Solving Implicit Functions: F1(x), F2(x) near 0

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SUMMARY

The discussion focuses on solving implicit functions defined by the equations F1(x) and F2(x) near the point (0,0,0). The user successfully determined that the functions are implicitly defined by computing the determinant of the matrix B(x), which yielded a non-zero determinant, confirming the existence of unique functions phi1 and phi2. For the partial derivatives, the user is uncertain whether to evaluate the derivative formula at x2 = 0 or leave it in terms of phi1(x2) and phi2(x2). The conclusion drawn from reviewing Rudin's treatment indicates that the derivative formula should be evaluated at the specific point.

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  • Understanding of implicit function theorem
  • Familiarity with matrix determinants
  • Knowledge of partial derivatives
  • Basic concepts of multivariable calculus
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  • Learn how to compute determinants of matrices in multivariable calculus
  • Explore the application of partial derivatives in implicit differentiation
  • Review the relevant sections in "Principles of Mathematical Analysis" by Walter Rudin
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asif zaidi
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Hello:
I thought I had this but in doing the problem I realized I didn't (or maybe I didn't).
One of the problems was that in class and notes all examples were done in terms of f(x,y). Obviously in h/w, the problem is given as f(x1,x2,x3) - just to confuse me !

Problem statement

a- For the following equation, decide whether they implicitly determine functions near 0
b- If the equation implicitly determine a function, compute the partial derivatives at 0

Equation:

F1(x) = ( x1[tex]^{2}[/tex] + x2[tex]^{2}[/tex] + x3[tex]^{2}[/tex] )[tex]^{3}[/tex] - x1 + x3 = 0;
F2(x) = cos (x1[tex]^{2}[/tex] + x2[tex]^{4}[/tex]) + exp(x3) - 2 = 0

Solution

For Part a-

I know I have to compute the determinant of a matrix. So for this example I played out with x1,x2,x3 and saw which determinant would not be 0. I came up with the following matrix.

B(x) = [partial_der_F1 (x1) partial_der_F1 (x3) ; partial_der_F2(x1) partial_der_F2(x3) ]. Evaluating this at 0, I get the following matrix

[-1 1; 0 1] and the determinant of this matrix is -1 != 0. So I can take the inverse of this matrix

Thus I can say that for each x2, the following function F(phi1(x2), x2, phi2(x2) ) = 0 where phi1 and phi2 are unique functions.

I think I have part a right. It is part b I am having problems with

For part b:

The formula for the derivative given in class notes is -B(x,g(x))[tex]^{-1}[/tex] A(x,g(x))

B = matrix calculated above
A = partial derivative of F1, F2 (2x1 matrix) with respect to x2.

Now my question is do I have to evaluate this at x2 = 0 or can I just leave it in terms of phi1(x2), x2, phi2(x2).

Whats confusing me is that if I compute A wrt x2 and evaluate at x2=0, I will get a 0 matrix.


Thanks in advance



Asif
 
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I looked over the baby Rudin treatment of this theorem (pg. 224-228) and my conclusion is that the formula given for a derivative in your class notes holds when evaluated at the point. Thus [tex]\left(\phi_1^\prime (0),\phi_2^\prime (0)\right)=(0,0)[/tex] is my conclusion if your calculations are correct.
 

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