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+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