Berend Hasselman 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, and t
On 17-11-2013, at 11:32, Hans W.Borchers wrote:
> Berend Hasselman 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 exam
Berend Hasselman 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 som
On 16-11-2013, at 13:11, Hans W.Borchers wrote:
> 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
Forgot to forward my answer to R-help.
Berend
On 16-11-2013, at 13:11, Hans W.Borchers wrote:
> 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
>
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
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