Question

I would like to use a MATLAB function to find the minimum length between a point and a curve? The curve is described by a complicated function that is not quite smooth. So I hope to use an existing tool of matlab to compute this. Do you happen to know one?

Answer 1

When someone says “its complicated” the answer is always complicated too, since I never know exactly what you have. So I’ll describe some basic ideas.

If the curve is a known nonlinear function, then use the symbolic toolbox to start with. For example, consider the function y=x^3-3*x+5, and the point (x0,y0) =(4,3) in the x,y plane.

Write down the square of the distance. Euclidean distance is easy to write.

(x - x0)^2 + (y - y0)^2 = (x - 4)^2 + (x^3 - 3*x + 5 - 3)^2

So, in MATLAB, I’ll do this partly with the symbolic toolbox. The minimal distance must lie at a root of the first derivative.

sym x
distpoly = (x - 4)^2 + (x^3 - 3*x + 5 - 3)^2;
r = roots(diff(distpoly))
r =
      -1.9126              
      -1.2035              
       1.4629              
      0.82664 +    0.55369i
      0.82664 -    0.55369i

I’m not interested in the complex roots.

r(imag(r) ~= 0) = []
r =
      -1.9126
      -1.2035
       1.4629

Which one is a minimzer of the distance squared?

subs(P,r(1))
ans =
    35.5086

subs(P,r(2))
ans =
    42.0327

subs(P,r(3))
ans =
    6.9875

That is the square of the distance, here minimized by the last root in the list. Given that minimal location for x, of course we can find y by substitution into the expression for y(x)=x^3-3*x+5.

subs('x^3-3*x+5',r(3))
ans =
       3.7419

So it is fairly easy if the curve can be written in a simple functional form as above. For a curve that is known only from a set of points in the plane, you can use my distance2curve utility. It can find the point on a space curve spline interpolant in n-dimensions that is closest to a given point.

For other curves, say an ellipse, the solution is perhaps most easily solved by converting to polar coordinates, where the ellipse is easily written in parametric form as a function of polar angle. Once that is done, write the distance as I did before, and then solve for a root of the derivative.

A difficult case to solve is where the function is described as not quite smooth. Is this noise or is it a non-differentiable curve? For example, a cubic spline is “not quite smooth” at some level. A piecewise linear function is even less smooth at the breaks. If you actually just have a set of data points that have a bit of noise in them, you must decide whether to smooth out the noise or not. Do you wish to essentially find the closest point on a smoothed approximation, or are you looking for the closest point on an interpolated curve?

For a list of data points, if your goal is to not do any smoothing, then a good choice is again my distance2curve utility, using linear interpolation. If you wanted to do the computation yourself, if you have enough data points then you could find a good approximation by simply choosing the closest data point itself, but that may be a poor approximation if your data is not very closely spaced.

If your problem does not lie in one of these classes, you can still often solve it using a variety of methods, but I’d need to know more specifics about the problem to be of more help.