diff --git a/ncg_optimizer/__init__.py b/ncg_optimizer/__init__.py
index c8654f5..18c30c9 100644
--- a/ncg_optimizer/__init__.py
+++ b/ncg_optimizer/__init__.py
@@ -16,7 +16,7 @@
 __all__ = (
     'LCG',
 )
-__version__ = '0.0.1b0'
+__version__ = '0.0.1b1'
 
 
 _package_opts = [
diff --git a/ncg_optimizer/lcg.py b/ncg_optimizer/lcg.py
index 48f230f..04f53ea 100644
--- a/ncg_optimizer/lcg.py
+++ b/ncg_optimizer/lcg.py
@@ -60,7 +60,7 @@ def step(self, closure=None):
                     continue
                 
                 d_p = p.grad
-                print(p)
+
                 n=len(d_p)
 
                 state = self.state[p]
diff --git a/tests/test_basic.ipynb b/tests/test_basic.ipynb
new file mode 100644
index 0000000..b668f5f
--- /dev/null
+++ b/tests/test_basic.ipynb
@@ -0,0 +1,627 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "import ncg_optimizer as optim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quadratic(tensor):\n",
+    "    x, y = tensor\n",
+    "    a = 2.0\n",
+    "    b = 1.0\n",
+    "    return (x ** 2) / a + (y ** 2) / b + 2 * x * y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rosenbrock(tensor):\n",
+    "    x, y = tensor\n",
+    "    return (1 - x) ** 2 + 1 * (y - x ** 2) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def beale(tensor):\n",
+    "    x, y = tensor\n",
+    "    f = (\n",
+    "        (1.5 - x + x * y) ** 2\n",
+    "        + (2.25 - x + x * y ** 2) ** 2\n",
+    "        + (2.625 - x + x * y ** 3) ** 2\n",
+    "    )\n",
+    "    return f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initial_state = (1.5, 1.5)\n",
+    "min_loc = (0, 0)\n",
+    "x = torch.Tensor(initial_state).requires_grad_(True)\n",
+    "x_min = torch.Tensor(min_loc)\n",
+    "iterations = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1.5000, 1.5000], requires_grad=True)\n",
+      "tensor([ 0.2360, -0.1854], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
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+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n",
+      "tensor([3.2783e-07, 4.3213e-07], requires_grad=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer = optim.LCG([x], eps=1e-3)\n",
+    "for _ in range(iterations):\n",
+    "    optimizer.zero_grad()\n",
+    "    f = quadratic(x)\n",
+    "    f.backward(retain_graph=True, create_graph=True)\n",
+    "    optimizer.step()\n",
+    "torch.allclose(x, x_min, atol=0.001)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "UUE",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13 (default, Mar 28 2022, 08:03:21) [MSC v.1916 64 bit (AMD64)]"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "0326a7f9ed817c17642f61d66b02c6fdb6b9844b168a64b2475a75c5fdc150c7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/test_basic.py b/tests/test_basic.py
deleted file mode 100644
index 51ba530..0000000
--- a/tests/test_basic.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import pytest
-import torch
-
-import ncg_optimizer as optim
-
-
-def rosenbrock(tensor):
-    x, y = tensor
-    return (1 - x) ** 2 + 1 * (y - x ** 2) ** 2
-
-
-def quadratic(tensor):
-    x, y = tensor
-    a = 1.0
-    b = 1.0
-    return (x ** 2) / a + (y ** 2) / b
-
-
-def beale(tensor):
-    x, y = tensor
-    f = (
-        (1.5 - x + x * y) ** 2
-        + (2.25 - x + x * y ** 2) ** 2
-        + (2.625 - x + x * y ** 3) ** 2
-    )
-    return f
-
-
-cases = [
-#    (rosenbrock, (1.5, 1.5), (1, 1)),
-     (quadratic, (1.5, 1.5), (0, 0)),
-#    (beale, (1.5, 1.5), (3, 0.5)),
-]
-
-
-def ids(v):
-    n = '{} {}'.format(v[0].__name__, v[1:])
-    return n
-
-
-optimizers = [
-    (optim.lcg, {'eps': 1e-5}, 500),
-]
-
-
-@pytest.mark.parametrize('case', cases, ids=ids)
-@pytest.mark.parametrize('optimizer_config', optimizers, ids=ids)
-def test_benchmark_function(case, optimizer_config):
-    func, initial_state, min_loc = case
-    optimizer_class, config, iterations = optimizer_config
-    x = torch.Tensor(initial_state).requires_grad_(True)
-    print([x])
-    x_min = torch.Tensor(min_loc)
-    print(iterations)
-    optimizer = optimizer_class([x], **config)
-    for _ in range(iterations):
-        optimizer.zero_grad()
-        f = func(x)
-        f.backward(retain_graph=True, create_graph=True)
-        optimizer.step()
-    assert torch.allclose(x, x_min, atol=0.001)
-
-    name = optimizer.__class__.__name__
-    assert name in optimizer.__repr__()
\ No newline at end of file