dsyr
Perform the symmetric rank 1 operation
A = α*x*x^T + A
.
Usage
var dsyr = require( '@stdlib/blas/base/dsyr' );
dsyr( order, uplo, N, α, x, sx, A, LDA )
Performs the symmetric rank 1 operation A = α*x*x^T + A
where α
is a scalar, x
is an N
element vector, and A
is an N
by N
symmetric matrix.
var Float64Array = require( '@stdlib/array/float64' );
var A = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
dsyr( 'row-major', 'upper', 3, 1.0, x, 1, A, 3 );
// A => <Float64Array>[ 2.0, 4.0, 6.0, 0.0, 5.0, 8.0, 0.0, 0.0, 10.0 ]
The function has the following parameters:
- order: storage layout.
- uplo: specifies whether the upper or lower triangular part of the symmetric matrix
A
should be referenced. - N: number of elements along each dimension of
A
. - α: scalar constant.
- x: input
Float64Array
. - sx: index increment for
x
. - A: input matrix stored in linear memory as a
Float64Array
. - lda: stride of the first dimension of
A
(a.k.a., leading dimension of the matrixA
).
The stride parameters determine how elements in the input arrays are accessed at runtime. For example, to iterate over every other element of x
in reverse order,
var Float64Array = require( '@stdlib/array/float64' );
var A = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
dsyr( 'row-major', 'upper', 3, 1.0, x, -2, A, 3 );
// A => <Float64Array>[ 26.0, 17.0, 8.0, 0.0, 10.0, 5.0, 0.0, 0.0, 2.0 ]
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float64Array = require( '@stdlib/array/float64' );
// Initial arrays...
var x0 = new Float64Array( [ 1.0, 1.0, 1.0, 1.0 ] );
var A = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
dsyr( 'row-major', 'upper', 3, 1.0, x1, -1, A, 3 );
// A => <Float64Array>[ 2.0, 3.0, 4.0, 0.0, 2.0, 3.0, 0.0, 0.0, 2.0 ]
dsyr.ndarray( uplo, N, α, x, sx, ox, A, sa1, sa2, oa )
Performs the symmetric rank 1 operation A = α*x*x^T + A
, using alternative indexing semantics and where α
is a scalar, x
is an N
element vector, and A
is an N
by N
symmetric matrix.
var Float64Array = require( '@stdlib/array/float64' );
var A = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
dsyr.ndarray( 'upper', 3, 1.0, x, 1, 0, A, 3, 1, 0 );
// A => <Float64Array>[ 2.0, 4.0, 6.0, 0.0, 5.0, 8.0, 0.0, 0.0, 10.0 ]
The function has the following additional parameters:
- ox: starting index for
x
. - sa1: stride of the first dimension of
A
. - sa2: stride of the second dimension of
A
. - oa: starting index for
A
.
While typed array
views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,
var Float64Array = require( '@stdlib/array/float64' );
var A = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
dsyr.ndarray( 'upper', 3, 1.0, x, -2, 4, A, 3, 1, 0 );
// A => <Float64Array>[ 26.0, 17.0, 8.0, 0.0, 10.0, 5.0, 0.0, 0.0, 2.0 ]
Notes
Examples
var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
var ones = require( '@stdlib/array/ones' );
var dsyr = require( '@stdlib/blas/base/dsyr' );
var opts = {
'dtype': 'float64'
};
var N = 3;
var A = ones( N*N, opts.dtype );
var x = discreteUniform( N, -10.0, 10.0, opts );
dsyr( 'row-major', 'upper', 3, 1.0, x, 1, A, 3 );
console.log( A );
C APIs
Usage
TODO
TODO
TODO.
TODO
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Examples
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