Optimizing with Golden Section Method: Choosing Alpha for Maximum Efficiency

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SUMMARY

The discussion focuses on optimizing functions using the Golden Section Method, specifically in selecting the alpha values for maximum efficiency. The initial alpha value, α1, is set to 0.618, derived from the Golden Ratio, while the subsequent alpha value, α2, is suggested to be 0.382. This value is calculated as 1 minus α1, indicating a systematic approach rather than arbitrary selection. Participants emphasize the importance of comparing function values f(x1) and f(x2) to determine the optimal point in the optimization process.

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Homework Statement
You are at the point (0,1). Find the minimum of the function in the direction (line) (1, 2)^T using
the Golden-Section line-search algorithm on the step-length interval [0, 1]. Stop when the length of
the interval is less than 0.2. Note: step-length interval could be described by the parameter t, and,
so, all the points along the direction (1, 2)^T
can be expressed as (0, 1) + t · (1, 2).
Relevant Equations
Function is: f(x1,x2)=(x2-x1^2)^2+e^x1^2
Golden Ratio (GR):0.618
My function is f(x1,x2)=(x2-x1 2)2+ex1^2
I understand the equation for finding a point is xk+1=xkkdk, where dk=(0,1) and x0=(0,1). For the first step I can choose α1=0.618 (GR), but how do we choose α2? In the solution manual I see they chose 0.382, was this just some arbitrary number? My assumption is that it is arbitrary. I understand afterwards I will have to compare f(x1) and f(x2). But I am struggling with how the second α2 was picked, any help would be appreciated.
 
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I realize this is several months old. What reference or background do you have associated with this problem?

I was not familiar with the Golden Section method, so I looked it up. I found this site, which provides some insight. https://www.geodose.com/2021/06/golden-section-search-python-application-example.html

I need to re-read the article, but I think the 0.382 comes from subtracting 0.618 from 1, though it could come from (0.618)²
 

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