ssyr
Perform the symmetric rank 1 operation
A = α*x*x**T + A.
Usage
var ssyr = require( '@stdlib/blas/base/ssyr' );
ssyr( 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 Float32Array = require( '@stdlib/array/float32' );
var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float32Array( [ 1.0, 2.0, 3.0 ] );
ssyr( 'row-major', 'upper', 3, 1.0, x, 1, A, 3 );
// A => <Float32Array>[ 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
Ashould be referenced. - N: number of elements along each dimension of
A. - α: scalar constant.
- x: input
Float32Array. - sx: index increment for
x. - A: input matrix stored in linear memory as a
Float32Array. - 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 Float32Array = require( '@stdlib/array/float32' );
var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
ssyr( 'row-major', 'upper', 3, 1.0, x, -2, A, 3 );
// A => <Float32Array>[ 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 Float32Array = require( '@stdlib/array/float32' );
// Initial arrays...
var x0 = new Float32Array( [ 1.0, 1.0, 1.0, 1.0 ] );
var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
// Create offset views...
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
ssyr( 'row-major', 'upper', 3, 1.0, x1, -1, A, 3 );
// A => <Float32Array>[ 2.0, 3.0, 4.0, 0.0, 2.0, 3.0, 0.0, 0.0, 2.0 ]
ssyr.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 Float32Array = require( '@stdlib/array/float32' );
var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float32Array( [ 1.0, 2.0, 3.0 ] );
ssyr.ndarray( 'upper', 3, 1.0, x, 1, 0, A, 3, 1, 0 );
// A => <Float32Array>[ 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 Float32Array = require( '@stdlib/array/float32' );
var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
ssyr.ndarray( 'upper', 3, 1.0, x, -2, 4, A, 3, 1, 0 );
// A => <Float32Array>[ 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 ssyr = require( '@stdlib/blas/base/ssyr' );
var opts = {
'dtype': 'float32'
};
var N = 3;
var A = ones( N*N, opts.dtype );
var x = discreteUniform( N, -10.0, 10.0, opts );
ssyr( 'row-major', 'upper', 3, 1.0, x, 1, A, 3 );
console.log( A );
C APIs
Usage
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Examples
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