How to Solve Optimization Problems with Multiple Variables and Constraints?

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SUMMARY

This discussion focuses on solving optimization problems involving multiple variables and constraints, specifically addressing methods for maximizing or minimizing functions. Key points include the necessity of evaluating both interior points where the gradient \nabla f = 0 and boundary points, particularly in compact domains. The conversation highlights the importance of examining boundaries defined by inequalities and suggests that in non-compact domains, local maxima and minima should be compared to behavior as variables approach infinity. The example function f(x, y) = (x^2 + y)e^{-x-y} illustrates the process of finding critical points and evaluating potential extrema.

PREREQUISITES
  • Understanding of multivariable calculus, including gradients and critical points.
  • Familiarity with boundary conditions and constraints in optimization problems.
  • Knowledge of compact and non-compact domains in mathematical analysis.
  • Ability to analyze functions and their limits as variables approach infinity.
NEXT STEPS
  • Study the method of Lagrange multipliers for constrained optimization.
  • Learn about the second derivative test for functions of multiple variables.
  • Explore numerical optimization techniques for complex functions.
  • Investigate the behavior of functions at infinity and asymptotic analysis.
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Mathematicians, engineers, and data scientists involved in optimization problems, particularly those working with multivariable functions and constraints.

Inertigratus
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Well, I'm having trouble doing optimization problems (maximizing and/or minimizing a function in more then one variable with/without constraints).

Would be a great help if someone could give me some good links on this topic or some methods generally.

If the domain is compact; where are the points that could possibly maximize/minimize the function?
Is it either points that satisfy the equation \nablaf = 0 and points on the boundary?
In one problem I did, the point that maximized the function didn't satisfy \nablaf = 0, how come?

How do I examine the boundary? if the domain is defined by an inequality and the equality corresponds to the boundary, do I just solve for either variable and plug into the original equation? What if it's a three variable function?

If the domain isn't compact, and both x and y go from 0 to infinity, what do I do then?
 
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For example if I want to find the optima on the boundary of (if they exist): f(x, y) = (x^2 + y)e^{-x-y} and:
0 \leq x \leq \infty , 0 \leq y \leq \infty
I can check when either variable is 0, what else can I do?
 
Inertigratus said:
Well, I'm having trouble doing optimization problems (maximizing and/or minimizing a function in more then one variable with/without constraints).

Would be a great help if someone could give me some good links on this topic or some methods generally.

If the domain is compact; where are the points that could possibly maximize/minimize the function?
Is it either points that satisfy the equation \nablaf = 0 and points on the boundary?
Yes, either a point in the interior such that \nabla f= 0 or a point on the boundary.

In one problem I did, the point that maximized the function didn't satisfy \nablaf = 0, how come?
Then it must have been a point on the boundary.

How do I examine the boundary? if the domain is defined by an inequality and the equality corresponds to the boundary, do I just solve for either variable and plug into the original equation? What if it's a three variable function?
If the original domain is n-dimensional, then its boundary is n-1 dimensional. You should be able to write the boundary in terms of n-1 parameters (possibly by solving the equation for the boundary for one of the variables in terms of the remaining n-1 variables). Then solve the n-1 dimensional problem, including looking at its boundary.

If the domain isn't compact, and both x and y go from 0 to infinity, what do I do then?
Then there may not be a max or min. Go ahead and find what local max and min you have, compare to what happens as x and y go to infinity.

Inertigratus said:
For example if I want to find the optima on the boundary of (if they exist): f(x, y) = (x^2 + y)e^{-x-y} and:
0 \leq x \leq \infty , 0 \leq y \leq \infty
I can check when either variable is 0, what else can I do?
Yes, the boundary consists of the lines x= 0 and y= 0. On x= 0, [/itex]f(0, y)= ye^{-y}. f&amp;#039;= e^{-y}- ye^{-y}= 0 when y= 1. Similarly, on y= 0, f(x, 0)= x^2e^{-x}. f&amp;#039;= 2xe^{-x}- x^2e^{-x}= 0 when x= 0 or x= 2. <b>Possible</b> max and min are at (0, 1), (0, 0), and (2, 0). To determine if they are global max or min, compare the value of the function at those points with points where \nabla f= 0 and the limits as x and y go to infinity.
 

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