Multi-dimensional root finding

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In summary, the conversation discusses finding the values of three non-linear functions in three dimensions using measured values of the functions. One suggested method is to use matrices to solve for the intersection of the surfaces created by the functions. Another suggestion is to assume a specific form for the function and use a transformation to simplify the equations. The use of Broyden's method and Cramer's rule are also mentioned.
  • #1
natski
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Hi all,

Consider that one has several functions, say 3, of the form f(x,y,z) and g(x,y,z) and h(x,y,z). You know the form of these equations and they are non-linear, long, messy equations. f', g' and h' are even longer and messier and therefore assume that they cannot be found.

Now consider you measure f, g and h and want to determine x, y and z.

What method is recommended for solving this in a numerical and computationally efficient (but simple to implement) manner?

Cheers,

Natski
 
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  • #2
Well, its if 3 dimensions (x,y,z), and you can find the values of the functions, what you'll essentially be left with are equations of surfaces. Any solution you obtain will be the intersection of those surfaces. If you know the functions, I'm pretty sure you could use matrices to solve for them.

For example, you have three functions of the form

[tex]f(x,y,z)=a_{0}+a_{1}x+...+a_{n}x^n+b_0+b_1y+...+b_ny^n +c_0+c_1z+...+c_nz^n[/tex]

along with all the xy, yz, zx terms of degree n, then you could create a matrix equation such that AX=B where A holds the coefficients, X holds the values x, x^2, etc and B holds the values of the 3 functions.

then X=A-1B will give you a solution.
 
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  • #3
Cramer's rule only applies for linear equations.
A matrix method would not be involve the Jacobian which cannot be computed in this case.

I think the solution lies within Broyden's method.
 
  • #4
Thats pretty messy though, perhaps another way to do so would be to assume that the function you have is of the form,

[tex]f(x,y,z)=(ax+by)^n + (cy+dz)^n +(ez+fx)^n[/tex]

Since you know f, you could find out the values of the constants. Perhaps to simplify the above equation, you could apply the transformation

[tex]X=\frac{a}{\sqrt{a^2+b^2}}x+\frac{b}{\sqrt{a^2+b^2}}y[/tex]

[tex]Y=\frac{c}{\sqrt{c^2+d^2}}y+\frac{d}{\sqrt{a^2+d^2}}z[/tex]

[tex]Z=\frac{e}{\sqrt{e^2+f^2}}z+\frac{f}{\sqrt{e^2+f^2}}x[/tex]

That would transform the above equation into

[tex]f(X,Y,Z)=k_1X^n +k_2Y^n+k_3Z^n[/tex]

where

[tex]k_1=(a^2+b^2)^{\frac{n}{2}}[/tex]

[tex]k_2=(c^2+d^2)^{\frac{n}{2}}[/tex]

[tex]k_3=(e^2+f^2)^{\frac{n}{2}}[/tex]
 
  • #5
I don't know much about Broyden's method, but from what wikipedia says, wouldn't you have to calculate the Jacobian there too?

If you use the transformation, I think you could use matrices to solve the equations and you can find the jacobian of the transformation. Then you could use cramer's rule to solve for Xn, Yn, Zn.
 

What is multi-dimensional root finding?

Multi-dimensional root finding is a mathematical problem where the goal is to find a set of input values that will make a multi-variable function evaluate to zero. This is commonly used in scientific and engineering applications to solve complex systems of equations.

How is multi-dimensional root finding different from one-dimensional root finding?

One-dimensional root finding involves finding the root of a single-variable function, while multi-dimensional root finding involves finding the roots of a multi-variable function. This requires a different set of algorithms and techniques.

What are some common algorithms used for multi-dimensional root finding?

Some common algorithms used for multi-dimensional root finding include Newton's method, Broyden's method, and the Levenberg-Marquardt algorithm. These methods use different approaches to iteratively solve for the roots of a multi-variable function.

What are some challenges in multi-dimensional root finding?

One of the main challenges in multi-dimensional root finding is the potential for multiple roots or no roots at all. This can make it difficult to determine the convergence of the solution. In addition, the presence of local minima or maxima can also complicate the root finding process.

In what fields is multi-dimensional root finding commonly used?

Multi-dimensional root finding has many applications in fields such as physics, engineering, economics, and computer graphics. It is commonly used to solve systems of equations in these fields and can be applied to a wide range of problems.

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