```python
import numpy as np
import sympy as sp
```


```python
def newton_1d_step(f_str, x_prev):
    x = sp.symbols('x') # We will assume that you use the symbol x
    formula = sp.sympify(f_str)
    df = sp.diff(formula, x)
    ddf = sp.diff(df, x)
    return x_prev - df.subs(x, x_prev) / ddf.subs(x, x_prev)
```


```python
# Test case
tol = 10**-7
expected = 0.239999999617
actual = newton_1d_step('4*sin(x**6)', 0.3)
assert abs(expected-actual) < tol
```

ed7bbac1f96e4e178ba736a2a4722090184a393b
ipynb
Jupyter Notebook
newton_1d_optimization.ipynb
Racso-3141/uiuc-cs357-fa21-scripts
e44f0a1ea4eb657cb77253f1db464d52961bbe5e
MIT
2021-11-02T05:56:10.000Z
2022-03-03T19:25:19.000Z
newton_1d_optimization.ipynb
Racso-3141/uiuc-cs357-fa21-scripts
e44f0a1ea4eb657cb77253f1db464d52961bbe5e
MIT
newton_1d_optimization.ipynb
Racso-3141/uiuc-cs357-fa21-scripts
e44f0a1ea4eb657cb77253f1db464d52961bbe5e
MIT
2021-10-30T15:18:01.000Z
2021-12-10T11:26:43.000Z
Qwen/Qwen-72B
1. YES
2. YES