I have a function that I am trying to find the minimum of. I know how to solve for the minimum of a quadratic function as well as a square root function. I don't know how to solve for the minimum of a function that has both of those elements. I have simplified the function as much as I can and it has come to this.

$$y = \sqrt{-3x^2-10x+225-40\sqrt{25-x^2}}$$

What is the procedure I must do to find the minimum?

Here is a general formula. I am trying to find the value of $x$ that minimises $y$.


  • $\begingroup$ Calculus? ${}{}$ $\endgroup$ – Tavish Apr 3 at 10:47
  • $\begingroup$ Do you need to find minimum analytically or numerical methods are accepted? $\endgroup$ – Yalikesifulei Apr 3 at 10:47
  • $\begingroup$ Have you tried $x=5\sin t$? $\endgroup$ – J.G. Apr 3 at 10:51
  • $\begingroup$ I'm not sure what you mean by analytically or numerically. I am contributing to a game and I just need to be able to program in a formula that will spit out the minimum value of this function. I chose arbitrary values to see if I could solve it, then I would create my proper formula. I could iterate through many x values and choose the minimum y as an approximation but that would be bad practice. $\endgroup$ – Phedg1 Apr 3 at 10:53
  • $\begingroup$ @J.G. What is t? $\endgroup$ – Phedg1 Apr 3 at 10:58

To minimize $y^2=(x-a)^2+(\tfrac{h}{w}\sqrt{w^2-x^2}-b)^2$ algebraically requires finding its turning points by solving $\frac{dy^2}{dx}=0$. This reduces to solving a quartic equation. Although quartics admit a solution in radicals, it's rarely worth using. It's probably best to use numerical minimization instead; it's not as expensive as you might think. Here's a Python solution:

from numpy import sqrt#Imported from numpy instead of math in case you need to parallelize
from scipy.optimize import minimize_scalar

def best_x(a, b, h, w):
    f = lambda x: (x-a)**2+(h/w)*sqrt(w**2-x**2)-b**2)**2
    max_x = abs(w)
    return minimize_scalar(f, bounds=(-max_x, max_x)).x

For me, this took 1 millisecond to solve the special case you mentioned originally. You should be able to adapt it to C++ using Boost, which will be even faster.


An alternative answer is available here. It is more work to implement but computationally cheaper. https://math.stackexchange.com/a/4093343/125521


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