Release 0.3.0 :Date: Jul 6, 2023 Significant structural changes for long-term support of future solvers, bug-fixes, and significant improvements to documentation. Major Changes: surrogates.GaussRBF and surrogates.LocalGaussRBF now calculate model-form uncertainties structural changes to MOOP class to support propagation of uncertainty information added EI_RandomConstraint acquisition, which can be used to implement Bayesian optimization -- note that for large budgets, this is not currently recommended due to computational expense of numerical integration updated LocalGPS to use trust regions, when provided, and perform multiple restarts SurrogateOptimizer class now has access to more information about the objective, including raw simulation outputs, in order to support more diverse structure-exploiting solvers Added additional stopping criteria to both MOOP.solve() and libE_MOOP.solve() -- all stopping criteria are now optional (although at least one must be specified) but they are ordered such that calling MOOP.solve(k), where k is a positional input, will pass to the iter_max criteria and produce the same behavior as before -- closes #18 API Changes: In most cases, none. However, it is possible that if users were previously passing arguments to the MOOP.solve() method explicitly, then the name of the first positional argument has changed: budget -> max_iters For users implementing their own searches, surrogates, optimizers, or acquisitions, several classes in the structs module have been updated to support the present restructuring of the MOOP class Docs: Updated Quickstart guide and README to demonstrate recommended inputs and settings for ParMOO -- this includes no more lambda functions, which closes #50 Added a FAQ page with additional usage details and responses to frequent questions -- the answers in which close #61 Added a new tutorial on how to perform high-dimensional multiobjective optimization on a limited budget with ParMOO Changed examples and documentation to use and discuss pandas dataframes, which generally produce more legible outputs Updated libE_MOOP example to demonstrate how to retrieve data in a way that is threadsafe for both Python MP and MPI usage Requirements: We now require scipy v1.10 or newer, due to usage of qmc integration tools At the time of this release, libEnsemble is using a deprecated version of Pydantic -- for this release only we have fixed the requirement on libEnsemble to v0.9.2, but we will relax this requirement in the future once they have patched the issue Bug-fixes: Fixed an issue where in rare cases, problems with too many categorical variables could produce unexpected batch sizes Errors in definition of test problems: DTLZ5, 6, and 7 (new implementations have been confirmed against pymoo) Fixed an issue which occasionally caused the libE_MOOP class to error out during post-run cleanup when used with MPI Patched an issue with format="pandas" option for MOOP.getSimulationData() class and added a similar option to all libE_MOOP "getter" functions Minor changes: Fixed typos in docs/doc-strings Updated styles to comply with new flake8 recommendations New unit tests added Added warnings when ParMOO is run with bad budget settings