Partial derivatives with Gradient and the chain rule

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Homework Help Overview

The discussion revolves around partial derivatives, gradients, and the chain rule in the context of two problems involving functions of multiple variables. The first problem involves determining the directions of increase and decrease of a function defined as f(x,y) = x - y, while the second problem requires calculating a second partial derivative of a function z = f(x,y) with respect to variables s and t.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the use of gradients to identify directions of increase and decrease for the first problem. There is an exploration of the relationship between the directional derivative and the gradient vector. For the second problem, attempts to apply the chain rule and compute second derivatives are noted, along with questions regarding the correctness of the derived expressions.

Discussion Status

Some participants confirm the correctness of initial attempts and provide hints to facilitate further understanding. There is an ongoing exploration of the implications of the gradient and its relationship to directional derivatives. Multiple interpretations of the problems are being examined, with participants expressing gratitude for clarifications that simplify their thought processes.

Contextual Notes

Participants are navigating a new subject area, which may contribute to initial confusion regarding the application of concepts. There is mention of the simplicity of the function in the first problem, suggesting a potential for visual representation to aid understanding.

Kruum
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Homework Statement



First problem: Let [tex]f(x,y) = x-y[/tex] and u = vi+wj. In which direction does the function decrease and increase the most? And what u (all of them) satisfies Duf = 0

Second problem: Let [tex]z = f(x,y)[/tex], where [tex]x = 2s+3t[/tex] and [tex]y = 3s-2t[/tex]. Determine [tex]\partial{z^2}/\partial{s^2}[/tex]

Homework Equations



Gradient and the chain rule

The Attempt at a Solution



For the first question in the first problem I've gotten using gradient: increase i-j and decrease -i+j. Am I correct? For the second question all I've gotten so far is (nabla)f(dot)u = 0 = (1-y)v+(x-1)w. Where do I get the second equation to solve both v and w?

Second problem gives me 4zxx+12zxy+9zyy. Is that completely wrong?
 
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What you have done so far is correct. For the second part, here's any easy hint: in 2 dimensions, a perpendicular to vector <a, b> is <-b, a>.


For the second problem, [tex]f_s= f_x x+_s+ f_y y_s= 2f_x+ 3f_y[/tex] .
That applies to any function of x,y so it applies to [tex]f_s[/tex] itself.

[tex]f_{ss}= (f_s)_s= 2(2f_x+ 3f_y)_x+ 3(2f_x+ 3f_y)_y= 4f_{xx}+ 6f_{yx}+ 6f_{xy}+ 9f_yy[/tex]
and since, for this function, with continuous second derivatives, the mixed derivatives are the same,
[tex]f_ss= 4f_{xx}+ 12f_{xy}+ 9f_{yy}[/tex]
exactly as you say.
 
HallsofIvy said:
What you have done so far is correct. For the second part, here's any easy hint: in 2 dimensions, a perpendicular to vector <a, b> is <-b, a>.

So I just mark (1-y)w-(x-1)v = 0?

For the second problem, [tex]f_s= f_x x+_s+ f_y y_s= 2f_x+ 3f_y[/tex] .
That applies to any function of x,y so it applies to [tex]f_s[/tex] itself.

Thank you, I never thought of that and it will make things much easier.
 
Kruum said:
So I just mark (1-y)w-(x-1)v = 0?
?? You had already found that the gradient was i- j and you appeared to know that the directional derivative, in the direction of unit vector [itex]\vec{i}[/itex] is the dot product [itex]\nabla f\cdot \vec{v}[/itex]. That will be 0 if and only if the two vectors are orthogonal: the direction in which the derivative is 0 is perpendicular to the gradient vector, i+ j.

Thank you, I never thought of that and it will make things much easier.
 
HallsofIvy said:
?? You had already found that the gradient was i- j and you appeared to know that the directional derivative, in the direction of unit vector [itex]\vec{i}[/itex] is the dot product [itex]\nabla f\cdot \vec{v}[/itex]. That will be 0 if and only if the two vectors are orthogonal: the direction in which the derivative is 0 is perpendicular to the gradient vector, i+ j.

Thank you. This is a new subject for me and my thinking was too complicated. I found that the vector is either [itex]\vec{i}+\vec{j}[/itex] or [itex]-\vec{i}-\vec{j}[/itex].

And I just realized that this function is so simple that I could have found the vectors by giving the function a set value and draw it in 2-dimensions. I know there is a fancy name for that, but I've no idea what it is in English.
 

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