Optimization, possibly just algebra help

In summary: I meant the formulae you gave for fx and fy. I did not mean you should set them equal to each other - that would not help!What I meant can be seen from the following example.Eliminate y from the two equationsx = 2y, x+y = 4or, equivalently, fromx = 2y, y = 4-xand you getx = 2(4-x)which is the same as3x = 8 or x = 8/3Now you can check this in the original equations.You can also eliminate x from the equationsx = 2y, x+y = 4and now you get0 =
  • #1
musicmar
100
0

Homework Statement


Find the critical points of the function. Then use the second derivative test to determine whether they are local minima or maxima(or state that the test fails).

f(x,y)=(x-y)(e(x2-y2))

The Attempt at a Solution


fx=(x-y)(2x(e(x2-y2)))+(e(x2-y2))=0

fy=(x-y)(-2y(e(x2-y2)))-(e(x2-y2))=0

(e(x2-y2))((x-y)(2x)-1)=0

e^anything can never be 0, so:
2x2-2xy-1=0

fy=(-2yx+2y-1)(e(x2-y2))=0
(2y2-1)/(-2y)=x

Substituting into fx:

2((2y2-1)/(-2y))2-2y((2y2-1)/(-2y))-1=0

((4y4-4y2+1)/-2y2)+2y2-2=0

-2y2+2-(1/2)y-2-2=0

(-1/2)y-2=0


Here is where I run into problems. y-2 can never be 0.

If someone could check my derivatives and my algebra, that would be great.
Thanks.
 
Physics news on Phys.org
  • #2
Well firstly the case that a derivative is never 0 would not be not a problem in itself - it would mean that the function has no extremum which is perfectly possible!

Your problem is at y=0.

Assuming no mistake, I think your result would sufficiently demonstrate that there is no extremum of the function except possibly at y=0. Where y=0 your treatment involving division by y which is valid everywhere else is invalid - you are not allowed to divide by 0!
If it is any consolation I believe Einstein once published a mistaken conclusion traced to having divided by something that was, not too obviously, 0.

In any case just look at the formulae for fx and fy (which you have not written out explicitly) and I think it can easily be seen that neither can be 0 at y=0.

Just put the two results fx = 0, fy = 0 side by side and it should be easy to eliminate between them without having to do any divisions. However there is more than one way to do this. If I am not mistaken (:rolleyes:don't count on it) you can prove there is no extremum, and can do it without slogging (which is often a warning of a wrong or at least not best approach).

(Any equation you get eliminating between the two has to be true at an extremum - but the converse is not true!)
Slightly tricky.
 
Last edited:
  • #3
What do you mean by the "formulae for fx and fy"? fx is simply the derivative of f with respect to x, is it not?

And by putting them side by side, do you mean I should set them equal to each other, because my professor specifically said not to do that.
 
Last edited:
  • #4
musicmar said:
What do you mean by the "formulae for fx and fy"? fx is simply the derivative of f with respect to x, is it not?

And by putting them side by side, do you mean I should set them equal to each other, because my professor specifically said not to do that.

Write the two equations out without the exponential. x=0 doesn't lead to a critical point nor does y=0. So you can feel free to divide by y as you've done. I haven't checked all of the algebra, but I think you are correct to conclude that there are no critical points.
 
  • #5
Well you might have made a mistake, anyway I get
for the fx factor

1 + 2x2 - 2xy = g1 say ...(1)

and for the fy factor

-1 + 2y2 - 2xy = g2 say ...(2)Now actually from either of these in fact you can easily see that y=0 cannot possibly correspond to g2 = 0 and therefore not to fy=0 (nor, superfluously, to g1=0 and fx=0). So if you have already concluded that there are no critical points anywhere except possibly where y = 0 this now tells you there are no critical points anywhere.Concerning my other suggestion of elimination. At the critical point fx=0 and fy=0 which only happens as you say when g1=0 and g2=0.

So at the critical point

g1 = g2 = 0 .

So it is absolutely true that you can say that

g1 = g2 ...(3)

at a critical point! However without the =0 bit eq.(3) can also be true at points, maybe an infinite number of, that are not critical points. So you can see why your Prof. says do not do (3) because most students will just treat this as mindless equation grinding and just assume without thinking one equation they somehow got from another is true and so get false results.

But think also: if you show eq. (3) cannot be true anywhere, it does follow that there are no critical points!

Actually what I thought to do, as eq.(3) does not seem to lead to anything convenient was to say equally at the critical point

g1 = -g2 = 0

and just the first equation leads to x=y . But you can easily see from (1) and (2) that when x=y neither g1 nor g2 can be 0, so there cannot be any critical points.

Actually I think you can use for such a problem

Afx = Bfy

where A, B are anything (not 0) useful e.g. for eliminating one of x, y to obtain a critical point as long as you check the results. But as I do not remember having heard of such an approach it is open to criticism.

And my argument/calculation was simpler than yours but trickier so maybe your approach was safer!
 
Last edited:

FAQ: Optimization, possibly just algebra help

What is optimization?

Optimization is the process of finding the best possible solution to a problem, given a set of constraints. In mathematics, optimization involves maximizing or minimizing an objective function in order to find the optimal value of a variable.

What is the difference between local and global optimization?

Local optimization refers to finding the best solution within a specific region or domain, while global optimization involves finding the overall best solution regardless of the region or domain. Local optimization can be easier to solve, but it may not always lead to the globally optimal solution.

How is optimization used in real life?

Optimization has many practical applications in various fields such as engineering, economics, and business. It is used to improve efficiency, minimize costs, and maximize profits. Some examples include optimizing production processes, scheduling tasks, and designing transportation routes.

What are the common techniques used in optimization?

Some commonly used techniques in optimization include linear programming, nonlinear programming, genetic algorithms, and gradient descent. These methods involve using mathematical models and algorithms to find the optimal solution to a given problem.

How can I improve my optimization skills?

To improve your optimization skills, it is important to have a strong foundation in mathematics, particularly in calculus and linear algebra. It is also helpful to practice solving different types of optimization problems and familiarize yourself with various optimization techniques and algorithms.

Back
Top