Dakota

Dakota is software for optimization and uncertainty quantification using computational models. It provides methods for optimization, parameter estimation, sensitivity or variance analysis, and uncertainty assessment. Engineering analysts can use it to explore designs and assess confidence in computational predictions. The C++ toolkit links simulation codes with iterative systems-analysis methods, and its capabilities can be combined into strategies such as hybrid optimization, surrogate-based optimization, and optimization under uncertainty. Dakota lists MPI for parallel distributed-memory communication, and its installation instructions include an optional PYTHONPATH setup for the dakota.interfacing Python module. Prebuilt binaries are available for macOS, Windows, and Red Hat Enterprise Linux 7 and 8; other platforms require building from source. Versions 5.0 and newer are available under the GNU Lesser General Public License. Some optional numerical libraries, including DOT, NLPQLP, and NPSOL/NLSSOL, require separate commercial licenses. Documentation for releases 6.17 and later is on Dakota’s documentation site, with older manuals available to download. The project began in 1994 and directs support questions to GitHub Discussions.