Newton's Method generalized to 3 dimensions

In summary, Newton's Method can be adapted to approximate solutions of a system of equations by looking for the intersection of two tangent planes to a surface at a point (r,s). If the initial approximation (x1,y1) is close to the point (r,s), then the tangent planes to the surface at (x1,y1) intersect in a straight line that should intersect the xy-plane closer to (r,s).
  • #1
kostoglotov
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6

Homework Statement



Prelim: my question is about a very specific part of a question whereby the student is asked to derive the final formula for the general solution in two vars, but I will post the entire question for clarify.

Newton's Method for approximating the roots of an equation f(x)=0 can be adapted to approximating solutions of a system of equations f(x,y)=0 and g(x,y) = 0. The surfaces z = f(x,y) and z = g(x,y) intersect in a curve that intersects the xy-plane at the point (r,s) which is the solution of the system.

If an initial approximation (x1,y1) is close to the point (r,s), then the tangent planes to the surface at (x1,y1) intersect in a straight line that should intersect the xy-plane closer to (r,s).

Show that

[tex]x_2 = x_1 - \frac{fg_y-f_yg}{f_xg_y-f_yg_x}[/tex]

and

[tex]y_2 = y_1 - \frac{f_xg-fg_x}{f_xg_y-f_y-g_x}[/tex]

Homework Equations



[itex]\vec{v} = \vec{n_f} \times \vec{n_g}[/itex]: direction vector of line of intersection of the two planes, basically the cross product of the normal vectors to the two planes

[itex]x_n = x_{n-1} - \frac{f(x_{n-1})}{f'(x_{n-1})}[/itex]: single var form of Newton's Method to be adapted.

The Attempt at a Solution



So I consider this line of intersection of two planes from two perspectives. One looking at it "down" the y-axis to consider just the relationship between z and x, and then by looking "down" the x-axis to consider just the relationship between z and y.

I can see that [itex]\frac{\partial z}{\partial x} = \frac{f_xg_y-f_yg_x}{g_y-f_y}[/itex] and [itex]\frac{\partial z}{\partial y} = \frac{f_xg_y-f_yg_x}{f_x-g_x}[/itex] by taking the z component of [itex]\vec{v}[/itex] (the direction vector of the line formed from the cross product) and dividing it by the x and y components of [itex]\vec{v}[/itex] respectively.

I get to here with it:

[tex]x_2 = x_1 - z_1 \frac{g_y-f_y}{f_xg_y-f_yg_x}[/tex]

Now, I can see that since all the points we are working with here are points of intersection, that the x,y and z's are all common for all the surfaces, planes and lines concerned. So z1 = f(x1,y1) = g(x1,y1)...so I can accept that we could pick either f or g to be z in the equation...but the text specifically states

[tex]x_2 = x_1 - \frac{fg_y-f_yg}{f_xg_y-f_yg_x}[/tex]

The text shows the z = f when multiplied by the gy and z = g when multiplied by the fy.

Why is this?

A follow up question to this one is to find the points of intersection of [itex]x^x+y^y=1000[/itex] and [itex]x^y + y^x = 100[/itex] so I can see how it might be important to choose z = f for multiplication with gy and z = g for multiplication with fy, but I can't spy the reasoning behind it.
 
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  • #2
I don't quite get the logic of your solution - but going back to the "relevant equations" part, if you calculate the vector ## \vec v ## then you are searching for the intersection of the line ## \{(x,y,z)+t\vec v,t\in\mathbb{R}\} ## with the plane z=0. Would that work ?
 
  • #3
wabbit said:
I don't quite get the logic of your solution - but going back to the "relevant equations" part, if you calculate the vector ## \vec v ## then you are searching for the intersection of the line ## \{(x,y,z)+t\vec v,t\in\mathbb{R}\} ## with the plane z=0. Would that work ?

Actually, I realized that I was just confusing myself. Provided f(x,y) = 0 and g(x,y) = 0, z = f = g when evaluated at the same x,y, so it doesn't matter which partial deriv you pair the z,f, or g with, they'll always be the same value anyway, so I think the text was just pairing f with gy and g with fy for aesthetic purposes, because regardless of which you choose that second problem I mentioned [itex]x^x+y^y=1000[/itex] and [itex]x^y + y^x = 100[/itex] will have the same solutions, which look just fine when you graph them.
 
  • #4
Well there is only one Newton formula you can arrive at, no aestethic choice involved - so I am not sure what you mean here.

Also my question in the previous post was rhetorical. It will work.
 
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1. How does Newton's Method work in 3 dimensions?

Newton's Method in 3 dimensions is an iterative numerical method used to find the roots of a multivariate function. It involves starting with an initial guess and using the function's gradient and Hessian matrix to update the guess until a root is found.

2. What is the difference between Newton's Method in 2 dimensions and 3 dimensions?

The main difference between Newton's Method in 2 dimensions and 3 dimensions is the use of the Hessian matrix. In 2 dimensions, the Hessian matrix is a 2x2 matrix, while in 3 dimensions it is a 3x3 matrix. This allows for a more accurate update of the guess in 3 dimensions.

3. Can Newton's Method be used for any multivariate function?

Yes, Newton's Method can be used for any multivariate function as long as the function is continuous and has a non-zero gradient and Hessian matrix at the root being sought. However, it may not always converge to a root or may converge to a local minimum instead of the global minimum.

4. What are the advantages of using Newton's Method in 3 dimensions?

One advantage of using Newton's Method in 3 dimensions is its fast convergence rate, meaning it can find a root in fewer iterations compared to other methods. Additionally, it can handle complex functions with multiple roots and can find local as well as global minima.

5. Are there any limitations to using Newton's Method in 3 dimensions?

One limitation of Newton's Method in 3 dimensions is that it requires the computation of the Hessian matrix, which can be time-consuming and computationally expensive for large functions. Additionally, it may fail to converge or converge to the wrong root if the initial guess is far from the actual root or if the function has multiple roots in close proximity.

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