/// <summary>
/// See <see cref="IVector.AxpyIntoThis(IVectorView, double)"/>.
/// </summary>
public void AxpyIntoThis(IVectorView otherVector, double otherCoefficient)
{
if (otherVector is Vector dense)
{
AxpyIntoThis(dense, otherCoefficient);
}
else
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector sparse)
{
if (sparse.RawIndices.Length != 0)
{
SparseBlas.Daxpyi(sparse.RawIndices.Length, otherCoefficient, sparse.RawValues,
sparse.RawIndices, 0, data, 0);
}
}
else
{
for (int i = 0; i < Length; ++i)
{
this.data[i] += otherCoefficient * otherVector[i];
}
}
}
}
/// <summary>
/// See <see cref="IVectorView.Axpy(IVectorView, double)"/>.
/// </summary>
public IVector Axpy(IVectorView otherVector, double otherCoefficient)
{
if (otherVector is Vector dense)
{
return(Axpy(dense, otherCoefficient));
}
else if (otherVector is SparseVector sparse)
{
Preconditions.CheckVectorDimensions(this, otherVector);
double[] result = new double[data.Length];
Array.Copy(data, result, data.Length);
SparseBlas.Daxpyi(sparse.RawIndices.Length, otherCoefficient, sparse.RawValues,
sparse.RawIndices, 0, result, 0);
return(Vector.CreateFromArray(result, false));
}
else
{
return(otherVector.LinearCombination(otherCoefficient, this, 1.0)); // To avoid accessing zero entries
}
}
/// <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));
}
/// <summary>
/// See <see cref="IVector.AxpyIntoThis(IVectorView, double)"/>.
/// </summary>
public void AxpyIntoThis(IVectorView otherVector, double otherCoefficient)
{
if (otherVector is Vector dense)
{
AxpyIntoThis(dense, otherCoefficient);
}
else
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector sparse)
{
//TODO: should I check whether the sparse vector is all 0, in order to avoid the BLAS call?
SparseBlas.Daxpyi(sparse.RawIndices.Length, otherCoefficient, sparse.RawValues,
sparse.RawIndices, 0, data, 0);
}
else
{
for (int i = 0; i < Length; ++i)
{
this.data[i] += otherCoefficient * otherVector[i];
}
}
}
}