How to pick a random direction in n-dimensional space I want to pick a random direction in n-dimensional space.  How can I do this?
The reason I want to do this is to pick a neighbor for hill climbing optimization.
 A: The easiest way to do this efficiently is to rely on the fact that a gaussian distribution is spherically symmetric and also separable. So, what you need to do is :
1) Build a vector V where each element is a Gaussian distributed value of mean 0, choose any width that makes sense. 
2) Normalize the vector V
This vector now is a random unit vector uniformly distributed across the hypersphere of the vector V. This algorithm is both fast and is linear in the dimension of V.
A: A simple method is to pick $n$ random numbers $x_1, \ldots, x_n$ from the interval [-1,1].  If $\sum x_i^2 > 1$ throw those numbers out and try again.  Otherwise, rescale so that $\sum x_i^2 = 1$.
For large $n$ this is inefficient with computer time (because $\sum x_i^2 > 1$ most of the time), but it might be more efficient with your time (because it's easy to program).
A: You can proceed as explained at http://mathworld.wolfram.com/HyperspherePointPicking.html
A: The GNU Scientific Library (GSL) has an implementation for this.  See http://www.gnu.org/software/gsl/manual/html_node/Spherical-Vector-Distributions.html in the manual.  It even refers to where Knuth describes the algorithm.
