I occasionally don't get convergence on my problem. My problem is setup as a Dymos problem. I am using IPOPT as my optimizer. If I am only running the problem once, I can check IPOPT.out for the converged string and that's ok.
I often want to run parameter sweeps, where I vary boundary conditions and problem options. I use Ray https://www.ray.io/, a python library for running parallel processes to do these. I turn off all file I/O that I can for this as otherwise the multiple processes interfere with each other writing to file.
However, it's then difficult to know if a particular process / case did not converge. For this reason actually having run_problem() return information on convergence would be useful. It doesn't seem to do that, so is there a way to get convergence info some other way, that does not involve reading a file?
I do realize there is the whole DOE driver system that is setup for OpenMDAO. However the learning curve looked rather steep. I got parallel processing working with Ray in a matter of hours, and it works quite well except for this one issue.
prob.driver.fail should be False if the the optimization was successful, and doesn't need to be read from a file. However, given the various levels of success in optimizers this might not be completely accurate. For instance, solved to acceptable tolerance vs. optimal solution found is a little difficult to capture in a simple boolean output, and we should probably find a better way to report the optimizer's success.