CONOPT: Convergence problem

Occasionally the NLP solver CONOPT will terminate with:

Iter Phase Ninf     Objective     RGmax    NSB   Step InItr MX OK    823   3        1.6227533982E+01 3.0E-02   407 0.0E+00      F  F    824   3        1.6227533982E+01 3.0E-02   401 0.0E+00      F  F    825   3        1.6227533982E+01 3.0E-02   405 0.0E+00      F  F    826   3        1.6227533982E+01 3.0E-02   407 0.0E+00      F  F    827   3        1.6227533982E+01 3.0E-02   407 ** Feasible solution. Convergence too slow. The change in objective    has been less than 4.8683E-11 for 20 consecutive iterations

In some cases we can reach a (local) optimum just by solving again:

solve ramsey maximizing AggW using nlp; solve ramsey maximizing AggW using nlp;

Indeed this trick actually works in this case and the second model solves quickly and terminates with:

Iter Phase Ninf     Objective     RGmax    NSB   Step InItr MX OK    193   4        1.6948156578E+01 2.7E+00   342 2.0E+00    1 F  T    194   4        1.6948221678E+01 1.4E-03   441 1.0E+00    1 F  T    195   4        1.6948492083E+01 1.9E-03   441 1.0E+00    6 T  T    196   4        1.6949366978E+01 1.3E-03   440 1.0E+00   24 F  T    197   4        1.6950307538E+01 3.1E-03   440 1.0E+00  113 F  T    198   4        1.6950345143E+01 1.4E-04   440 1.0E+00  171 F  T    199   4        1.6950345450E+01 1.6E-05   440 1.0E+00  136 F  T    200   4        1.6950345453E+01 1.6E-06   440 1.0E+00  101 F  T    201   4        1.6950345453E+01 1.0E-07   440 1.0E+00    7 F  T    202   4        1.6950345453E+01 5.6E-08   440 ** Optimal solution. Reduced gradient less than tolerance.