Minima of a multivariable function.

In summary, the minimum value of the function f(x,y)=e^{x+y}-2 within the constraints x≥0 and y≥0 is -1, which occurs at the point (0,0). The discriminant test is not applicable in this case as the function is monotonically increasing in the given region.
  • #1
Cpt Qwark
45
1

Homework Statement


Find the minimum value of [tex]f(x,y)=e^{x+y}-2[/tex] within x≥0 and y≥0.

Homework Equations


[tex]D=f_{xx}(a,b)f_{yy}(a,b)-[f_{xy}(a,b)]^2[/tex]
Answer is -1

The Attempt at a Solution


So for all partial derivatives I got [tex]e^{x+y}[/tex] (and mixed), but when I calculate the discriminate (subbing in (0,0) I get [tex]D=e⋅e-(e)^2=0[/tex].

I was just confused on how the answer is -1 when the discriminate has to be larger then zero to be a minimum point.
 
Physics news on Phys.org
  • #2
Does the function have local extrema? What is the condition that a local extreme exist?
 
  • #3
Cpt Qwark said:

Homework Statement


Find the minimum value of [tex]f(x,y)=e^{x+y}-2[/tex] within x≥0 and y≥0.

Homework Equations


[tex]D=f_{xx}(a,b)f_{yy}(a,b)-[f_{xy}(a,b)]^2[/tex]
Answer is -1

The Attempt at a Solution


So for all partial derivatives I got [tex]e^{x+y}[/tex] (and mixed), but when I calculate the discriminate (subbing in (0,0) I get [tex]D=e⋅e-(e)^2=0[/tex].

I was just confused on how the answer is -1 when the discriminate has to be larger then zero to be a minimum point.

When you have constraints (even simple ones like x,y >= 0) the usual first-and second-order tests must be modified. In other words, you cannot just look at the gradient and Hessian of f(x,y) at the solution point, if that point lies on the boundary. In such a problem the discriminant does NOT have to be > 0.

Anyway, in problems where it applies that is just a sufficient condition, not a necessary one. A correct statement---but not in this problem---is that if the discriminant is > 0 the point is a strict local minimum. The corresponding necessary condition is that the discriminant be >= 0, (not > 0).

In your case, the function f(x,y) is monotonically strictly increasing in the region x >=0, y >= 0, so the solution is at the boundary x = y = 0. Basically, no fancy tests are needed.
 
  • #4
So it's simply [tex]e^{(0)(0)}-2=-1?[/tex]
 
  • #5
Yes. Since the partial derivatives of [itex]e^{x+y}-2[/itex] are both positive for all [itex]x\ge 0[/itex], [itex]y\ge 0[/itex] are positive this function is always increasing as x and y increase so its minimum value must occur at (0, 0).
 

1. What is a minima of a multivariable function?

A minima of a multivariable function is a point on the function's graph that represents the lowest value of the function in a specific region. This point is also known as the local minimum, as it is the smallest value within a small neighborhood of the point.

2. How do you find the minima of a multivariable function?

To find the minima of a multivariable function, you must take the partial derivatives of the function with respect to each variable and set them equal to zero. Then, you can solve for the values of the variables that make the partial derivatives equal to zero. These values will give you the coordinates of the minima.

3. What is the difference between a local and global minima?

A local minima is the smallest value of a function within a small region, while a global minima is the smallest value of the function over its entire domain. Local minima can occur multiple times within a function, while global minima can only occur once.

4. Can a multivariable function have more than one minima?

Yes, a multivariable function can have multiple minima. These points may represent different local minima or be the same point that represents both a local and global minima. The number of minima a function has depends on the complexity and shape of the function.

5. How does the Hessian matrix help in finding the minima of a multivariable function?

The Hessian matrix is a square matrix of second-order partial derivatives of a multivariable function. By analyzing the eigenvalues of the Hessian matrix, we can determine whether a critical point (where the partial derivatives are equal to zero) is a minima, maxima, or saddle point. This can help in identifying and confirming the minima of a multivariable function.

Similar threads

  • Calculus and Beyond Homework Help
Replies
1
Views
466
  • Calculus and Beyond Homework Help
Replies
5
Views
550
  • Calculus and Beyond Homework Help
Replies
9
Views
593
  • Calculus and Beyond Homework Help
Replies
18
Views
1K
  • Calculus and Beyond Homework Help
Replies
5
Views
622
  • Calculus and Beyond Homework Help
Replies
20
Views
464
  • Calculus and Beyond Homework Help
Replies
2
Views
1K
Replies
4
Views
651
  • Calculus and Beyond Homework Help
Replies
8
Views
1K
  • Calculus and Beyond Homework Help
Replies
19
Views
966
Back
Top