Geometric average versus arithmatic average

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The discussion focuses on the relationship between geometric and arithmetic averages, particularly how to adjust weights to equalize them. Given a set of numbers with specified weights, the geometric average is calculated as approximately 48.4991, while the arithmetic average is 62. To find new normalized weights that allow the arithmetic average to match the geometric average, one must consider that the weights may need to sum to more than 1. The suggestion is to normalize the weights by dividing by their total sum, ensuring they add up to 1. Achieving equality between the arithmetic mean and geometric mean requires all terms to be equal, indicating a specific condition for the weights.
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Homework Statement


I have a range of numbers numbers n_i, each with a different weight w_i that sum up to 1. To keep things simple, let's take the case where we have three numbers with the following weights:

n_i w_i
------------------------------
100 0.5
30 0.2
20 0.3

Their geometric average is (100^{0.5})*(30^{0.2})*(20^{0.3})=48.4991. The arithmetic average of the numbers is 100*0.5 + 30*0.2 + 20*0.3=62, so it is larger than the geometric average.

How can I find a new set of normalized weights w_i' that sum to 1 that can be used to find the arithmetic average of the numbers such that it is equal to the geometric average? In other words, I would like to find a new set w_i' such that

100*w_1' + 30*w_2' + 20*w_3' = (100^{0.5})*(30^{0.2})*(20^{0.3}) given that w_1'+w_2'+w_3'=1.

The weights are all nonzero.My best attempt at the moment is

<br /> \sum_i (\text{GA} \frac{w_i}{n_i}) n_i<br />

where GA is the geometric average. This sum yields GA as expected, but the weights are larger than 1.
 
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Unweighted, the arithmetic mean is always >= geometric mean, so I suspect your weights may have to have a sum > 1. Think what happens if one of the numbers you're averaging is zero.
 
To get equality between AM and GM, you need all terms equal. What does that suggest for the weights? You can always get the weights to add to 1 by normalising: divide by the sum of the weights.
 
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