You can see there are 3 versions of the same Objective Function (With % for the first 2).
In this example of numbers the first 2 fails to supply an answer whole the third succeeds.
In other numeric examples they all converge.
I recommend you not use the quiet option when trying to assess and diagnose solution difficulties. Then you’ll be able to see the solver output.
I solved all 3 variants with both sdpt3 and sedumi, achieving cvx_optval = 38. Things were looking a little precarious with the solvers when using cvx_precision(‘best’), but looked fine with default precision.
I did copy and paste. Then did edits, such as not doing quiet, changing which objective was uncommented, and changing precision and cvx_solver. My results are as I described above.
I meant with cvx_precision(‘best’) do you see the same issues as I did?
If you did, since it doesn’t happen in default precision, it is not something of CVX translation but solvers issue?
I wonder if it happens on the commercial solvers as well.
I’m afraid it’s not likely unless someone supplies a patch. The fact that it works without cvx_precision('best') certainly reduces any priority I might place on it.