Re: [R] The smallest enclosing ball problem

Berend Hasselman bhh at xs4all.nl writes: Forgot to forward my answer to R-help. Berend Thanks, Berend, for thinking about it. \sum xi = 1 is a necessary condition to generate a valid geometric solution. The three points in the example are very regular and your apporach works, but imagine

Re: [R] The smallest enclosing ball problem

Berend Hasselman bhh at xs4all.nl writes: It seems you are absolutely right. I always assumed a quadratic programming solver will -- as all linear programming solvers do -- automatically require the variables to be positive. I checked it for some more examples in 10 and even 100 dimensions,

[R] The smallest enclosing ball problem

I wanted to solve the following geometric optimization problem, sometimes called the enclosing ball problem: Given a set P = {p_1, ..., p_n} of n points in R^d, find a point p_0 such that max ||p_i - p_0|| is minimized. A known algorithm to solve this as a Qudratic Programming task is

Re: [R] optimization: multiple assignment problem

Jean-Francois Chevalier Jean-Francois.Chevalier at bisnode.com writes: You have already given the answer yourself. You have binary variables x(j, i), you need to set up the inequalities, and then apply one of the mixed-integer linear programming solvers in R, for instance 'lpSolve', 'Rglpk',