Finding the Directional Derivative in Multivariable Calculus

In summary, the directional derivative of f(x) in the direction of the unit vector u is equal to (2L(x)*gradient(x))*u.
  • #1
Carl140
49
0

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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  • #2
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?
 
  • #3
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.
 
  • #4
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.
 

Related to Finding the Directional Derivative in Multivariable Calculus

1. What is multivariable calculus?

Multivariable calculus is a branch of mathematics that deals with functions of several variables, also known as multivariate functions. It extends the concepts and techniques of single-variable calculus to functions with multiple independent variables.

2. What are some practical applications of multivariable calculus?

Multivariable calculus has numerous practical applications in fields such as physics, engineering, economics, and statistics. Some examples include modeling complex systems, optimization problems, and analyzing changing rates of multiple variables.

3. What are the basic concepts and techniques of multivariable calculus?

The basic concepts and techniques of multivariable calculus include partial derivatives, multiple integrals, vector calculus, and optimization. These concepts are used to analyze and solve problems involving functions with multiple variables.

4. How does multivariable calculus differ from single-variable calculus?

In single-variable calculus, the focus is on functions with one independent variable, while in multivariable calculus, the focus is on functions with multiple independent variables. This leads to a more complex analysis and use of techniques such as partial derivatives and multiple integrals.

5. What are some tips for studying multivariable calculus?

Some tips for studying multivariable calculus include practicing problems, understanding the underlying concepts and connections between them, and seeking help from professors or tutors when needed. It is also important to have a solid understanding of single-variable calculus before delving into multivariable calculus.

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