Multivariable Calculus (1st order approximation)

In summary: As h goes to 0, all the terms with δ, ε, and θ go to 0. Therefore, the numerator becomes 0, and the denominator becomes 1. So the limit is equal to 0/1 = 0. This shows that as h goes to 0, the error e(h) goes to 0 faster than h, which is what we expect from a first order approximation.
  • #1
plzen90
4
0

Homework Statement



Define f:R2→R3 by
f(x,y,z)=(xy+z)
...(x2-yz)

let p = (1,1,1)T and h=(δ,ε,θ)

a)what are n and m? evaluate f(p) and f(p+h)
b)Calculate the Jacobian Matrix Df(x,y,z) and evaluate Df(p)
c) Calculate the error e(h) in the first order approximation to f(p+h)
d) show clearly that

lim h→0 |e(h)| =0
......|h|


Explain why this is what you expect

Homework Equations





The Attempt at a Solution



a)
n=3
m=2

f(p)=(1,1,1) = (2)
......(0)

f(p+h) = f(1+δ, 1+ε, 1+θ)

=(2+δε+δ+ε+θ)
2+2δ-εθ-ε-θ)

b)
jac= (y x 1)
...(2x -z -y)

Df(p)=(1 1 1)
...(2 -1 -1)

c)
f(p+h)≈f(p)+Df(p)h

only calculation of Df(p)h needed to work out error.

=(y+x+1)h
(2x-z-y)

=(ε+δ+1)
(2δ-θ-ε)

e(h)=f(p+h)-(f(p) + Df(p)h)

=(δε+θ-ε-1)
2-εθ)

(not confident on this)

d)not attempted yet/ don't know how to
 
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  • #2
show it clearly.

The first part of your solution looks correct. The Jacobian matrix is a 3x2 matrix, and the derivative at point p is a 1x2 matrix.

For part c), you are correct in saying that the error is equal to f(p+h) - (f(p) + Df(p)h). However, the calculation of Df(p)h is incorrect. It should be:

Df(p)h = (y+x+1)δ + (2x-z-y)ε
(y+x+1)ε + (2x-z-y)θ

Substituting in the values for p and h, we get:

Df(p)h = (2+1)δ + (2-1-1)ε
(2+1)ε + (2-1-1)θ

= 3δ + 0ε
3ε + 0θ

= (3δ, 3ε)

Therefore, the error e(h) becomes:

e(h) = (δε+θ-ε)
(δ2-εθ)

- (2+0+0)
(0+0-0)

= (δε+θ-ε-2)
(δ2-εθ)

For part d), you can show that the limit as h goes to 0 of e(h)/|h| is equal to 0 by simplifying the expression:

lim h→0 |e(h)|/|h| = lim h→0 ((δε+θ-ε-2)^2 + (δ2-εθ)^2)/(|δ|+|ε|+|θ|)

As h goes to 0, all the terms with δ, ε, and θ go to 0 as well. Therefore, the numerator becomes 0, and the denominator becomes 0+0+0 = 0. This means that the limit is equal to 0/0, which is an indeterminate form. To evaluate this limit, we can use L'Hopital's rule, which states that if we have a limit of the form 0/0, we can take the derivative of the numerator and denominator separately and evaluate the limit again. Applying this rule, we get:

lim h→0 |e(h)|/|h| = lim h→0 (2
 

Related to Multivariable Calculus (1st order approximation)

What is multivariable calculus?

Multivariable calculus is a branch of mathematics that deals with functions of multiple variables, usually in three-dimensional space. It involves the study of limits, derivatives, and integrals of functions with more than one independent variable.

What is first order approximation in multivariable calculus?

First order approximation, also known as linear approximation, is a method used to approximate a function with a simpler, linear function. It involves finding the tangent plane to the function at a specific point and using that to estimate the function's value near that point.

Why is first order approximation useful in multivariable calculus?

First order approximation is useful because it allows us to approximate a complex function with a simpler one, making it easier to solve and analyze. It is also used in optimization problems to find the minimum or maximum value of a function.

What is the formula for first order approximation?

The formula for first order approximation is f(x,y) ≈ f(x0,y0) + (fx(x0,y0)(x-x0)) + (fy(x0,y0)(y-y0)), where (x0,y0) is the point at which the approximation is being made, fx and fy are the partial derivatives of the function at that point with respect to x and y, and (x,y) are the coordinates of the point being approximated.

What are some real-world applications of first order approximation in multivariable calculus?

First order approximation has many practical applications, including in physics, engineering, economics, and computer graphics. It is used to approximate the trajectory of a projectile, optimize the design of a bridge, model supply and demand in a market, and create computer-generated imagery in movies and video games.

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