kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt

@@ -175,6 +175,28 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         assertTrue(tensor.eq(tensorSVD))
     }
 
+    @Test
+    fun testSVDGolabKahan() = DoubleTensorAlgebra{
+        testSVDGolabKahanFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDGolabKahanFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+        val buffer = doubleArrayOf(
+            1.000000, 2.000000, 3.000000,
+            2.000000, 3.000000, 4.000000,
+            3.000000, 4.000000, 5.000000,
+            4.000000, 5.000000, 6.000000,
+            5.000000, 6.000000, 7.000000
+        )
+        testSVDGolabKahanFor(fromArray(intArrayOf(5, 3), buffer))
+    }
+
+    @Test
+    fun testBatchedSVDGolabKahan() = DoubleTensorAlgebra{
+        val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
+        val (tensorU, tensorS, tensorV) = tensor.svdGolabKahan()
+        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
+        assertTrue(tensor.eq(tensorSVD))
+    }
+
     @Test
     fun testBatchedSymEig() = DoubleTensorAlgebra {
         val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
@@ -199,3 +221,17 @@ private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double
 
     assertTrue(tensor.eq(tensorSVD, epsilon))
 }
+
+private fun DoubleTensorAlgebra.testSVDGolabKahanFor(tensor: DoubleTensor) {
+    val svd = tensor.svdGolabKahan()
+
+    val tensorSVD = svd.first
+        .dot(
+            diagonalEmbedding(svd.second)
+                .dot(svd.third.transpose())
+        )
+
+    assertTrue(tensor.eq(tensorSVD))
+}
+
+