JConvex: systematically deal with rational and big coefficients

[fingolfin]

When I refactored JConvex, I replaced code like this:

        s := JuliaToGAP( IsString, Julia.string( cone!.pmobj.RAYS ) );
        res_string := SplitString( s, '\n' );
        res_string := List( [ 2 .. Length( res_string ) ], i -> Concatenation( "[", ReplacedString( res_string[ i ], " ", "," ), "]" ) );
        rays := EvalString( Concatenation( "[", JoinStringsWithSeparator( res_string, "," ), "]" ) );

by stuff like this:

        rays := JuliaToGAP( IsList, JuliaMatrixInt( cone!.pmobj.RAYS ) );

where JuliaMatrixInt really is the Julia type Matrix{Int}.

I think that was a mistake. We probably should be using Matrix{Rational{BigInt}}. Because JConvex then often proceeds with code like this:

        # sometimes, Polymake returns rational rays - we turn them into integral vectors
        scaled_rays := [];
        for i in [ 1 .. Length( rays ) ] do
            scale := Lcm( List( rays[ i ], r -> DenominatorRat( r ) ) );
            Append( scaled_rays, [ scale * rays[ i ] ] );
        od;

From @lkastner I learned that one can let Polymake do this scaling, by calling Polymake.common.primitive. So perhaps even better would be to replace all that scaling code by something like that (note the use of a to-be-defined JuliaMatrixBigInt):

        rays := _Polymake_jl.common.primitive( cone!.pmobj.RAYS ); # ensure integer coeffs
        rays := JuliaToGAP( IsList, JuliaMatrixBigInt( rays ) );

and then `scaled_rays is superfluous.