test: fix zlaswp tests for large datasets

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---

Author: aayush0325

lib/node_modules/@stdlib/lapack/base/zlaswp/test/test.ndarray.js

@@ -559,7 +559,7 @@ tape( 'the function efficiently handles large datasets (row-major)', function te
 	var o;
 
 	ord = 'row-major';
-	sh = [ 5, 100 ];
+	sh = [ 5, 1000 ];
 	st = shape2strides( sh, ord );
 	o = 0;
 

lib/node_modules/@stdlib/lapack/base/zlaswp/test/test.zlaswp.js

@@ -32,6 +32,7 @@ var reverse = require( '@stdlib/array/base/reverse' );
 var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
 var numel = require( '@stdlib/ndarray/base/numel' );
 var shape2strides = require( '@stdlib/ndarray/base/shape2strides' );
+var reinterpret = require( '@stdlib/strided/base/reinterpret-complex128' );
 var zlaswp = require( './../lib/zlaswp.js' );
 
 
@@ -272,8 +273,11 @@ tape( 'if provided an increment between successive values of `IPIV` equal to `0`
 });
 
 tape( 'the function efficiently handles large datasets (column-major)', function test( t ) {
+	var expectedMat;
+	var expectedBuf;
 	var expected;
 	var IPIV;
+	var Abuf;
 	var buf;
 	var mat;
 	var out;
@@ -284,25 +288,29 @@ tape( 'the function efficiently handles large datasets (column-major)', function
 	var o;
 
 	ord = 'column-major';
-	sh = [ 5, 100 ];
+	sh = [ 5, 1000 ];
 	st = shape2strides( sh, ord );
 	o = 0;
 
 	// Define a linear buffer:
-	buf = oneTo( numel( sh ), 'generic' );
-
-	// Convert to a nested array:
-	mat = ndarray2array( buf, sh, st, o, ord );
+	buf = oneTo( 2 * numel( sh ), 'generic' );
 
 	// Define an input matrix in linear storage:
 	A = new Complex128Array( buf );
 
+	Abuf = reinterpret( A, 0 );
+
+	// Convert to a nested array:
+	mat = ndarray2array( Abuf, [ sh[ 0 ], sh[ 1 ], 2 ], [ 2 * st[0], 2 * st[1], 1 ], o, ord );
+
 	// Create an array of pivot indices:
 	IPIV = new Int32Array( [ 9999, 9999, 9999, 1, 0 ] );
 
 	// Define the expected output array:
-	expected = reverse( mat.slice() );
-	expected = new Complex128Array( flatten( expected, sh, true ) );
+	expectedMat = reverse( mat.slice() );
+	expectedBuf = flatten( expectedMat, sh, true );
+	expectedBuf = flatten( expectedBuf, [ numel( sh ), 2 ], false );
+	expected = new Complex128Array( expectedBuf );
 
 	// Interchange rows:
 	out = zlaswp( ord, sh[1], A, sh[0], 3, 4, IPIV, 1 );
@@ -461,8 +469,11 @@ tape( 'if provided an increment between successive values of `IPIV` equal to `0`
 });
 
 tape( 'the function efficiently handles large datasets (row-major)', function test( t ) {
+	var expectedBuf;
+	var expectedMat;
 	var expected;
 	var IPIV;
+	var Abuf;
 	var buf;
 	var mat;
 	var out;
@@ -473,25 +484,29 @@ tape( 'the function efficiently handles large datasets (row-major)', function te
 	var o;
 
 	ord = 'row-major';
-	sh = [ 5, 100 ];
+	sh = [ 5, 1000 ];
 	st = shape2strides( sh, ord );
 	o = 0;
 
 	// Define a linear buffer:
-	buf = oneTo( numel( sh ), 'generic' );
-
-	// Convert to a nested array:
-	mat = ndarray2array( buf, sh, st, o, ord );
+	buf = oneTo( 2 * numel( sh ), 'generic' );
 
 	// Define an input matrix in linear storage:
 	A = new Complex128Array( buf );
 
+	Abuf = reinterpret( A, 0 );
+
+	// Convert to a nested array:
+	mat = ndarray2array( Abuf, [ sh[ 0 ], sh[ 1 ], 2 ], [ 2 * st[0], 2 * st[1], 1 ], o, ord );
+
 	// Create an array of pivot indices:
 	IPIV = new Int32Array( [ 9999, 9999, 9999, 1, 0 ] );
 
 	// Define the expected output array:
-	expected = reverse( mat.slice() );
-	expected = new Complex128Array( flatten( expected, sh, false ) );
+	expectedMat = reverse( mat.slice() );
+	expectedBuf = flatten( expectedMat, sh, false );
+	expectedBuf = flatten( expectedBuf, [ numel( sh ), 2 ], false );
+	expected = new Complex128Array( expectedBuf );
 
 	// Interchange rows:
 	out = zlaswp( ord, sh[1], A, sh[1], 3, 4, IPIV, 1 );