diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
index c0d241a31..d3e67fbdc 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
@@ -161,76 +161,6 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 }
     }
 
-    @Test
-    fun testSVD() = DoubleTensorAlgebra{
-        testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
-        testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
-    }
-
-//    @Test
-//    fun testSVDError() = DoubleTensorAlgebra{
-//       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
-//        )
-//        testSVDFor(fromArray(intArrayOf(5, 3), buffer))
-//    }
-
-    @Test
-    fun testBatchedSVD() = DoubleTensorAlgebra {
-        val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
-        val (tensorU, tensorS, tensorV) = tensor.svd()
-        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
-        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 testSVDGolabKahanError() = DoubleTensorAlgebra{
-//        val buffer = doubleArrayOf(
-//            1.0, 2.0, 3.0, 2.0, 3.0,
-//            4.0, 3.0, 4.0, 5.0, 4.0,
-//            5.0, 6.0, 5.0, 6.0, 7.0
-//        )
-//        testSVDGolabKahanFor(fromArray(intArrayOf(3, 5), buffer))
-//    }
-
-//    @Test
-//    fun testSVDGolabKahanBig() = DoubleTensorAlgebra{
-//        val tensor = DoubleTensorAlgebra.randomNormal(intArrayOf(100, 100, 100), 0)
-//        testSVDGolabKahanFor(tensor)
-//    }
-//
-//    @Test
-//    fun testSVDBig() = DoubleTensorAlgebra{
-//        val tensor = DoubleTensorAlgebra.randomNormal(intArrayOf(100, 100, 100), 0)
-//        testSVDFor(tensor)
-//    }
-
-    @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)
@@ -240,24 +170,63 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 
-
-}
-
-
-private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10) {
-    val svd = tensor.svd()
-
-    val tensorSVD = svd.first
-        .dot(
-            diagonalEmbedding(svd.second)
-                .dot(svd.third.transpose())
+    @Test
+    fun testSVD() = DoubleTensorAlgebra{
+        testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+        val buffer1 = 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
         )
+        testSVDFor(fromArray(intArrayOf(5, 3), buffer1))
+        val buffer2 = doubleArrayOf(
+            1.0, 2.0, 3.0, 2.0, 3.0,
+            4.0, 3.0, 4.0, 5.0, 4.0,
+            5.0, 6.0, 5.0, 6.0, 7.0
+        )
+        testSVDFor(fromArray(intArrayOf(3, 5), buffer2))
+    }
 
-    assertTrue(tensor.eq(tensorSVD, epsilon))
+    @Test
+    fun testBatchedSVD() = DoubleTensorAlgebra{
+        val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0)
+        testSVDFor(tensor1)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0)
+        testSVDFor(tensor2)
+    }
+
+    @Test
+    fun testSVDPowerMethod() = DoubleTensorAlgebra{
+        testSVDPowerMethodFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
+        testSVDPowerMethodFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+    }
+
+    @Test
+    fun testBatchedSVDPowerMethod() = DoubleTensorAlgebra {
+        val tensor1 = randomNormal(intArrayOf(2, 5, 3), 0)
+        testSVDPowerMethodFor(tensor1)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(intArrayOf(30, 30, 30), 0)
+        testSVDPowerMethodFor(tensor2)
+    }
+
+//    @Test
+//    fun testSVDPowerMethodError() = DoubleTensorAlgebra{
+//       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
+//        )
+//        testSVDPowerMethodFor(fromArray(intArrayOf(5, 3), buffer))
+//    }
 }
 
-private fun DoubleTensorAlgebra.testSVDGolabKahanFor(tensor: DoubleTensor) {
-    val svd = tensor.svdGolabKahan()
+private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor) {
+    val svd = tensor.svd()
 
     val tensorSVD = svd.first
         .dot(
@@ -268,4 +237,14 @@ private fun DoubleTensorAlgebra.testSVDGolabKahanFor(tensor: DoubleTensor) {
     assertTrue(tensor.eq(tensorSVD))
 }
 
+private fun DoubleTensorAlgebra.testSVDPowerMethodFor(tensor: DoubleTensor, epsilon: Double = 1e-10) {
+    val svd = tensor.svdPowerMethod()
 
+    val tensorSVD = svd.first
+        .dot(
+            diagonalEmbedding(svd.second)
+                .dot(svd.third.transpose())
+        )
+
+    assertTrue(tensor.eq(tensorSVD, epsilon))
+}
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