OpenMDAO Logo

An open-source MDAO framework written in Python.

OpenMDAO 2.2.1

OpenMDAO 2.2.1 came out today. Here are the release notes:

Release Notes for OpenMDAO 2.2.1

April 2, 2018

New Features:
————–
– check_partials() improvements to formatting and clarity. Report of potentially-bad derivatives summarized at bottom of output.
– check_partials() only compares fwd analytic to FD for any components that provide derivatives directly through the Jacobian
argument to compute_partials or linearize. (significantly less output to view now).
– Docs for UnstructuredMetaModel improved.
– pyoptsparse wrapper only calls run_model before optimization if LinearConstraints are included.
– ScipyOptimizerDriver is now smarter about how it handles linear constraints. It caches the derivatives and doesn’t recompute them anymore.
– Docs for ExternalCode improved to show how to handle derivatives.
– cache_linear_solution argument to add_design_var, add_constraint, add_objective, allows iterative linear solves to use previous solution as initial guess.
– New solver debugging tool via the `debug_print` option: writes out initial state values, so failed cases can be more easily replicated.
– Added generic KS component.
– Added generic Bspline component.
– Improved error msg when class is passed into add_subsystem.
– Automated Jacobian coloring algorithm now works across all variables (previously, it was just local within a variable).
– Major refactor of the `compute_totals` method to clean up and simplify.

Backwards-Compatible API Changes:
———————————–
N/A

Backwards-Incompatible API changes:
———————————–
N/A

Bug Fixes:
———–
– compute_totals works without any arguments now (just uses the default set of des_vars, objectives, and constraints).
– UnstructuredMetaModel can now be sub-classed.
– Deprecated ScipyOptimizer class wasn’t working correctly, but can now actually be used.

Comments are closed.

Fork me on GitHub