# Wiki

Clone wiki# blaze / Adaptors

## General Concepts

Adaptors act as wrappers around the general Matrix Types. They adapt the interface of the matrices such that certain invariants are preserved. Due to this adaptors can provide a compile time guarantee of certain properties, which can be exploited for optimized performance.

The **Blaze** library provides a total of 9 different adaptors:

In combination with the general matrix types, **Blaze** provides a total of 40 different matrix types that make it possible to exactly adapt the type of matrix to every specific problem.

## Examples

The following code examples give an impression on the use of adaptors. The first example shows the multiplication between two lower matrices:

```
using blaze::DynamicMatrix;
using blaze::LowerMatrix;
using blaze::rowMajor;
using blaze::columnMajor;
LowerMatrix< DynamicMatrix<double,rowMajor> > A;
LowerMatrix< DynamicMatrix<double,columnMajor> > B;
DynamicMatrix<double,columnMajor> C;
// ... Resizing and initialization
C = A * B;
```

When multiplying two matrices, at least one of which is triangular, **Blaze** can exploit the fact that either the lower or upper part of the matrix contains only default elements and restrict the algorithm to the non-zero elements. Thus the adaptor provides a significant performance advantage in comparison to a general matrix multiplication, especially for large matrices.

The second example shows the `SymmetricMatrix`

adaptor in a row-major dense matrix/sparse vector multiplication:

```
using blaze::DynamicMatrix;
using blaze::DynamicVector;
using blaze::CompressedVector;
using blaze::rowMajor;
using blaze::columnVector;
SymmetricMatrix< DynamicMatrix<double,rowMajor> > A;
CompressedVector<double,columnVector> x;
DynamicVector<double,columnVector> y;
// ... Resizing and initialization
y = A * x;
```

In this example it is not intuitively apparent that using a row-major matrix is not the best possible choice in terms of performance since the computation cannot be vectorized. Choosing a column-major matrix instead, however, would enable a vectorized computation. Therefore **Blaze** exploits the fact that `A`

is symmetric, selects the best suited storage order and evaluates the multiplication as

```
y = trans( A ) * x;
```

which significantly increases the performance.

Previous: Matrix Operations ---- Next: Symmetric Matrices

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