refactor: update the implementation of blas/base/dsymv

---
type: pre_commit_static_analysis_report
description: Results of running static analysis checks when committing changes.
report:
  - task: lint_filenames
    status: passed
  - task: lint_editorconfig
    status: passed
  - task: lint_markdown
    status: passed
  - task: lint_package_json
    status: passed
  - task: lint_repl_help
    status: passed
  - task: lint_javascript_src
    status: passed
  - task: lint_javascript_cli
    status: na
  - task: lint_javascript_examples
    status: passed
  - task: lint_javascript_tests
    status: passed
  - task: lint_javascript_benchmarks
    status: passed
  - task: lint_python
    status: na
  - task: lint_r
    status: na
  - task: lint_c_src
    status: na
  - task: lint_c_examples
    status: na
  - task: lint_c_benchmarks
    status: na
  - task: lint_c_tests_fixtures
    status: na
  - task: lint_shell
    status: na
  - task: lint_typescript_declarations
    status: passed
  - task: lint_typescript_tests
    status: passed
  - task: lint_license_headers
    status: passed
---

Author: prajjwalbajpai

lib/node_modules/@stdlib/blas/base/dsymv/README.md

@@ -20,7 +20,7 @@ limitations under the License.
 
 # dsymv
 
-> Perform the matrix-vector operation `y = α*A*x + β*y` where `α` and `β` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
+> Perform the matrix-vector operation `y = α*A*x + β*y`.
 
 <section class="usage">
 
@@ -92,7 +92,7 @@ dsymv( 'row-major', 'upper', 3, 1.0, A, 3, x1, -1, 1.0, y1, -1 );
 // y0 => <Float64Array>[ 1.0, 4.0, 3.0, 2.0 ]
 ```
 
-#### dsymv.ndarray( order, uplo, N, α, A, LDA, x, sx, ox, β, y, sy, oy )
+#### dsymv.ndarray( uplo, N, α, A, sa1, sa2, oa, x, sx, ox, β, y, sy, oy )
 
 Performs the matrix-vector operation `y = α*A*x + β*y` using alternative indexing semantics and where `α` and `β` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
 
@@ -103,12 +103,15 @@ var A = new Float64Array( [ 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 3.0 ] );
 var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
 var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
 
-dsymv.ndarray( 'row-major', 'upper', 3, 2.0, A, 3, x, -1, 2, 1.0, y, 1, 0 );
+dsymv.ndarray( 'upper', 3, 2.0, A, 3, 1, 0, x, -1, 2, 1.0, y, 1, 0 );
 // y => <Float64Array>[ 7.0, 10.0, 9.0 ]
 ```
 
 The function has the following additional parameters:
 
+-   **sa1**: stride for the first dimension of `A`.
+-   **sa2**: stride for the second dimension of `A`.
+-   **oa**: starting index for `A`.
 -   **ox**: starting index for `x`.
 -   **oy**: starting index for `y`.
 
@@ -121,7 +124,7 @@ var A = new Float64Array( [ 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 3.0 ] );
 var x = new Float64Array( [ 1.0, 1.0, 1.0 ] );
 var y = new Float64Array( [ 1.0, 1.0, 1.0 ] );
 
-dsymv.ndarray( 'row-major', 'lower', 3, 1.0, A, 3, x, -1, 2, 1.0, y, -1, 2 );
+dsymv.ndarray( 'lower', 3, 1.0, A, 3, 1, 0, x, -1, 2, 1.0, y, -1, 2 );
 // y => <Float64Array>[ 4.0, 3.0, 2.0 ]
 ```
 
@@ -154,13 +157,16 @@ var opts = {
     'dtype': 'float64'
 };
 
-var N = 3;
+var N = 5;
 var A = ones( N*N, opts.dtype );
 
 var x = discreteUniform( N, 0, 255, opts );
 var y = discreteUniform( N, 0, 255, opts );
 
-dsymv.ndarray( 'row-major', 'upper', N, 1.0, A, N, x, 1, 0, 1.0, y, 1, 0 );
+dsymv( 'row-major', 'upper', N, 1.0, A, N, x, 1, 1.0, y, 1 );
+console.log( y );
+
+dsymv.ndarray( 'upper', N, 1.0, A, N, 1, 0, x, 1, 0, 1.0, y, 1, 0 );
 console.log( y );
 ```
 

lib/node_modules/@stdlib/blas/base/dsymv/benchmark/benchmark.ndarray.js

@@ -65,7 +65,7 @@ function createBenchmark( N ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			z = dsymv( 'row-major', 'upper', N, 1.0, A, N, x, 1, 0, 1.0, y, 1, 0 );
+			z = dsymv( 'upper', N, 1.0, A, N, 1, 0, x, 1, 0, 1.0, y, 1, 0 );
 			if ( isnan( z[ i%z.length ] ) ) {
 				b.fail( 'should not return NaN' );
 			}

lib/node_modules/@stdlib/blas/base/dsymv/docs/repl.txt

@@ -28,7 +28,7 @@
         Scalar constant.
 
     A: Float64Array
-        Matrix.
+        Input matrix.
 
     lda: integer
         Stride of the first dimension of `A` (a.k.a., leading dimension of the
@@ -63,7 +63,7 @@
     <Float64Array>[ 4.0, 4.0 ]
 
 
-{{alias}}.ndarray( order, uplo, N, α, A, lda, x, sx, ox, β, y, sy, oy )
+{{alias}}.ndarray( uplo, N, α, A, sa1, sa2, oa, x, sx, ox, β, y, sy, oy )
     Performs the matrix-vector operation `y = α*A*x + β*y` using alternative
     indexing semantics and where `α` and `β` are scalars, `x` and `y` are `N`
     element vectors, and `A` is an `N` by `N` symmetric matrix.
@@ -74,10 +74,6 @@
 
     Parameters
     ----------
-    order: string
-        Row-major (C-style) or column-major (Fortran-style) order. Must be
-        either 'row-major' or 'column-major'.
-
     uplo: string
         Specifies whether to reference the upper or lower triangular part of
         `A`. Must be either 'upper' or 'lower'.
@@ -89,11 +85,16 @@
         Scalar constant.
 
     A: Float64Array
-        Matrix.
+        Input matrix.
 
-    lda: integer
-        Stride of the first dimension of `A` (a.k.a., leading dimension of the
-        matrix `A`).
+    sa1: integer
+        Stride for the first dimension of `A`.
+
+    sa2: integer
+        Stride for the second dimension of `A`.
+
+    oa: integer
+        Starting index for `A`.
 
     x: Float64Array
         Input vector.
@@ -126,8 +127,7 @@
     > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, 1.0 ] );
     > var y = new {{alias:@stdlib/array/float64}}( [ 1.0, 1.0 ] );
     > var A = new {{alias:@stdlib/array/float64}}( [ 1.0, 2.0, 2.0, 1.0 ] );
-    > var ord = 'row-major';
-    > {{alias}}.ndarray( ord, 'upper', 2, 1.0, A, 2, x, 1, 0, 1.0, y, 1, 0 )
+    > {{alias}}.ndarray( 'upper', 2, 1.0, A, 2, 1, 0, x, 1, 0, 1.0, y, 1, 0 )
     <Float64Array>[ 4.0, 4.0 ]
 
     See Also

lib/node_modules/@stdlib/blas/base/dsymv/docs/types/index.d.ts

@@ -1,7 +1,7 @@
 /*
 * @license Apache-2.0
 *
-* Copyright (c) 2024 The Stdlib Authors.
+* 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.
@@ -27,13 +27,13 @@ import { Layout, MatrixTriangle } from '@stdlib/types/blas';
 */
 interface Routine {
 	/**
-	* Performs the matrix-vector operation `y = alpha*A*x + beta*y` where `alpha` and `beta` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
+	* Performs the matrix-vector operation `y = α*A*x + β*y` where `α` and `β` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
 	*
 	* @param order - storage layout
 	* @param uplo - specifies whether the upper or lower triangular part of the symmetric matrix `A` is to be referenced
 	* @param N - number of elements along each dimension in the matrix `A`
 	* @param alpha - scalar constant
-	* @param A - matrix
+	* @param A - input matrix
 	* @param LDA - stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`)
 	* @param x - first input array
 	* @param strideX - `x` stride length
@@ -55,21 +55,23 @@ interface Routine {
 	( order: Layout, uplo: MatrixTriangle, N: number, alpha: number, A: Float64Array, LDA: number, x: Float64Array, strideX: number, beta: number, y: Float64Array, strideY: number ): Float64Array;
 
 	/**
-	* Performs the matrix-vector operation `y = alpha*A*x + beta*y` using alternative indexing semantics and where `alpha` and `beta` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
+	* Performs the matrix-vector operation `y = α*A*x + β*y`, using alternative indexing semantics and where `α` and `β` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
 	*
 	* @param order - storage layout
 	* @param uplo - specifies whether the upper or lower triangular part of the symmetric matrix `A` should be referenced
 	* @param N - number of elements along each dimension in the matrix `A`
 	* @param alpha - scalar constant
-	* @param A - matrix
-	* @param LDA - stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`)
+	* @param A - input matrix
+	* @param strideA1 - stride for the first dimension of `A`
+	* @param strideA2 - stride for the second dimension of `A`
+	* @param offsetA - starting index for `A`
 	* @param x - first input array
 	* @param strideX - `x` stride length
-	* @param offsetX - starting `x` index
+	* @param offsetX - starting index for `x`
 	* @param beta - scalar constant
 	* @param y - second input array
 	* @param strideY - `y` stride length
-	* @param offsetY - starting `y` index
+	* @param offsetY - starting index for `y`
 	* @returns `y`
 	*
 	* @example
@@ -79,20 +81,20 @@ interface Routine {
 	* var x = new Float64Array( [ 1.0, 1.0, 1.0 ] );
 	* var y = new Float64Array( [ 0.0, 0.0, 0.0 ] );
 	*
-	* dsymv.ndarray( 'row-major', 'lower', 3, 1.0, A, 3, x, 1, 0, 0.0, y, 1, 0 );
+	* dsymv.ndarray( 'lower', 3, 1.0, A, 3, 1, 0, x, 1, 0, 0.0, y, 1, 0 );
 	* // y => <Float64Array>[ 1.0, 2.0, 3.0 ]
 	*/
-	ndarray( order: Layout, uplo: MatrixTriangle, N: number, alpha: number, A: Float64Array, LDA: number, x: Float64Array, strideX: number, offsetX: number, beta: number, y: Float64Array, strideY: number, offsetY: number ): Float64Array;
+	ndarray( uplo: MatrixTriangle, N: number, alpha: number, A: Float64Array, strideA1: number, strideA2: number, offsetA: number, x: Float64Array, strideX: number, offsetX: number, beta: number, y: Float64Array, strideY: number, offsetY: number ): Float64Array;
 }
 
 /**
-* Performs the matrix-vector operation `y = alpha*A*x + beta*y` where `alpha` and `beta` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
+* Performs the matrix-vector operation `y = α*A*x + β*y` where `α` and `β` are scalars, `x` and `y` are `N` element vectors, and `A` is an `N` by `N` symmetric matrix.
 *
 * @param order - storage layout
 * @param uplo - specifies whether the upper or lower triangular part of the symmetric matrix `A` is to be referenced
 * @param N - number of elements along each dimension in the matrix `A`
 * @param alpha - scalar constant
-* @param A - matrix
+* @param A - input matrix
 * @param LDA - stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`)
 * @param x - first input array
 * @param strideX - `x` stride length
@@ -118,7 +120,7 @@ interface Routine {
 * var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
 * var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
 *
-* dsymv.ndarray( 'row-major', 'upper', 3, 2.0, A, 3, x, 1, 0, 1.0, y, 2, 0 );
+* dsymv.ndarray( 'upper', 3, 2.0, A, 3, 1, 0, x, 1, 0, 1.0, y, 2, 0 );
 * // y => <Float64Array>[ 3.0, 2.0, 11.0 ]
 */
 declare var dsymv: Routine;