.. _releasenotes-617:

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Version 6.17 (2022/11/15)
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**Highlight: Integrated User Manual**

Dakota 6.17 includes a beta version of a new Sphinx-based integrated
user manual. It aggregates content from the historical User's, Theory,
and Reference manuals, as well as the Dakota website. Feedback on this
new compendium is invited.

*Enabling / Accessing:* Primarily from https://dakota.sandia.gov, also
in :file:`<dakota_src>/docs/user`.

**Highlight: User Support Moves to Github**

External user support is moving to Github Discussions. Discussions offers
many advantages over the dakota-users listserv, including threaded
conversations, advanced formatting via markdown, LaTeX-formatted mathematical
expressions, and more.

We plan to operate dakota-users and the new Discussions forums concurrently
until February 15, 2023, when dakota-users will be shut down.

*Enabling / Accessing:* Create an account on https://github.com/ and visit
Dakota's `Discussions page <https://github.com/orgs/snl-dakota/discussions>`_.


**Improvements by Category**

*Graphical User Interface*

- Experimental Multi-level/Multi-fidelity UQ editor adds support for
  ensemble vs. hierarchical surrogate models and has basic estimator
  performance prediction based on pilot samples.

*Interfaces, Input/Output*

- Allow the experimental Python plugin interface to work correctly
  as a callback underneath a top-level Dakota Python environment.

- Updated version of ``pyprepro/dprepro`` addresses numerous small
  issues.

*UQ Methods*

- MLMF sampling: Refined sample summary output to distinguish the
  allocated sample profile from the successfully evaluated samples,
  clarifying the final results.  These counts may differ in the
  presence of solution_mode pilot_projection, final_statistics
  estimator_performance, or simulation fault tolerance.

- MLMF sampling: For fault tolerant sampling, allocations no longer
  backfill for simulation failures by default (reflecting the
  observation that simulation faults are almost always biased in the
  parameter space, and backfill amplifies this bias.)

- MLMF sampling: For numerical solutions in ACV and MFMC based on
  solving optimization subproblems, add support for minimizing total
  cost with respect to an accuracy constraint and support these alternate
  objective/constraint definitions within bi-level model tuning.

- MLMF sampling: Hardened online cost recovery for edge cases.

- Update (experimental) MUQ package for Bayesian calibration

*Documentation Infrastructure*

- New Sphinx-based documentation infrastructure unifies historical
  manuals and permits building the Dakota documentation on its own or
  as part of a CMake-based Dakota build.
 
**Miscellaneous Enhancements and Bugfixes**

- Enh: Make build system more tolerant of HDF5/h5py variants when
  registering unit tests. Allow working with h5py 3.0+.

- Enh: Routine testing on RHEL8 operating system with system-default
  compilers and tools.

- Enh: Console output/errors are more reliably and consistently
  redirected when options are given in the input file. (Most
  TPL-generated output will still not appear in them however).

- Enh: Restart file versioning. Restart files now have embedded
  version information and will attempt to detect whether an imported
  file is compatible.

- Enh: Refined check for zero variance QoIs and other cases that could
  lead to numerical exceptions, e.g., in calculating Sobol' indices.

- Enh: Enable exporting PCE coefficients when using in Bayesian
  calibration.

- Bug fix: MLMF UQ methods properly generate tabular output for
  concurrent evaluations

- Bug fix: Dakota should now compile when using a C++17-compliant
  compiler. (Thanks to Heidi Thornquist for reporting and testing.)

- Bug fix: Properly map inactive variables through subspace models,
  notably discrete string variables.

**Deprecated and Changed**

- Dakota no longer supports Python 2 for Dakota-provided Python
  utilities such as dprepro or dakota.interfacing, though tools may
  continue to work for some time.

- Migrated to std C++ random number generators and distribution
  utilities in a number of places in Dakota and associate TPLs. This
  favored over rand()/srand() and Boost utilities. Will break
  reproducibility of specific sample sets w.r.t. historical Dakota
  versions, even on the same architecture and toolchain.

- Future (post-6.17) Dakota releases will require a C++14-compliant
  compiler and will only be tested on gcc-8.5.0 and newer (and similar
  vintage) compilers.

- LaTeX is no longer required to build default versions of Dakota
  manuals.

**Compatibility**

- CMake 3.17 or newer is required to compile Dakota

- Documentation tools:

  * Sphinx and associated plugins are required for building the new
    integrated user manual.

  * Java 11 JDK is required for documentation generation and input
    specification maintenance.

  * Doxygen 1.8.20 is required for building the developer manual.

- No changes to minimum C++11 compiler requirement, Boost 1.69, or
  required/optional external TPLs.

**Other Notes and Known Issues**
