Secant Method Minimum Estimation

In summary, the conversation discusses the use of the Secant method to estimate the local minimum of a function. The initial values for the method are given and a confusion regarding the first iteration is addressed. The correct calculation is shown and a potential typo in the given solution is pointed out.
  • #1
roam
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Homework Statement



I am having some trouble understanding the following solved problem:

Use two iterations of the Secant method to estimate where ##x^2 -x## has a local minimum. Start with ##x^{(0)} =1##, ##x^{(1)} =1/2.##

Answer:

2016_04_18_174819.jpg

Homework Equations



Secant iteration:

$$x_k = x_{k-1} - \frac{x_{k-1} - x_{k-2}}{f'_{k-1} - f'_{k-2}}$$

The Attempt at a Solution



So I understand that for the minimum we need to solve at the derivative of the function, which is

$$3x^2 -1$$

Using the given initial values ##x^{(0)} =1##, ##x^{(1)} =1/2,## the first iteration of the Secant method becomes$$x^{(3)} = \frac{1}{2} - \frac{(1/2)- 1}{(3(1/2)^2 -1) - (3(1)^2 -1)} = 0.2778$$

So, how did they get ##0.556## for the first iteration?

Also, why did they use ##1/4## instead of ##1/2## as ##x_{k-1}##?

I can't really follow the given answer, so I am wondering if there is an error in the solution. Any help is greatly appreciated.
 
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  • #2
I have learned a different secant method. Please check it out.

In your calculation, however, in fact you correctly use the function, not the derivative .

[edit] my mistake. Not the function nor the derivative. The derivative of ##x^2-x## is ##2x-1## !

But you do forget to multiply with f(1/2).
And you confuse yourself by calling it x(3)
 
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  • #3
Thank you so much for the explanation, it makes perfect sense now. I got the right answer, but I believe there is a typo in the solution in that ##x_{k-1}## in the first iteration should be ##1/2## instead of ##1/4.##
 
  • #4
I think you've got it right ! Well observed and no wonder you were a bit disoriented :smile:
 
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1. What is the Secant Method Minimum Estimation?

The Secant Method Minimum Estimation is a numerical method used to find the minimum of a function. It is an iterative process that uses the secant line between two points to estimate the minimum point of the function.

2. How does the Secant Method Minimum Estimation work?

The Secant Method Minimum Estimation works by using two initial points, x0 and x1, on the function and finding the secant line between these points. The intersection of this secant line with the x-axis gives an improved estimate of the minimum point. This process is repeated until a desired level of accuracy is achieved.

3. What are the advantages of using the Secant Method Minimum Estimation?

The Secant Method Minimum Estimation has several advantages over other optimization methods. It does not require the function to be differentiable, making it applicable to a wider range of functions. It also does not require the calculation of derivatives, which can be time-consuming and prone to error. Additionally, the method is relatively easy to implement and does not require a lot of computational resources.

4. What are the limitations of the Secant Method Minimum Estimation?

One limitation of the Secant Method Minimum Estimation is that it may not always converge to the true minimum point. This can happen if the initial points are too far apart or if the function has multiple local minima. Additionally, the method may be slower to converge compared to other optimization methods, especially for highly nonlinear functions.

5. How can the accuracy of the Secant Method Minimum Estimation be improved?

The accuracy of the Secant Method Minimum Estimation can be improved by using a smaller interval between the initial points, choosing initial points closer to the minimum point, or increasing the number of iterations. Additionally, using a combination of the Secant Method with other optimization methods, such as the Newton's method, can also improve the accuracy of the estimation.

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