/// <summary>
/// See <see cref="IMatrixView.LinearCombination(double, IMatrixView, double)"/>.
/// </summary>
public IMatrix LinearCombination(double thisCoefficient, IMatrixView otherMatrix, double otherCoefficient)
{
if (otherMatrix is CscMatrix otherCSC) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherCSC))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] resultValues = new double[values.Length];
if (thisCoefficient == 1.0)
{
Array.Copy(this.values, resultValues, values.Length);
Blas.Daxpy(values.Length, otherCoefficient, otherCSC.values, 0, 1, this.values, 0, 1);
}
else if (otherCoefficient == 1.0)
{
Array.Copy(otherCSC.values, resultValues, values.Length);
Blas.Daxpy(values.Length, thisCoefficient, this.values, 0, 1, resultValues, 0, 1);
}
else
{
Array.Copy(this.values, resultValues, values.Length);
BlasExtensions.Daxpby(values.Length, otherCoefficient, otherCSC.values, 0, 1,
thisCoefficient, resultValues, 0, 1);
}
return(new CscMatrix(NumRows, NumColumns, resultValues, this.rowIndices, this.colOffsets));
}
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this matrix)
return(DenseStrategies.LinearCombination(this, thisCoefficient, otherMatrix, otherCoefficient));
}
/// <summary>
/// See <see cref="IVectorView.LinearCombination(double, IVectorView, double)"/>.
/// </summary>
public IVector LinearCombination(double thisCoefficient, IVectorView otherVector, double otherCoefficient)
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector otherSparse) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherSparse))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] result = new double[this.values.Length];
if (thisCoefficient == 1.0)
{
Array.Copy(this.values, result, this.values.Length);
Blas.Daxpy(values.Length, otherCoefficient, otherSparse.values, 0, 1, result, 0, 1);
}
else if (otherCoefficient == 1.0)
{
Array.Copy(otherSparse.values, result, values.Length);
Blas.Daxpy(values.Length, thisCoefficient, this.values, 0, 1, result, 0, 1);
}
else
{
Array.Copy(this.values, result, this.values.Length);
BlasExtensions.Daxpby(values.Length, otherCoefficient, otherSparse.values, 0, 1,
thisCoefficient, result, 0, 1);
}
return(new SparseVector(Length, result, indices));
}
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this vector)
return(DenseStrategies.LinearCombination(this, thisCoefficient, otherVector, otherCoefficient));
}
/// <summary>
/// See <see cref="IVectorView.Axpy(IVectorView, double)"/>.
/// </summary>
public IVector Axpy(IVectorView otherVector, double otherCoefficient)
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector otherSparse) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherSparse))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] result = new double[this.values.Length];
Array.Copy(this.values, result, this.values.Length);
Blas.Daxpy(values.Length, otherCoefficient, otherSparse.values, 0, 1, result, 0, 1);
return(new SparseVector(Length, result, indices));
}
}
else if (otherVector is Vector otherDense)
{
double[] result = otherDense.Scale(otherCoefficient).RawData;
SparseBlas.Daxpyi(this.indices.Length, 1.0, this.values, this.indices, 0, result, 0);
return(Vector.CreateFromArray(result, false));
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this vector)
return(DenseStrategies.LinearCombination(this, 1.0, otherVector, otherCoefficient));
}
public IMatrix LinearCombination(double thisCoefficient, IMatrixView otherMatrix, double otherCoefficient) => DenseStrategies.LinearCombination(this, thisCoefficient, otherMatrix, otherCoefficient);
