Finding the Directional Derivative in Multivariable Calculus

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Homework Help Overview

The discussion revolves around finding the directional derivative of a function defined as f: R^n -> R, specifically f(x) = x*L(x), where L is a linear function. Participants are exploring the mathematical concepts involved in calculating the directional derivative f'(x;u) in the context of multivariable calculus.

Discussion Character

  • Exploratory, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • The original poster attempts to derive the gradient of the function and expresses uncertainty about the correctness of their steps, particularly regarding the linearity of L and its implications for the gradient. Other participants suggest considering the function in component form and using matrix notation to clarify the gradient calculation.

Discussion Status

Participants are actively engaging with the problem, with some providing guidance on expressing the function and its gradient in terms of matrices. There is a focus on understanding the product rule and the implications of linear maps, but no consensus has been reached on the correctness of the original poster's approach.

Contextual Notes

There is an emphasis on the need for clarity in notation and understanding of matrix operations, as the original poster expresses confusion about transitioning from functions to matrix representations. The discussion is framed within the constraints of a homework assignment, which may limit the extent of guidance provided.

Carl140
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Homework Statement



Let f: R^n -> R be defined as follows:

f(x) = x*L(x) where * denotes the standard inner product and L: R^n -> R^n is a linear
function.
I'm trying to find the directional derivative f'(x;u).


Homework Equations



I know that f'(x;u) (the directional derivative of f(x) in the direction of the unit vector u)
is equal to gradient(f(x)) * u where * denotes inner product.

The Attempt at a Solution



In this case gradient(f(x)) = gradient(x*L(x)) = gradient(x)*L(x) + L(x)*gradient(x)
(Not sure if this step is correct).
Then because L: R^n -> R^n is a linear map the differential of L is L again, no?
So we get: gradient(f(x)) = gradient(x)*L(x) + L(x)*gradient(x) = 2L(x)*gradient(x).

Conclusion: the directional derivative in the direction of u is then (2L(x)*gradient(x))*u where u is a unit vector.

Is this correct? I'm a little bit confused about the part of L being a linear map, is it correct
to state that the gradient is again L itself?
 
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You are writing expressions that don't really make sense. Think about what f(x) looks like in components. It's f(x)=x_i*L_ij*x_j (where x_i are the components of x, L_ij is the matrix representing L and i and j are summed from 1...n). The kth component of the gradient is df/x_k. Can you think how to write that concisely using matrices?
 
Ok Dick, thanks for your reply. So
I think f(x) = x^t * A * x where x represents the column vector and A is the matrix
which represents L. Is this correct so far?
Then how can I take the gradient? I'm really confused about this matrices thing,
all I'm used to is functions.
 
Yes, now write it in indices. x_i*A_ij*x_j (summed over i and j). It's a quadratic form. You need to use the product rule. Take d/dx_k of it to get the kth component of the gradient. You get something nonzero if either i=k or j=k. So d/dx_k is A_kj*x_j+x_i*A_ik where the first is summed over i and the second is summed over j. Express that in terms of matrix products. I know I'm repeating myself, but I don't know what else to say.
 

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