Optimization (min/max and concavity)

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In summary, the conversation discusses using the first and second derivative tests to find maximum and minimum values in optimization problems. The first derivative test involves finding x values that make f'(x)=0 and then checking if f(x) is increasing or decreasing on intervals around those x values. The second derivative test involves finding x values that make f''(x)=0 and checking if f(x) is concave up or down on intervals around those x values. It is recommended to also learn the first derivative test as it may be easier to use. It is important to note that the second derivative test may fail if f''(x)=0. To determine if a point is a maximum or minimum, arbitrary points can be chosen and plugged into f
  • #1
Angry Citizen
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This isn't a homework question, although I am in a calculus course. I'm a little fuzzy on the method that I was taught (discover intervals and all that nonsense to make sure f'(x)=0 is a max or a min). I was curious if, when I discovered the values of x such f'(x)=0, I could then find f''(x)=0 to determine if each f'(x) is a max/min, or merely a concavity point (thus, if f''(x)=0 is the same as f'(x)=0, it isn't a max/min).

Thanks!
 
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  • #2
f'(x) = 0 is for finding x values that you then plug back into f(x) to find mins and maxes.
you then check an x value on the left and the right of the f'(x) = 0 x value to determine if f(x) is increasing or decreasing on that interval. f''(x) = 0 is for finding x values that are inflection points. (where concave up or down changes to the other) again, you try values to the right and left of f''(x) = 0 to find the intervals that it's concave up and down
 
  • #3
Yes you can use the second derivative test in optimization problems to verify that your x value is a maximum or minimum. if f is concave up (f''(x) is positive). you have a local minimum value. If it is concave down (f''(x) is negative) you have a local maximum. Although i would really recommend you take the time to learn the first derivative test with the intervals as it will often be easier than finding the second derivative. Additionally, the second derivative test fails when f''(x)=0 I have made an optimization tutorial on my website, please see the example and the first derivative test section. Here is a link: http://www.theoremsociety.com/forums/index.php?showtopic=6"
 
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  • #4
Once you find the values of x such that f'(x)=0 you can pick arbitrary points around that value of x to determine if the point is a max/min/neither. You can also plug the value into f''(x); if the number that comes out is <0 you have a maximum, >0 minimum. Does that make sense?
 
  • #5
Coriolis314 said:
Once you find the values of x such that f'(x)=0 you can pick arbitrary points around that value of x to determine if the point is a max/min/neither. You can also plug the value into f''(x); if the number that comes out is <0 you have a maximum, >0 minimum. Does that make sense?

I only apply this technique safely if the points represent the endpoints of a closed interval, following the extreme value theorem. If the endpoints are not clear or the interval is not closed, I generally use the first- or second derivative test (it is possible that there could be more that one local maxima and picking aribtrary points may not account for all of them- although this usually doesn't occur in elementary problems)
 

Related to Optimization (min/max and concavity)

1. What is optimization in scientific terms?

Optimization is the process of finding the best solution or outcome for a given problem or system. It involves maximizing or minimizing a specific objective function while considering various constraints and limitations.

2. What is the difference between minimizing and maximizing in optimization?

Minimizing refers to finding the lowest possible value of the objective function, while maximizing aims to find the highest possible value. Both involve finding the optimal solution, but they have opposite goals.

3. How is concavity related to optimization?

Concavity is a measure of how a function is curved. It is related to optimization because a function's concavity can affect the shape of the graph and the location of its minimum or maximum points. A function's concavity can also indicate whether it is a convex or concave function, which can impact the optimization process.

4. How do you determine the concavity of a function?

The second derivative of a function can be used to determine its concavity. If the second derivative is positive, the function is concave up, and if it is negative, the function is concave down. The inflection points, where the concavity changes, can also be found by setting the second derivative to zero and solving for x.

5. What are some practical applications of optimization in science?

Optimization is widely used in various fields of science, such as engineering, economics, and physics. Some practical applications include optimizing production processes, resource allocation, and logistics. In scientific research, optimization techniques are used to find the best models and parameters for data fitting and prediction.

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