Ok,

One of the possibilities is this alogrithm:

1. Construct list of all n-tuples in the root process as a vector of pair<int, ntuple>.( this feel a bit redudant as std::vector's indices is naturally what I want)

2. use reduce with the type T = pair< int, ntuple >, and have my function object evaluate f at 2 ntuples and return the pair that gives the better f(.) evaluation of the two.

Where do the f evaluations take place in this case? All at the root process,or distributed?

On 14 October 2013 10:39, Matthias Troyer <troyer@phys.ethz.ch> wrote:

Look at the documentation of MPI_MAX_LOC. The datatype of to be reduced needs to be a pair, consisting of value and index. You can do the same with your custom function.

On 14 Oct 2013, at 11:02, MM <finjulhich@gmail.com> wrote:

> That will give me the optimum value of 霡(x1, ..., xn) but not _which_ (x1,....xn) gives that optimum evaluation of f.
>
> MM
>
>
> On 14 October 2013 09:57, Matthias Troyer <troyer@phys.ethz.ch> wrote:
>
> On 14 Oct 2013, at 10:53, MM <finjulhich@gmail.com> wrote:
>
>> Hello
>> In a problem of getting the maximum of a multi-variable function, I would use mpi::reduce and provide it with a function object that compares the evaluations of this function.
>>
>> Each process evaluates a part of the space, ie a number of n-tuples of the n-dimension space.
>>
>> How do I get the n-tuple that yields the max of f(), given that MPI_MAXLOC is not available in boost mpi?
>>
>> Thanks
>>
>> MM
>
>
> Hi MM,
>
> you can just provide your own function object to the boost::mpi::reduce function
>
> Matthias
>
>
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