May 28, 2019
Comments Off on OpenMDAO 2.7.0 Released
OpenMDAO 2.7.0 has been released.
Here are the release notes listing the new features, bug fixes and a couple of backwards-incompatible changes:
- You can now define guess_nonlinear method at the group level
- New documentation added about the N2 diagram usage
- Significant improvement to documentation search functionality (by default, only searches the feature docs and user guide now)
- Improvements to support for complex-step across a full model (specifically when guess_nonlinear is implemented)
- Improved support for full-model complex-step when models have guess_nonlinear methods defined
- **Experimental** FD and CS based coloring methods for partial derivative approximation. Valuable for efficiently using FD/CS on vectorized (or very sparse) components
- `Solver failed to converge` message now includes solver path name to make it more clear what failed
- Improved pathname information in the singular matrix error from DirectSolver
- Directsolver has an improved error message when it detects identical rows or columns in Jacobian
- NonlinearGaussSeidel solver now accounts for residual scaling in its convergence criterion
- New naming scheme for solver debug print files (the old scheme was making names so long it caused OSErrors)
- ExecComp now allows unit=<something> and shape=<something> arguments that apply to all variables in the expression
- New AkimaSpline component with derivatives with respect to training data inputs
- Several improvements for the N2 diagram for large models
- N2 diagram html files have been reduced in size significantly
- `openmdao view_model` command line utility now supports case record database files
- (Experimental) Automatic XDSM generator (using either Latex with pyXDSM or html with XDSMjs)
- contributed by Peter Onodi
- uses XDSMjs v0.6.0 by Rémi Lafage (https://github.com/OneraHub/XDSMjs)
Backwards Incompatible API Changes:
- New APIs for total derivative coloring that are more consistent with partial derivative coloring (previous APIs are deprecated and coloring files generated with the previous API will not work)
- The API for providing a guess function to the BalanceComp has changed. guess_function is now passed into BalanceComp as an init argument.
- Changed the N2 diagram json data formatting to make the file size smaller. You can’t use older case record databases to generate an N2 diagram with latest version.
- The problem level `record_iteration` method was not properly respecting the
- All component methods related to execution now include `discrete_inputs` and `discrete_outputs` arguments when a component is defined with discrete i/o. If no discrete i/o is defined, the API remains unchanged. (includes `solve_nonlinear`, `apply_nonlinear`, `linearize`, `apply_linear`, `compute`,
- The internal Driver API has changed, a driver should execute the model with `run_solve_nonlinear` to ensure that proper scaling operations occur .
- CaseRecorder was reporting incorrect values of scaled variables (analysis was correct, only case record output was wrong)
- ExecComp problem when vectorize=True, but only shape was defined.
- Incorrect memory allocation in parallel components when local size of output went to 0
- Multidimensional `src_indices` were not working correctly with assembled Jacobians
- Fixed problem with genetic algorithm not working with vector design variables
- contributed by jennirinker
- Fixed incompatibility with mpich mpi library causing “PMPI_Allgather(945).: Buffers must not be aliased” error
- contributed by fzhale and nbons
February 22, 2019
Comments Off on OpenMDAO 2.6.0 Released!
OpenMDAO 2.6.0 is out!
Here are the release notes listing the new features, bug fixes and a couple of backwards-incompatible changes:
MetaModelStructured will detect NaN in requested sample and print a readable message.
- ScipyOptimizeDriver now supports Hessian calculation option for optimizers that use it.
- User can specify src_indices that have duplicates or have two inputs on a single component connected to the same output, and still have CSC Jacobian work.
- User can get/set problem values in a straightforward manner: prob['var'] = 2., etc. even when running in parallel.
- Problem 'get' access to distributed variables will raise an exception since it isn't clear what behavior is expected, i.e. should prob['comp.x'] return the full distributed variable or just the local part.
- Directsolver has an improved error message when it detects identical rows or columns in Jacobian.
- The NonlinearBlockGS solver has been updated with a less expensive implementation that does not call the compute method of Explicit components as many times.
- User can request a directional-derivative check (similar to SNOPTs built-in level 0 check) for check_partials.
- check_partials with compact_print will now always show all check pairs.
- The N^2 diagram (from `openmdao view_model`) now shows the solver hierarchy.
- KSComp has an improved, vectorized implementation.
- list_inputs now includes a 'shape' argument.
- User can generate an XDSM from a model (`openmdao xdsm`).
- User can set `units=<something>` for an execcomp, when all variables have the same units.
Backwards Incompatible API Changes:
- The default bounds enforcement for BoundsEnforceLS is now 'scalar' (was 'vector')
- Direct solver will now use 'assemble_jac=True' by default
- Recording options 'includes' and 'excludes' now use promoted names for outputs (absolute path names are still used for inputs)
- FloatKrigingSurrogate has been deprecated since it does not provide any unique functionality.
- FloatMultifiKrigingSurrogate has been deleted because it was never used, was incorrectly implemented, and provides no new functionality.
- Complex-step around Newton+DirectSolver now works with assembled Jacobians.
- Armijolinesearch implementation was incorrect when used with solve_subsystems. The implementation is now correct.
February 14, 2019
Comments Off on A fond farewell to Keith Marsteller
Many users of the OpenMDAO framework have interacted with Keith Marsteller in one way or another over the last 11 years. Keith has decided to move on to other projects and interests and, though the development team is sad to see him go, we wish him all the best in his new endeavors.
If you’ve used OpenMDAO at all, you’ve very likely felt Keith’s influence even if you didn’t realize it. He has managed our development and release process, led the construction of our entire CI system, played a key role in defining the structure our docs, built our web-site, and generally pitched in to keep the overall development process running smoothly all the time. For users he often helped solve installation issues and provided guidance on how to build plugins.
Those who know Keith, even a little bit, know of his fondness of running up hills — the bigger the better, but he’ll do repeats if he has to! Gradient based optimization is sometimes referred to as “climbing the hill”, and so in honor of Keith’s time on the team we are pleased to announce that Keith has been awarded the first ever, OpenMDAO Hill Climb Award. This award is given to those who display stubborn determination in the face of huge hills (literal or metaphorical) that block their path, and climb to the top no matter what obstacles lie in their way. Keith earned this award by sticking with the project through over 10 years of development, three re-writes, 17000 different CI systems, 28 docs re-factors, an unexpected white-board marker battle, and far too many really bad inside jokes.
Keith, we’ll miss you, but we look forward to hearing about your new hill climb adventures in the future.
February 1, 2019
Comments Off on OpenMDAO Papers for Scitech 2019
There are several new papers, published for Scitech 2019, that used OpenMDAO as a tool to reach their conclusions, that we wanted to share. Here are seven OpenMDAO-related works that you may find interesting:
“Battery performance modeling on sceptor x-57 subject to thermal and transient considerations,” by authors J. Chin, S. L. Schnulo, T. Miller, K. Prokopius, and J. S. Gray
“Gradient-based propeller optimization with acoustic constraints,” by authors D. J. Ingraham, J. S. Gray, and L. V. Lopes.
“Multi-point design and optimization of a turboshaft engine for tiltwing turboelectric vtol air taxi,” by author J. W. Chapman.
“Optimal control within the context of multidisciplinary design, analysis, and optimization,” by authors R. D. Falck and J. S. Gray.
“Load flow analysis with analytic derivatives for electric aircraft design optimization,” by authors E. S. Hendricks, J. W. Chapman, and E. Artskin-Hariton.
“Multipoint variable cycle engine design using gradient-based optimization,” by authorsJ. P. Jasa, J. S. Gray, J. A. Seidel, C. A. Mader, and J. R. R. A. Martins.
“A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy – Applied to a Wing Topology Optimization Problem,” by authors S. Roy, Willam A. Crossley, B. K. Stanford, K. T. Moore, and J. S. Gray.
If you liked these, and want to see all publications related to OpenMDAO, check out our publications page.
January 22, 2019
Comments Off on The OpenMDAO Paper!
A lot of blood, sweat, and tears has gone into OpenMDAO over the years. The project planning started in 2007, coding started in 2008, and our first release came in 2010. We’ve been through three major versions of the code:
- V0 (2010-2013): our first attempt! Serial, with a really awkward API. This was the playground where we really found the core ideas that would power OpenMDAO!
- V1 (2013-2017): OpenMDAO, now with parallel computing and a decent API! This was the first version of OpenMDAO to work in parallel, but we sacrificed serial performance to get there.
- V2 (2017-[a long time into the future]): Awesome performance for both serial AND parallel models, plus an even better API.
With the release of OpenMDAO V2, we finally achieved flexible framework that was efficient for serial and parallel problems. So we finally wrote an overview paper for the framework, and its recently been accepted for publication in the Journal of Structural and Multidisciplinary Optimization. You can find the pre-print here.
There is a bit of something for everyone in this paper. There is a high-level overview of the framework, including how implicit and explicit components leverage the underlying core APIs to support multidisciplinary modeling. There is a walk-through example of how some of the key underlying mathematics of the framework are used and how analytic derivatives are computed. Lastly, there are examples of how and when to use some of the specialized algorithms for computing derivatives efficiently for different kinds of problems.
We hope the paper helps you understand the framework better, and most importantly, helps you to solve some really nice MDO problems!
November 8, 2018
Comments Off on Research Debt
There is a new online journal called distill.pub that has an interesting new format. It places a particular emphasis on clear explanations of concepts and high-quality infographics that help to further understanding. This is so obviously a good idea, that it may seem somewhat unclear why a journal would need to explicitly include it as a metric for judging publications at all. The reality is that a lot of really great papers are not written with accessibility as a primary goal of the work. Researchers often focus on developing new theory, new applications, or new implementations to advance the state-of-the-art. There often isn’t time to develop clear and concise explanations that will help specialists and non-specialists alike understand the work, but the lack there of does limit the impact the work on the broader field. Still confused? Here, we made this super-fancy animation to help you understand it better!
The term “research debt” has been coined to describe this phenomenon, where some members of a particular community have made large advances but the broader group has yet to catch up. distil.pub has a great, in-depth discussion of research debt. They talk about different ways it can occur, what its fundamental costs really amount to, and when you should consider investing the time and effort to reduce it. Distill.pub is looking to publish papers that help to reduce research debt! As our helps to demonstrate, good explanation helps others in the field to better understand and build off your work and thus increases its impact.
When reading about the idea of research debt, it became clear that OpenMDAO could be viewed as project that reduces the research debt in the gradient-based optimization community. The most advanced gradient-based optimization techniques, with analytic derivatives, offer the potential for 10-to-10,000-times faster optimizations. Unfortunately these methods are usually implemented in an ad-hoc and problem-specific manner that makes it tough to generalize to a wide range of applications. What OpenMDAO does is implement these methods in a way that makes it possible to use analytic derivatives without being an expert in them, and without having to use a custom implementation for each problem.
Since OpenMDAO version 2.3, we’ve been continuously introducing new features that offer dramatic performance improvements. If you haven’t tried out assembled Jacobians (added in v2.3), you are missing out! There is also a new graph-coloring approach that works miracles for some problems (added in v2.4). But along the way we’ve also been trying to limit our own research debt by keeping the docs up-to-date and working to improve their clarity and usefulness. Writing more docs keeps the new methods more accessible to OpenMDAO users. More importantly, it also forces us to think about ways to explain the concepts more clearly and concisely which often leads to simpler APIs and more general implementations.
So if you’ve ever gotten really deep down into a literature review on a subject and thought, “Hey, there is a better way to explain all this!”, now you have a great place to publish peer-reviewed journal articles that will help everyone in the broader community. The exact same concept goes for OpenMDAO docs too. If you’re working with our tools and you think you see a way to explain features in a simpler and more general manner, please feel free to submit a pull request. We’ll be happy to add an acknowledgment in the docs you contributed, so you get credit for helping to pay down the research debt!
October 31, 2018
Comments Off on OpenMDAO version 2.5.0 is released!
OpenMDAO 2.5.0 is out, containing lots of new features, including some backwards-incompatible changes. Here are the release notes:
Release Notes for OpenMDAO 2.5.0
October 31, 2018
- list_outputs() method now includes a `prom_name` argument to include the promoted name in the printed output (Thanks John Jasa!).
- N2 viewer now includes tool tip (hover over) showing the promoted name of any variable.
- Improved error msg when building sparse partials with duplicate col/row entries.
- You can now build the docs without MPI/PETSc installed (you will get warnings, but no longer errors).
- Major internal refactor to reduce overhead on compute_totals calls (very noticeable on large design spaces but still cheap problems).
- Components now have a `under_complex_step` attribute you can check to see if complex-step is currently active.
- Components `distributed` attribute has been moved to an option (old attribute has been deprecated).
- MetaModelUnstructured will now use FD for partial derivatives of surrogates that don't provide analytic derivatives (user can override the default settings if they wish to use CS or different FD config).
- Improvements to SimpleGA to make it more stable, and added support constraints via penalty functions (Thanks madsmpedersen and onodip).
- Parallel FD and CS at the component and group level is now supported.
- Can turn off the analytic derivative sub-system if it is not needed via an argument to setup().
- Derivative coloring now works for problems that run under MPI.
- New Components in the standard library:
- Mux and Demux components.
- New CaseRecording/CaseReading Features:
- DesVar AND output variable bounds are both reordered now.
- Improved error msg if you try to load a non-existent file with CaseReader.
- **Experimental Feature**: Discrete data passing is now supported as an experimental feature... we're still testing it, and may change the API!
Backwards Incompatible API Changes:
- `get_objectives` method on CaseReader now returns a dict-like object.
- Output vector is now locked (read-only) when inside the `apply_nonlinear` method (you shouldn't have been changing it then anyway!).
- default step size for complex-step has been changed to 1e-40.
- Moderate refactor of the CaseReader API. It is now self-consistent.
August 1, 2018
Comments Off on OpenMDAO 2.4.0 Released!
Release Notes for OpenMDAO 2.4.0
August 1, 2018
– Better error message when upper and lower have the wrong shape for add_design_var and add_constraint methods.
– pyOptSparseDriver now runs the initial condition for ALPSO and NSGA-II optimizers.
– Normalization in EQConstraintComp and BalanceComp is now optional.
– New Components in the standard library:
– New Solvers in the standard library:
– New CaseRecording/CaseReading Features:
– .load_case() method lets you pull values from a case back into the problem.
– Updated the Case recording format for better performance.
– User can call .record_iteration() to save specific cases.
Recording options for this method are separate from driver/solver options.
This is so you can record one (or more) cases manually with *ALL* the variables in it if you want to.
Backwards-Incompatible API Changes:
– The input and output vectors are now put into read-only modes within certain component methods.
NOTE: This REALLY REALLY should not break anyone’s models, but it is technically a backwards-incompatible change…
If you were changing values in these arrays when you shouldn’t have been, you’re going to get an error now.
Fix it… the new error is enforcing correct behavior.
If it was working before, you got lucky.
– Various small bug fixes to check_partials and check_totals.
– ArmijoGoldstein linesearch iteration counter works correctly now.
– The record_derivatives recorder_option now actually does something!
June 12, 2018
Comments Off on OpenMDAO 2.3.0 Released!
We’re proud to announce the release of OpenMDAO 2.3.0. This is a substantial release that comes with many new features. Some of the new features are small, but there are are few major ones. You can read all the details in the release notes, but here are the highlights:
OpenMDAO now has an in-memory assembled Jacobian structure that can significantly reduce the computational cost for computing derivatives and solving for Newton updates when using certain kinds of linear solvers. If you have models with components that provide derivatives via the
linearize() methods, or which use FD to approximate component partials, then you’ll very likely want to use this feature. If you have any models that use the DirectSolver, then you definitely want to use this feature.
Check out the feature doc for assembled Jacobians to learn how to use this new feature. You can also read more generally about how to know if you should be using assembled Jacobians in this Theory Manual article.
If you are doing gradient-based optimization with analytic derivatives, or using our NewtonSolver, then you will really want to check out these docs and test out this new feature. We are seeing better than a 10X speed up for our models!
New docs about how to more efficiently compute derivatives:
We’ve done a massive re-write of the docs relating to how you work with derivatives. There are a bunch of advanced features for speeding up derivative computations that you might not have realized were there because the old docs were not organized that well (sorry!). So, even if you’ve been using OpenMDAO for a while now, we suggest you give the new feature docs a read. If you want to dive a bit deeper into the theory behind the advanced algorithms, we’ve also added some new Theory Manual sections. If you’ve looked over our feature docs on derivatives and are now wondering if any of the more advanced features are useful for your model then we suggest you check out that Theory Manual section.
Not Just Gradient-Based Optimization:
Although our major focus is on gradient-based optimization, we know that there are problems where gradient-free methods are useful. We (finally) ported the DOEDriver from version 1, including support for parallel execution. We also added support for parallel execution to the SimpleGADriver.
Sometimes, you want to set or get a value with different units than those with which the value was initially defined. The Problem class now has
set_val() methods that accept a
units argument. As long as the unit is compatible, OpenMDAO will handle the conversion for you when you use these methods.
April 2, 2018
Comments Off on 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
– 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:
Backwards-Incompatible API changes:
– 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.