The Dakota toolkit provides a flexible, extensible interface between
analysis codes and iteration methods. Dakota contains algorithms for
optimization with gradient and nongradient-based methods; uncertainty
quantification with sampling, reliability, stochastic expansion,
and epistemic methods; parameter estimation with nonlinear least
squares methods; and sensitivity/variance analysis with design of
experiments and parameter study capabilities. These capabilities may
be used on their own or as components within advanced strategies such
as surrogate-based optimization, mixed integer nonlinear programming,
or optimization under uncertainty.
Optional dependency: openmpi (for distributed memory parallel
capabilities)
You can build with multiple jobs by setting the MAKEFLAGS environment
variable.
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