diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/margarita.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/margarita.kt
deleted file mode 100644
index c376d6d0f..000000000
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/margarita.kt
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.linear.transpose
-import space.kscience.kmath.misc.PerformancePitfall
-import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.Structure2D
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.core.*
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import kotlin.math.*
-
-fun MutableStructure2D<Double>.print() {
-    val n = this.shape.component1()
-    val m = this.shape.component2()
-    for (i in 0 until n) {
-        for (j in 0 until m) {
-            val x = (this[i, j] * 100).roundToInt() / 100.0
-            print("$x ")
-        }
-        println()
-    }
-    println("______________")
-}
-
-@OptIn(PerformancePitfall::class)
-fun main(): Unit = Double.tensorAlgebra.withBroadcast {
-    val shape = intArrayOf(5, 3)
-    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
-    )
-    val buffer2 = doubleArrayOf(
-        0.000000, 0.000000, 0.000000,
-        0.000000, 0.000000, 0.000000,
-        0.000000, 0.000000, 0.000000
-    )
-    val tensor = fromArray(shape, buffer).as2D()
-    val v = fromArray(intArrayOf(3, 3), buffer2).as2D()
-    val w_shape = intArrayOf(3, 1)
-    var w_buffer = doubleArrayOf(0.000000)
-    for (i in 0 until 3 - 1) {
-        w_buffer += doubleArrayOf(0.000000)
-    }
-    val w = BroadcastDoubleTensorAlgebra.fromArray(w_shape, w_buffer).as2D()
-    tensor.print()
-    var ans = Pair(w, v)
-    tensor.svdGolabKahan(v, w)
-
-    println("u")
-    tensor.print()
-    println("w")
-    w.print()
-    println("v")
-    v.print()
-
-
-}