Significance of derivative for functions of several variables

In summary: ,x_n-1,y_0,...,y_n-1,z_0) is the curve that passes through the point (x_0,...,x_n-1,y_0,...,y_n-1,z_0) with the smallest possible distance from the origin.
  • #1
AAQIB IQBAL
11
0
For single variable functions derivative means slope of tangent what does it mean for functions of more than one variable.
book says that a function is said to be differentiable if:
f(x + Δx , y + Δy) - f(x , y) = AΔx + BΔy + ε'ψ(Δx , Δy) + εh(Δx , Δy)
WHERE ε, ε' → 0 AS Δx , Δy → 0.
PLEASE PROVIDE SOME ASSISTANCE ON IT. :confused: :cry: :confused:

THANX IN ADVANCE
 
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  • #2
The derivative is the slope of the tangent to a "surface" in a particular direction. In the so f'(x=a) is usually the slope of the tangent to f at x=a in the x direction.
 
  • #3
AAQIB IQBAL is talking about "the derivative" as if it were a single number and, for functions of several variables, that is not true.

Simon Bridge is talking about the partial derivatives- imagine walking along a hillside (representing the surface z= f(x,y)) moving due east (the positive x-axis). The rate at which you go up or down is the partial derivative with respect to x. Moving northward (the positive y-axis) the rate at which you go up or down is the partial derivative with respect to y.

The closest thing to "THE" derivative is the gradient, [itex]\nabla f[/itex], which is a vector pointing in the direction of fastest increase whose length is the rate of increase in that direction. Just as the "tangent line", having slope [itex]f'(x_0)[/itex], to a curve in the plane gives the "best" linear approximation to y= f(x) close to [itex]x= x_0[/itex], the "tangent plane, [itex]z= f_x(x_0,y_0)(x- x_0)+ f_y(x_0,y_0)(y- y_0)+ z_0[/itex] gives the best linear approximation to z= f(x,y) close to [itex](x_0, y_0)[/itex]. In terms of the gradient, that can be written [itex]z= \nabla f(x_0,y_0)\cdot <x- x_0, y- y_0>+ z_0[/itex] where that multiplication is the dot product of vectors.

The formula your book gives is actually the equation of that tangent plane where the left side is "[itex]z- z_0[/itex]", [itex]\Delta x= x- x_0[/itex], [itex]\Delta y= y- y_0[/itex] and the last two terms are the "error" terms- the distance from the tangent plane to the actual surface.
 
  • #4
HallsofIvy said:
AAQIB IQBAL is talking about "the derivative" as if it were a single number and, for functions of several variables, that is not true.

Simon Bridge is talking about the partial derivatives- imagine walking along a hillside (representing the surface z= f(x,y)) moving due east (the positive x-axis). The rate at which you go up or down is the partial derivative with respect to x. Moving northward (the positive y-axis) the rate at which you go up or down is the partial derivative with respect to y.

The closest thing to "THE" derivative is the gradient, [itex]\nabla f[/itex], which is a vector pointing in the direction of fastest increase whose length is the rate of increase in that direction. Just as the "tangent line", having slope [itex]f'(x_0)[/itex], to a curve in the plane gives the "best" linear approximation to y= f(x) close to [itex]x= x_0[/itex], the "tangent plane, [itex]z= f_x(x_0,y_0)(x- x_0)+ f_y(x_0,y_0)(y- y_0)+ z_0[/itex] gives the best linear approximation to z= f(x,y) close to [itex](x_0, y_0)[/itex]. In terms of the gradient, that can be written [itex]z= \nabla f(x_0,y_0)\cdot <x- x_0, y- y_0>+ z_0[/itex] where that multiplication is the dot product of vectors.

The formula your book gives is actually the equation of that tangent plane where the left side is "[itex]z- z_0[/itex]", [itex]\Delta x= x- x_0[/itex], [itex]\Delta y= y- y_0[/itex] and the last two terms are the "error" terms- the distance from the tangent plane to the actual surface.
thank you, you have provided most satisfactory answer to my problem (especially "... imagine walking along ...").
Now i need to know one thing that can we assume the coefficient of z in the equation of tangent plane is non zero without loss of generality.
Actually while deriving the equation i need to divide throughout by coefficient of z, that is why i am asking this question.
 
  • #5
AAQIB IQBAL said:
thank you, you have provided most satisfactory answer to my problem (especially "... imagine walking along ...").
Now i need to know one thing that can we assume the coefficient of z in the equation of tangent plane is non zero without loss of generality.
Actually while deriving the equation i need to divide throughout by coefficient of z, that is why i am asking this question.
IF you are given the surface as "z= f(x,y)" that is necessarily true. The most general form for a surface is "F(x,y,z)= constant", as, for example, the surface of a sphere, given by [itex]x^2+ y^2+ z^2= r^2[/itex]. In that case, the gradient of F, [itex]\nabla F[/itex], is normal to the surface at any point and the tangent plane to F(x,y,z)= C at [itex](x_0, y_0, z_0)[/itex] is [itex]\nabla\cdot<x- x_0, y- y_0, z- z_0>= 0[/itex]. In the case that z= f(x, y), we can write F(x, y, z)= f(x,y)- z= 0 so that [itex]\nabla F= <f_x, f_y, -1>[/itex] and the tangent plane is given by [itex]f_x(x- x_0)+ f_y(z- z_0)- (z- f(x_0, y_0))= 0[/itex]. So the coefficient of z, in the case that z= f(x,y), is non-zero.
 

1. What is the definition of a derivative for functions of several variables?

The derivative for functions of several variables is a measure of how a function changes with respect to its inputs. It is defined as the limit of the average rate of change of the function as the change in inputs approaches zero. In other words, it represents the instantaneous rate of change of the function at a specific point.

2. How is the derivative of a function of several variables calculated?

The derivative of a function of several variables is calculated using partial derivatives. This involves taking the derivative of the function with respect to each input variable separately, while keeping the other variables constant. The resulting partial derivatives are then combined to form the gradient vector, which represents the direction and magnitude of the steepest increase of the function.

3. What is the significance of the derivative for functions of several variables in real-world applications?

The derivative for functions of several variables is essential in many fields of science and engineering. It is used to optimize processes, such as in economics and finance, by finding the maximum or minimum values of a function. It is also crucial in physics and engineering to understand the behavior of systems and make predictions based on changes in variables.

4. How does the derivative of a function of several variables relate to the graph of the function?

The derivative of a function of several variables can be visualized as a vector field, where the gradient vector at each point represents the direction and magnitude of the steepest increase of the function. This vector field can be used to determine the shape and characteristics of the graph of the function, such as the location of critical points and the direction of increase or decrease.

5. Can the concept of a derivative be extended to functions of more than two variables?

Yes, the concept of a derivative can be extended to functions of any number of variables. In fact, the concept of partial derivatives, which is used to calculate the derivative for functions of several variables, can be further extended to functions of infinite variables, as seen in the field of calculus of variations.

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