(k,j) / LU.Get(k,k));
div.Multiplicator = 1 / LU[k, k];
Brows[k].Assign(div);
// blocking
if (Browk == null)
{
Browk = Cern.Colt.Matrix.DoubleFactory1D.Dense.Make(B.Columns);
}
Browk.Assign(Brows[k]);
// sparsity detection
int maxCardinality = nx / CUT_OFF; // == heuristic depending on speedup
Browk.GetNonZeros(nonZeroIndexes, null, maxCardinality);
int cardinality = nonZeroIndexes.Count;
Boolean sparse = (cardinality < maxCardinality);
//Browk.GetNonZeros(nonZeroIndexes,null);
//Boolean sparse = nonZeroIndexes.Count < nx/10;
for (int i = 0; i < k; i++)
{
// for (int j = 0; j < nx; j++) B[i][j] -= B[k][j]*LU[i][k];
// for (int j = 0; j < nx; j++) B.set(i,j, B.Get(i,j) - B.Get(k,j)*LU.Get(i,k));
minusMult.Multiplicator = -LU[i, k];
if (minusMult.Multiplicator != 0)
{
if (sparse)
{
Brows[i].Assign(Browk, minusMult, nonZeroIndexes);
}
else
{
Brows[i].Assign(Browk, minusMult);
}
}
}
}
}
public override DoubleMatrix2D ZMult(DoubleMatrix2D B, DoubleMatrix2D C, double alpha, double beta, Boolean transposeA, Boolean transposeB)
{
if (transposeB)
{
B = B.ViewDice();
}
int m = Rows;
int n = Columns;
if (transposeA)
{
m = Columns;
n = Rows;
}
int p = B.Columns;
Boolean ignore = (C == null);
if (C == null)
{
C = new DenseDoubleMatrix2D(m, p);
}
if (B.Rows != n)
{
throw new ArgumentException(String.Format(Cern.LocalizedResources.Instance().Exception_Matrix2DInnerDimensionMustAgree, ToStringShort(), (transposeB ? B.ViewDice() : B).ToStringShort()));
}
if (C.Rows != m || C.Columns != p)
{
throw new ArgumentException(String.Format(Cern.LocalizedResources.Instance().Exception_IncompatibleResultMatrix, ToStringShort(), (transposeB ? B.ViewDice() : B).ToStringShort(), C.ToStringShort()));
}
if (this == C || B == C)
{
throw new ArgumentException(Cern.LocalizedResources.Instance().Exception_MatricesMustNotBeIdentical);
}
if (!ignore)
{
C.Assign(F1.Mult(beta));
}
// cache views
DoubleMatrix1D[] Brows = new DoubleMatrix1D[n];
for (int i = n; --i >= 0;)
{
Brows[i] = B.ViewRow(i);
}
DoubleMatrix1D[] Crows = new DoubleMatrix1D[m];
for (int i = m; --i >= 0;)
{
Crows[i] = C.ViewRow(i);
}
ForEachNonZero(
new Cern.Colt.Function.IntIntDoubleFunction((i, j, value) =>
{
var fun = F2.PlusMult(value * alpha);
//fun.multiplicator = value * alpha;
if (!transposeA)
{
Crows[i].Assign(Brows[j], fun);
}
else
{
Crows[j].Assign(Brows[i], fun);
}
return(value);
}
));
return(C);
}
public override DoubleMatrix2D ZMult(DoubleMatrix2D B, DoubleMatrix2D C, double alpha, double beta, Boolean transposeA, Boolean transposeB)
{
if (transposeB)
{
B = B.ViewDice();
}
int m = Rows;
int n = Columns;
if (transposeA)
{
m = Columns;
n = Rows;
}
int p = B.Columns;
Boolean ignore = (C == null);
if (C == null)
{
C = new DenseDoubleMatrix2D(m, p);
}
if (B.Rows != n)
{
throw new ArgumentException("Matrix2D inner dimensions must agree:" + ToStringShort() + ", " + (transposeB ? B.ViewDice() : B).ToStringShort());
}
if (C.Rows != m || C.Columns != p)
{
throw new ArgumentException("Incompatibel result matrix: " + ToStringShort() + ", " + (transposeB ? B.ViewDice() : B).ToStringShort() + ", " + C.ToStringShort());
}
if (this == C || B == C)
{
throw new ArgumentException("Matrices must not be identical");
}
if (!ignore)
{
C.Assign(F1.Mult(beta));
}
// cache views
DoubleMatrix1D[] Brows = new DoubleMatrix1D[n];
for (int i = n; --i >= 0;)
{
Brows[i] = B.ViewRow(i);
}
DoubleMatrix1D[] Crows = new DoubleMatrix1D[m];
for (int i = m; --i >= 0;)
{
Crows[i] = C.ViewRow(i);
}
ForEachNonZero(
new Cern.Colt.Function.IntIntDoubleFunction((i, j, value) =>
{
var fun = F2.PlusMult(value * alpha);
//fun.multiplicator = value * alpha;
if (!transposeA)
{
Crows[i].Assign(Brows[j], fun);
}
else
{
Crows[j].Assign(Brows[i], fun);
}
return(value);
}
));
return(C);
}
public override DoubleMatrix2D ZMult(DoubleMatrix2D B, DoubleMatrix2D C, double alpha, double beta, Boolean transposeA, Boolean transposeB)
{
if (transposeB)
{
B = B.ViewDice();
}
int m = Rows;
int n = Columns;
if (transposeA)
{
m = Columns;
n = Rows;
}
int p = B.Columns;
Boolean ignore = (C == null);
if (C == null)
{
C = new DenseDoubleMatrix2D(m, p);
}
if (B.Rows != n)
{
throw new ArgumentException(String.Format(Cern.LocalizedResources.Instance().Exception_Matrix2DInnerDimensionMustAgree, ToStringShort(), (transposeB ? B.ViewDice() : B).ToStringShort()));
}
if (C.Rows != m || C.Columns != p)
{
throw new ArgumentException(String.Format(Cern.LocalizedResources.Instance().Exception_IncompatibleResultMatrix, ToStringShort(), (transposeB ? B.ViewDice() : B).ToStringShort(), C.ToStringShort()));
}
if (this == C || B == C)
{
throw new ArgumentException(Cern.LocalizedResources.Instance().Exception_MatricesMustNotBeIdentical);
}
if (!ignore)
{
C.Assign(F1.Mult(beta));
}
// cache views
DoubleMatrix1D[] Brows = new DoubleMatrix1D[n];
for (int i = n; --i >= 0;)
{
Brows[i] = B.ViewRow(i);
}
DoubleMatrix1D[] Crows = new DoubleMatrix1D[m];
for (int i = m; --i >= 0;)
{
Crows[i] = C.ViewRow(i);
}
int[] idx = Indexes.ToArray();
double[] vals = Values.ToArray();
for (int i = Starts.Length - 1; --i >= 0;)
{
int low = Starts[i];
for (int k = Starts[i + 1]; --k >= low;)
{
int j = idx[k];
var fun = F2.PlusMult(vals[k] * alpha);
//fun.Multiplicator = vals[k] * alpha;
if (!transposeA)
{
Crows[i].Assign(Brows[j], fun);
}
else
{
Crows[j].Assign(Brows[i], fun);
}
}
}
return(C);
}
