Jacky L's question at Yahoo Answers regarding minimizing a sum of squares

In summary, the question is asking to show that the sum of two numbers is at least half of the square of their sum. Using Lagrange multipliers and the second derivative test, it can be shown that the minimum value of the objective function is equal to half of the square of the sum, confirming the statement. Other optimization questions can be posted in the forum provided.
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  • #2
Hello Jacky L,

Let's let one number be $x$ and the other be $y$. The expression we want to minimize, our objective function is:

\(\displaystyle f(x,y)=x^2+y^2\)

subject to the constraint:

\(\displaystyle g(x,y)=x+y-k=0\)

Now, using Lagrange multipliers, we obtain the system:

\(\displaystyle 2x=\lambda\)

\(\displaystyle 2y=\lambda\)

which implies:

\(\displaystyle x=y\)

and substitution into the constraint tells us:

\(\displaystyle x=y=\frac{k}{2}\)

which means there is one extremum for the objective function at:

\(\displaystyle f\left(\frac{k}{2},\frac{k}{2} \right)=\frac{1}{2}k^2\)

We may verify this is the minimum by observing:

\(\displaystyle f(0,k)=f(k,0)=k^2\)

We could also proceed by using:

\(\displaystyle y=k-x\) and so:

\(\displaystyle f(x)=x^2+(k-x)^2=2x^2-2kx+k^2\)

Since $f(x)$ is a quadratic, we may simply find the axis of symmetry to determine the critical value:

\(\displaystyle x=-\frac{-2k}{2\cdot2}=\frac{k}{2}\)

Since the parabola opens upwards, we know the vertex is the global minimum.

If you are to use the calculus to determine the critical value, we would begin by differentiating with respect to $x$, and equating this to zero, and solving for $x$ and this will reveal the critical value:

\(\displaystyle f'(x)=4x-2=0\,\therefore\,x=\frac{k}{2}\)

Using the second derivative test, we find:

\(\displaystyle f''(x)=4>0\)

Hence the extremum at the critical value is a minimum, and so:

\(\displaystyle f_{\text{min}}=f\left(\frac{k}{2} \right)=\frac{1}{2}k^2\)

To Jacky L, and any other guests viewing this topic, I invite and encourage you to post other optimization questions in our http://www.mathhelpboards.com/f10/ forum.

Best Regards,

Mark.
 

Related to Jacky L's question at Yahoo Answers regarding minimizing a sum of squares

1. What is "minimizing a sum of squares"?

"Minimizing a sum of squares" is a mathematical concept that involves finding the values of variables that will result in the smallest possible sum of the squared differences between the observed data and the predicted data. It is commonly used in regression analysis and optimization problems.

2. Why is minimizing a sum of squares important?

Minimizing a sum of squares is important because it allows us to find the best fit for a given set of data. By minimizing the sum of squares, we are essentially finding the line or curve that will most accurately represent the relationship between the variables in the data.

3. How is minimizing a sum of squares calculated?

The process of minimizing a sum of squares involves finding the derivative of the sum of squares equation and setting it equal to zero. This will give us the values of the variables that will result in the minimum sum of squares. This process is typically done using computer algorithms and mathematical software.

4. What are some real-world applications of minimizing a sum of squares?

Minimizing a sum of squares has many practical applications, such as in linear regression to predict future trends, in finance to optimize investment portfolios, and in engineering to design efficient systems. It is also used in data analysis to identify outliers and to improve the accuracy of statistical models.

5. Are there any limitations to minimizing a sum of squares?

While minimizing a sum of squares is a useful tool, it does have some limitations. For example, it assumes that the relationship between the variables is linear and that the data points have equal variance. It also does not account for any underlying factors that may affect the data, such as confounding variables. Therefore, it is important to consider these limitations when using this method in data analysis.

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