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* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
var getStride = require( '@stdlib/ndarray/base/stride' );
var getOffset = require( '@stdlib/ndarray/base/offset' );
var getData = require( '@stdlib/ndarray/base/data-buffer' );
var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
var strided = require( '@stdlib/blas/base/gsyr' ).ndarray;
// MAIN //
/**
* Performs the symmetric rank 1 operation `A = alpha*x*x^T + A`, where `alpha` is a scalar, `x` is a one-dimensional ndarray, and `A` is an `N` by `N` symmetric matrix.
*
* ## Notes
*
* - The function expects the following ndarrays:
*
* - a one-dimensional input ndarray corresponding to `x`.
* - a two-dimensional input ndarray corresponding to `A`.
* - a zero-dimensional ndarray specifying whether the upper or lower triangular part of the symmetric matrix `A` should be referenced.
* - a zero-dimensional ndarray containing a scalar constant corresponding to `alpha`.
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @returns {Object} output ndarray
*
* @example
* var matrix = require( '@stdlib/ndarray/matrix/ctor' );
* var vector = require( '@stdlib/ndarray/vector/ctor' );
* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
* var resolveEnum = require( '@stdlib/blas/base/matrix-triangle-resolve-enum' );
*
* var x = vector( [ 1.0, 2.0, 3.0 ], 'generic' );
* var A = matrix( [ [ 1.0, 2.0, 3.0 ], [ 2.0, 1.0, 2.0 ], [ 3.0, 2.0, 1.0 ] ], 'generic' );
*
* var uplo = scalar2ndarray( resolveEnum( 'upper' ), {
* 'dtype': 'int8'
* });
* var alpha = scalar2ndarray( 2.0, {
* 'dtype': 'generic'
* });
*
* var out = gsyr( [ x, A, uplo, alpha ] );
* // returns <ndarray>[ [ 3.0, 6.0, 9.0 ], [ 2.0, 9.0, 14.0 ], [ 3.0, 2.0, 19.0 ] ]
*
* var bool = ( out === A );
* // returns true
*/
function gsyr( arrays ) {
var alpha;
var uplo;
var x;
var A;
x = arrays[ 0 ];
A = arrays[ 1 ];
uplo = ndarraylike2scalar( arrays[ 2 ] );
alpha = ndarraylike2scalar( arrays[ 3 ] );
strided( uplo, numelDimension( A, 0 ), alpha, getData( x ), getStride( x, 0 ), getOffset( x ), getData( A ), getStride( A, 0 ), getStride( A, 1 ), getOffset( A ) );
return A;
}
// EXPORTS //
module.exports = gsyr;
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