What is the Derivative of Functions from R2->R2 and How is it a Linear Map?

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The discussion focuses on the differentiation of functions from R2 to R2, specifically examining the function f(x,y) = (x, -y). It is established that the derivative of such functions is represented as a linear map through its Jacobian matrix, which in this case is a 2x2 matrix. The derivative at a point is defined as the linear function that best approximates the original function at that point. For the function f(x,y), the Jacobian matrix is given by the columns (1,0) and (0,-1).

PREREQUISITES
  • Understanding of R2 vector spaces
  • Familiarity with the concept of derivatives in multivariable calculus
  • Knowledge of Jacobian matrices and their applications
  • Basic principles of linear algebra
NEXT STEPS
  • Study the definition and properties of Jacobian matrices in multivariable calculus
  • Read "Elements of Real Analysis" by Bartle for a deeper understanding of differentiation
  • Explore the concept of linear approximations of functions in R2
  • Learn about the application of derivatives in optimization problems
USEFUL FOR

Students of multivariable calculus, mathematicians, and anyone interested in understanding the differentiation of functions from R2 to R2, particularly in the context of linear mappings and Jacobian matrices.

domhal
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I would like to learn about differentiation of functions from R2->R2. Such functions are mentioned briefly at the beginning of the text for my complex analysis course for sake of comparison. However, I find that I don't know very much about them.

The book considers f(x,y) = (x, -y), saying that f is differentiable and that "Its derivative at a point is the linear map given by its Jacobian...". I don't understand, for example, how the derivative is a linear map. I associate (perhaps incorrectly) the derivative with a rate of change. Could someone recommend some resources (books, websites etc) for understanding the differentiation of such functions?

Domhal
 
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Think about this definition of the derivative, implicitly:
[tex]\lim_{h \rightarrow 0} \left ( f(\mathbf{x + h}) - f(\mathbf{x}) = \mathbf{h} Df(x) \right )[/tex]
In this case, if [tex]\mathbf{h} \in R^2[/tex] and [tex]f(\mathbf{x}) :R^2 \rightarrow R^2[/tex] then, for the dimensionality to make sense, [tex]Df(\mathbf{x})[/tex] has to be a 2x2 matrix.

I strongly recommend the Bartle Elements of Real Analysis to read more on this. It's chapters on many variable functions are quite interesting and thorough, as is the rest of the book.
 
A more general definition of the derivative of any function (at a point) is the linear function that best approximates the function at that point.

In that sense, the derivative of, say, y= x2, at x= 1 is not the number 2 but the linear function y= 2x.

The derivative of f(x,y)= (x,-y) is the linear function whose matrix (in the i, j basis) is the matrix with columns (1,0), (0, -1).
 

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