How to Minimize the Sum of Squares with Given Distinct Elements?

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    2016
In summary, the minimum value of sum of squares is the smallest possible value that can be obtained by adding the squares of a set of numbers. It is calculated by taking the sum of the squared differences between each data point and the mean of the data set. This measure is significant in statistics as it helps to assess the quality of a regression model or the spread of a data set. It cannot be negative, but can be zero if there is no variation in the data. The presence of outliers can greatly affect the minimum value of sum of squares, and removing them or using robust methods can help mitigate their impact.
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anemone
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Here is this week's POTW:

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Let $x_1,\,x_2,\,x_3,\,x_4,\,x_5,\,x_6,\,x_7$ and $x_8$ be distinct elements in the set $\{-7,\,-5,\,-3,\,-2,\,2,\,4,\,6,\,13\}$.

What is the minimum possible value of $(x_1+\,x_2+\,x_3+\,x_4)^2+(x_5+\,x_6+\,x_7+\,x_8)^2$?

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Congratulations to the following members for their correct solution::)

1. Ackbach
2. Opalg
3. lfdahl
4. kaliprasad
5. greg1313

Solution from Opalg:
The minimum value is $34$. There is more than one way to achieve this, for example $$(-7+2+4+6)^2 + (-5-3-2+13)^2 = 5^2 + 3^2 = 25 + 9 = 34.$$

To see that no smaller total is possible, notice that the sum of all eight numbers is $8$. So if $X = x_1+x_2+x_3+x_4$ and $Y = x_5+x_6+x_7+x_8$ then $X+Y = 8$. If either $X$ or $Y$ is $6$ or greater then its square is (at least) $36$, which is already too large. So the only possibilities for achieving the minimum are $\{X,Y\} = \{5,3\}$ as in the above example, or $X=Y=4.$

To see that the case $X=Y=4$ cannot occur, we may as well assume that $13$ is one of the numbers in $X$. If $X$ contains no other positive numbers, then it must contain three of the negative numbers with sum $-9.$ But it is easy to see that this cannot happen. (If we leave out $-7$ from the set $\{-7,-5,-3,-2\}$ then the sum of the other three numbers is $-10$. If we leave out $-5$ then the sum is $-12$. And so on.)

If $X$ contains another positive number besides $13$ then that number had better be $2$. (If it is $4$ or $6$ then the positive numbers in $X$ will sum to $17$ or $19$, and there is no way for the two negative numbers in $X$ to bring the sum of all the numbers in $X$ down to $4$.) But if $X$ contains $13$ and $2$ (with sum $15$) then the two negative numbers in $X$ must have sum $-11$, and there is no way to achieve that.

Thus $X=Y=4$ is not achievable, and the minimum only occurs when $\{X,Y\} = \{5,3\}$.

Alternate solution from Ackbach:
This is a problem tailor-made for a brute force search algorithm: simply generate all possible permutations of the set, compute the function for each permutation, and see which one gives you the best outcome. I've implemented this solution in LabVIEW as follows:

https://www.physicsforums.com/attachments/6029

This VI is the top-level code that calls the permutation generator and processes each one.

https://www.physicsforums.com/attachments/6030

This VI generates all the possible permutations using Heap's algorithm.

The answer is 34, the output from the set $\{2,-7,6,4,-3,-5,-2,13\}$.

Suggested model solution:
Note that the sum of the elements in the set is 8. Let $a=x_1+\,x_2+\,x_3+\,x_4$ so $x_5+\,x_6+\,x_7+\,x_8=8-a$.

Then we have

$(x_1+\,x_2+\,x_3+\,x_4)^2+(x_5+\,x_6+\,x_7+\,x_8)^2=a^2+(8-a)^2=2a^2+16a+64=2(a-4)^2+32\ge 34$ as $(a-4)^2> 1$.

34 can be attained by letting $x_1,\,x_2,\,x_3,\,x_4$ be distinct elements in the set $\{-7,\,-5,\,2,\,13\}$.
 

FAQ: How to Minimize the Sum of Squares with Given Distinct Elements?

What is the minimum value of sum of squares?

The minimum value of sum of squares is the smallest possible value that can be obtained by adding the squares of a set of numbers. It is often used in statistical analysis to measure the variation or dispersion of a data set.

How is the minimum value of sum of squares calculated?

The minimum value of sum of squares can be calculated by taking the sum of the squared differences between each data point and the mean of the data set. This is also known as the squared error and is represented by the equation ∑(x - x̄)^2.

What is the significance of the minimum value of sum of squares in statistics?

The minimum value of sum of squares is an important measure in statistics as it helps to assess the quality of a regression model or the spread of a data set. It is used to find the best fit line or curve for a set of data points and can also be used to compare the effectiveness of different models.

Can the minimum value of sum of squares be negative?

No, the minimum value of sum of squares cannot be negative. This is because it is the sum of squared values, which are always positive. However, the value can be zero if there is no variation in the data set.

How is the minimum value of sum of squares affected by outliers?

The presence of outliers in a data set can significantly impact the minimum value of sum of squares. Outliers are extreme values that differ significantly from the rest of the data and can skew the results. In such cases, it is common to remove outliers or use robust statistical methods to mitigate their effects on the minimum value of sum of squares.

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