Branch:
wxWidgets-wip
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12.0
12.1
12.2
13.0
13.1
13.37
14.0
14.1
14.2
15.0
bedlam
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dive
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gitignore
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willysr
wxWidgets-wip
GPG-KEY
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Lorenzo Trevisan
dfb1f3be71
academic/dakota: Update BUILD.
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2 years ago | |
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| README | 2c3623e58a academic/dakota: Fix README. | 3 years ago |
| dakota.SlackBuild | dfb1f3be71 academic/dakota: Update BUILD. | 2 years ago |
| dakota.csh | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 6 years ago |
| dakota.info | fc0732657e academic/dakota: Added documentation. | 2 years ago |
| dakota.sh | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 6 years ago |
| doinst.sh | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 6 years ago |
| slack-desc | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 6 years ago |
README
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.
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.