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
