Optimization/maximization with multivariable calculus

In summary, the conversation is about a problem in calculus where the task is to maximize the Riemann Sum subject to certain constraints. The student is struggling with understanding the concept and asks for help in finding the solution. Suggestions are made to use partial derivatives or Lagrange multipliers, but ultimately, it is suggested to think about the problem in terms of vectors. The thread starter has not been active since 2007, so it is unlikely to receive a response from them.
  • #1
adradmin
4
0

Homework Statement



Maximize the Riemann Sum ##\Sigma^{n}_{i=1}x_{i}*y_i## subject to constraints ##\Sigma^{n}_{i=1}x^{2}_{i}=1## and ##\Sigma^{n}_{i=1}y^{2}_{i}=1 ##

Homework Equations


My teacher doesn't speak English very well. I'm in Calculus 3 and the average on his exams are around 40%. I'm a good Engineering student and need help. In class, we did an optimization problem where you can maximize the volume of say a box since you know length, width, height. We never went over this and I'm wondering how to do this. Despite him teaching us derivatives in that section, should I approach by doing integration and find the bounded region of x^2 and y^2 individually? Limits were never taught that much in high school so I really would appreciate your help.

The Attempt at a Solution


Maybe try the partial derivatives of the last two equations with respect to x and y, then set them to zero to find critical points? That's the only way I knew how to find local max and absolute max. Please help if you can. Thanks guys.
 
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  • #2
You could try Lagrange multipliers, but in this case the easiest is probably to think what it means in terms of vectors. What should the relationship be between ##\vec x## and ##\vec y##?
 
Last edited:
  • #3
haruspex said:
I assume it should read <snip>
So do I, and I have edited the OP to read the same as what you wrote. I doubt we'll hear from the thread starter, who hasn't been back since 2007.
 
  • #4
Mark44 said:
I doubt we'll hear from the thread starter, who hasn't been back since 2007.
Yes, it is from the tranche of old unanswered threads that came in from MHB. My approach to these is to answer exactly as I would for a fresh thread.
 
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1. What is optimization or maximization in multivariable calculus?

Optimization or maximization in multivariable calculus is the process of finding the maximum or minimum value of a multivariable function. This involves finding the values of the independent variables that will result in the highest or lowest value of the dependent variable.

2. What is the difference between optimization and maximization?

Optimization and maximization both involve finding the best possible value of a function, but they differ in their goals. Optimization aims to find the overall best value, whether it is the maximum or minimum, while maximization specifically seeks to find the highest value.

3. What is the role of partial derivatives in optimization?

Partial derivatives are used in optimization to calculate the rate of change of a multivariable function with respect to each of its independent variables. This allows us to identify the critical points of the function, which are the potential maximum or minimum points.

4. How do you determine if a critical point is a maximum, minimum, or saddle point?

To determine the nature of a critical point, we use the second derivative test. If the second derivative is positive at the critical point, it is a minimum. If the second derivative is negative, it is a maximum. If the second derivative is zero, further analysis is needed to determine if it is a saddle point.

5. Can optimization or maximization be applied in real-world situations?

Yes, optimization and maximization with multivariable calculus are frequently used in real-world situations. For example, businesses may use it to determine the most profitable price for a product, or engineers may use it to design the most efficient structure. It is a valuable tool for decision-making and problem-solving in various fields.

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