.. _releasenotes-621: """""""""""""""""""""""""""""""""""""" Version 6.21 (2024/11/14) """""""""""""""""""""""""""""""""""""" **Highlight: Numerical robustness for ML BLUE** The multilevel best linear unbiased estimator (ML BLUE) now uses truncated SVD for all matrix solutions (previously handled by Cholesky factorization with equilibration), enabling more robust solution recovery for resouce allocations and final moment estimates. This can mitigate ill-conditioned for problems with larger group counts, for groups containing some level of model redundancy, and for larger budgets with more aggressive sample profiles. *Enabling / Accessing:* New SVD solver strategies are built-in with truncation tolerances tuned from numerical studies. *Documentation:* Refer to the reference documentation starting from :dakkw:`method-multilevel_blue` for additional information on ML BLUE and its options. Benchmark examples with three models (tunable problem), five models (steady state diffusion), and 8 models (transient diffusion) are available under dakota/test in the release distribution. **Highlight: New options for using MCMC algorithms from the MUQ library** This release exposes the DILI (Dimension-Independent Likelihood-Informed subspace :cite:p:`Cui2016109`) MCMC sampler from the `MIT UQ Library (MUQ) <https://mituq.bitbucket.io/source/_site/index.html>`_. *Enabling / Accessing:* Add the :dakkw:`method-bayes_calibration-muq-dili` keyword to the :dakkw:`bayes_calibration muq <method-bayes_calibration-muq>` method keyword group to select this submethod. MUQ is available in Linux and macOS downloads, but not yet in Windows. It must be enabled when building from source by setting the CMake variable ``DAKOTA_HAVE_MUQ=ON``. *Documentation:* Consult Dakota's keyword documentation for the :dakkw:`method-bayes_calibration-muq-dili` method or the `MUQ documentation <https://mituq.bitbucket.io/source/_site/latest/index.html>`_. **Highlight: JSON parameters and results files** This release adds support for parameters and results files in JSON format, which is intended to simplify driver development. *Enabling / Accessing:* Two new keyword groups were added to the interface portion of the input file, :dakkw:`interface-analysis_drivers-fork-parameters_format` and :dakkw:`interface-analysis_drivers-fork-results_format`. The ``json`` keyword within these groups is used to select JSON format files. In addition, the :dakkw:`interface-analysis_drivers-fork-parameters_format-aprepro` keyword was moved to the ``parameters_format`` group, and the :dakkw:`interface-analysis_drivers-fork-results_format-standard-labeled` keyword is now located in the ``results_format`` group. *Documentation:* The User's Manual describes the schemas for JSON :ref:`parameters <variables:parameters:json>` and :ref:`results <responses:results:json>` files. **Improvements by Category** *Interfaces, Input/Output* - Writing evaluations by evaluation ID is supported by the :class:`BatchSplitter` class in the :ref:`dakota.interfacing <interfaces:dakota.interfacing>` module. - The :func:`python_interface` decorator in the :ref:`dakota.interfacing <interfaces:dakota.interfacing>` module now supports batch evaluations. - Support for reading and writing JSON parameters and results files was added to :ref:`dakota.interfacing <interfaces:dakota.interfacing>`. *Models* - Vector-valued Python surrogates now supported. Python surrogates now can provide Hessians. *Optimization Methods* *UQ Methods* - Hierarchical DAG enumeration (essentially a reordering of model dependencies) is now available for weighted MLMC and MFMC. See :dakkw:`method-multilevel_sampling-weighted-search_model_graphs` and :dakkw:`method-multifidelity_sampling-search_model_graphs`. - Hierarchical sampling methods (MLMC aka ``multilevel_sampling``, and MLCV MC aka ``multilevel_multifidelity_sampling``) extended to support concurrent multi-batch processing. - New run-time group throttles for ML BLUE: ``best_conditioned`` and ``rcond_tolerance`` now prune groups based on the conditioning of their group covariances. - Improved robustness for ``offline_pilot`` mode by employing the offline covariances for both sample allocation and final moment rollup. This relaxes previous contraints on the minimum high-fidelity samples for subsequent online sampling. - Online cost recovery can now be alternated with pre-specified cost on a per model basis. - MAP pre-solve and chain samples export enabled for MUQ methods. **Miscellaneous Enhancements and Bugfixes** - Enh: When building Dakota from source with the Python direct interface enabled, NumPy 2 is supported. - Bug fix: JEGA global log file is closed when the environment is destructed. - Bug fix: When performing batch evaluations with the Python direct interface, the evaluation ID is now correctly reported. **Deprecated and Changed** - The :dakkw:`interface-analysis_drivers-fork-parameters_format-aprepro` keyword was moved to the :dakkw:`interface-analysis_drivers-fork-parameters_format` group, and the :dakkw:`interface-analysis_drivers-fork-results_format-standard-labeled` keyword is now located in the :dakkw:`interface-analysis_drivers-fork-results_format` group. - The legacy Python interface is no longer enabled in binary downloads. It is also disabled by default when building from source. - The "eval_id" in the direct Python parameters diciontary is now a string. - Many submethod-specific :dakkw:`method-bayes_calibration-muq` keywords were renamed and moved to better follow Dakota conventions. For example, the keyword ``dr_num_stages``, a control used by the :dakkw:`method-bayes_calibration-muq-delayed_rejection` and :dakkw:`method-bayes_calibration-muq-dram` algorithms, formerly resided just below the ``muq`` keyword. It has been renamed to :dakkw:`method-bayes_calibration-muq-delayed_rejection-num_stages` and made a subkeyword of both submethods. **Compatibility** - Building Dakota from source now requires a C++14 compliant compiler.