Why this maximization approach fails?

In summary, the goal is to find all points where the direction of fastest change of the function f(x,y) = x^2 + y^2 -2x - 2y is in the direction of <1,1>. The approach of setting the gradient of the function to <1,1> does not work, but setting the partial derivatives of x and y to equal values does. This makes sense because the partial derivatives represent the rate of change in the x and y directions. The function is a paraboloid that opens upwards.
  • #1
friendbobbiny
49
2

Homework Statement


Find all points at which the direction of fastest change of the function [tex] f(x,y) = x^2 + y^2 -2x - 2y [/tex]is in the direction of <1,1>.

Homework Equations


[tex] <\nabla f = \frac{\delta f}{\delta x} , \frac{\delta f}{\delta y} , \frac{\delta f}{\delta z}>[/tex]

The Attempt at a Solution


[tex] \frac{\nabla f}{|\nabla f|}[/tex] = <1,1>

This doesn't work but

[tex]\frac{\delta f}{\delta x} = \frac{\delta f}{\delta y}[/tex]

does. This latter approach makes sense. The partial derivatives at x and y have the same value. The former approach should work, however. In general, [tex] \nabla f [/tex] gives the direction of maximum change.
 
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  • #2
NVM

set [tex] \frac{\nabla f}{|\nabla f|}[/tex] to <root(2)/2, root(2)/2>

Resolved!
 
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Likes davidmoore63@y and berkeman
  • #3
friendbobbiny said:

Homework Statement


Find all points at which the direction of fastest change of the function [tex] f(x,y) = x^2 + y^2 -2x - 2y [/tex]is in the direction of <1,1>.

Homework Equations


[tex] <\nabla f = \frac{\delta f}{\delta x} , \frac{\delta f}{\delta y} , \frac{\delta f}{\delta z}>[/tex]
Your function is one with two variables, so the gradient should be a vector with two components, not three.
$$\nabla f = <\frac{\partial f}{\partial x}, \frac{\partial f}{\partial y}>$$
friendbobbiny said:

The Attempt at a Solution


[tex] \frac{\nabla f}{|\nabla f|}[/tex] = <1,1>

This doesn't work but

[tex]\frac{\delta f}{\delta x} = \frac{\delta f}{\delta y}[/tex]

does. This latter approach makes sense. The partial derivatives at x and y have the same value. The former approach should work, however. In general, [tex] \nabla f [/tex] gives the direction of maximum change.
Do you have a feel for what the surface for this function looks like? The graph of the surface is a paraboloid that opens upward.
 
  • #4
friendbobbiny said:

Homework Statement


Find all points at which the direction of fastest change of the function [tex] f(x,y) = x^2 + y^2 -2x - 2y [/tex]is in the direction of <1,1>.

Homework Equations


[tex] <\nabla f = \frac{\delta f}{\delta x} , \frac{\delta f}{\delta y} , \frac{\delta f}{\delta z}>[/tex]

Just a note on Latex. First, your [itex]\nabla F[/itex] belongs outside the < > denoting the vector. Second you can use "\partial" to indicate partial derivatives rather than "\delta":
[tex] \nabla f = <\frac{\partial f}{\partial x} , \frac{\partial f}{\partial y} , \frac{\partial f}{\partial z}>[/tex]

3. The Attempt at a Solution
[tex] \frac{\nabla f}{|\nabla f|}[/tex] = <1,1>

This doesn't work but

[tex]\frac{\delta f}{\delta x} = \frac{\delta f}{\delta y}[/tex]

does. This latter approach makes sense. The partial derivatives at x and y have the same value. The former approach should work, however. In general, [tex] \nabla f [/tex] gives the direction of maximum change.
 

FAQ: Why this maximization approach fails?

Why is a maximization approach not always effective?

A maximization approach may fail in situations where there are multiple conflicting objectives, limited resources, or uncertainty in the data. In these cases, it may be more beneficial to use a different approach, such as a satisficing or optimization approach.

What are some common reasons for a maximization approach to fail?

Some common reasons for a maximization approach to fail include unrealistic assumptions, inadequate data or information, and changing external factors. It is important to carefully consider the limitations and assumptions of any approach before applying it to a problem.

Can a maximization approach fail due to human error?

Yes, a maximization approach can fail due to human error. This can happen if the data is not properly collected or analyzed, if there are errors in the optimization algorithm, or if the results are misinterpreted. It is important to double check all inputs and outputs and to carefully review the results to avoid potential errors.

Are there situations where a maximization approach is not appropriate?

Yes, there are situations where a maximization approach may not be appropriate. For example, if the problem at hand is complex and has multiple objectives, a maximization approach may not be able to adequately capture all the trade-offs and constraints. In these cases, a different approach may be more suitable.

How can we determine if a maximization approach is the best approach for a problem?

To determine if a maximization approach is the best approach for a problem, it is important to carefully consider the problem at hand and its specific characteristics. This includes identifying the objectives, constraints, and available resources. Additionally, it can be helpful to compare the results of a maximization approach with those of other approaches, such as optimization or satisficing, to determine the most suitable approach for the problem.

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