Two-Variable Optimisation Confusion

In summary, for a multivariate function, a point is a stationary point if all of the partial derivatives are zero. In this case, the function is f(x,y) = xe-x(y2 - 4y) and the stationary points are (1,4), (1,0), (0,2). However, it is important to note that for the partial derivative fy(x,y), the solutions would also include (0,y) and (x,2) as any value for x and y can make the partial derivative equal to zero.
  • #1
JoshMaths
26
0
Hi,

So f(x,y) = xe-x(y2 - 4y)
Find all stationary points and classify them i got

for fx(x,y) s.p (1,4),(1,0)
for fy (x,y) s.p (0,2)

I thought that you don't need double differentials at this stage and if it is a s.p it must satisfy

for fx(x0,y0) = 0
for fy (x0,y0) = 0

which means s.p must hold for both partial derivatives?

I know the s.ps are wrong so if someone could advise that would be great.

Josh
 
Physics news on Phys.org
  • #2
For a multivariate function, a point is a stationary point (better: a critical point) if all of the partial derivatives are zero. In your case, you need fx(x,y) and fy(x,y) to be zero for a point (x,y) to be a critical point. You went wrong in another regard as well. Consider ##\frac{\partial f(x,y)}{\partial y} = xe^{-x}(2y-4)##. You have (0,2) as the only point at which fy(x,y)=0. That's not correct. fy(x,y) is a product. A product is zero if any of its factors is zero. Thus fy(x,y) is zero whenever x is 0 or when y is 2.
 
  • #3
Ah so for fy(x,y) = 0 the solutions would be (0,y) and (x,2) as you can have any value for x and y for respective values that make the partial derivative zero?

Thanks for your help.
 

Related to Two-Variable Optimisation Confusion

1. What is two-variable optimization confusion?

Two-variable optimization confusion refers to the confusion that arises when trying to optimize a function with two independent variables. This can occur when there is no clear direction for improvement or when the two variables have conflicting relationships with the function.

2. How do I know if a function has two-variable optimization confusion?

A function may have two-variable optimization confusion if it has multiple local maxima or minima, or if changing one variable in a certain direction does not consistently result in an improvement in the function.

3. What causes two-variable optimization confusion?

Two-variable optimization confusion can be caused by a variety of factors, such as complex relationships between the two variables, non-linear relationships, or the presence of constraints on the variables.

4. How can I solve two-variable optimization confusion?

Solving two-variable optimization confusion requires careful analysis of the function and its relationship to the two variables. This may involve graphing the function, finding critical points, and considering the constraints on the variables.

5. Are there any strategies for avoiding two-variable optimization confusion?

There are several strategies that can help avoid two-variable optimization confusion, such as simplifying the function, breaking it down into smaller parts, and carefully considering the relationship between the two variables. Additionally, using optimization techniques such as gradient descent can help navigate complex functions with multiple variables.

Similar threads

Replies
3
Views
1K
  • Calculus and Beyond Homework Help
Replies
11
Views
2K
  • Calculus and Beyond Homework Help
Replies
1
Views
2K
  • Calculus and Beyond Homework Help
Replies
2
Views
3K
Replies
1
Views
2K
  • Calculus and Beyond Homework Help
Replies
3
Views
1K
  • Calculus and Beyond Homework Help
Replies
10
Views
2K
  • Introductory Physics Homework Help
Replies
1
Views
2K
  • Calculus and Beyond Homework Help
Replies
6
Views
2K
  • Quantum Physics
Replies
9
Views
1K
Back
Top