added runge kutta gills method to maths/ numerical_analysis (TheAlgorithms#10967)

* added runge kutta gills method

* Apply suggestions from code review

---------

Co-authored-by: Tianyi Zheng <[email protected]>

Author: 2 people

Diff for: maths/numerical_analysis/runge_kutta_gills.py

@@ -0,0 +1,89 @@
+"""
+Use the Runge-Kutta-Gill's method of order 4 to solve Ordinary Differential Equations.
+
+https://www.geeksforgeeks.org/gills-4th-order-method-to-solve-differential-equations/
+Author : Ravi Kumar
+"""
+from collections.abc import Callable
+from math import sqrt
+
+import numpy as np
+
+
+def runge_kutta_gills(
+    func: Callable[[float, float], float],
+    x_initial: float,
+    y_initial: float,
+    step_size: float,
+    x_final: float,
+) -> np.ndarray:
+    """
+    Solve an Ordinary Differential Equations using Runge-Kutta-Gills Method of order 4.
+
+    args:
+    func: An ordinary differential equation (ODE) as function of x and y.
+    x_initial: The initial value of x.
+    y_initial: The initial value of y.
+    step_size: The increment value of x.
+    x_final: The final value of x.
+
+    Returns:
+        Solution of y at each nodal point
+
+    >>> def f(x, y):
+    ...     return (x-y)/2
+    >>> y = runge_kutta_gills(f, 0, 3, 0.2, 5)
+    >>> y[-1]
+    3.4104259225717537
+
+    >>> def f(x,y):
+    ...     return x
+    >>> y = runge_kutta_gills(f, -1, 0, 0.2, 0)
+    >>> y
+    array([ 0.  , -0.18, -0.32, -0.42, -0.48, -0.5 ])
+
+    >>> def f(x, y):
+    ...     return x + y
+    >>> y = runge_kutta_gills(f, 0, 0, 0.2, -1)
+    Traceback (most recent call last):
+        ...
+    ValueError: The final value of x must be greater than initial value of x.
+
+    >>> def f(x, y):
+    ...     return x
+    >>> y = runge_kutta_gills(f, -1, 0, -0.2, 0)
+    Traceback (most recent call last):
+        ...
+    ValueError: Step size must be positive.
+    """
+    if x_initial >= x_final:
+        raise ValueError(
+            "The final value of x must be greater than initial value of x."
+        )
+
+    if step_size <= 0:
+        raise ValueError("Step size must be positive.")
+
+    n = int((x_final - x_initial) / step_size)
+    y = np.zeros(n + 1)
+    y[0] = y_initial
+    for i in range(n):
+        k1 = step_size * func(x_initial, y[i])
+        k2 = step_size * func(x_initial + step_size / 2, y[i] + k1 / 2)
+        k3 = step_size * func(
+            x_initial + step_size / 2,
+            y[i] + (-0.5 + 1 / sqrt(2)) * k1 + (1 - 1 / sqrt(2)) * k2,
+        )
+        k4 = step_size * func(
+            x_initial + step_size, y[i] - (1 / sqrt(2)) * k2 + (1 + 1 / sqrt(2)) * k3
+        )
+
+        y[i + 1] = y[i] + (k1 + (2 - sqrt(2)) * k2 + (2 + sqrt(2)) * k3 + k4) / 6
+        x_initial += step_size
+    return y
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()