目錄樹:
331a473f1a
11.0
12.0
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14.0
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bedlam
bedlam-name-fixes
dive
erik
gitignore
libressl-gopher-test
master
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python-renames
rworkman
urchlay
user/aclemons/updates
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user/anddt/office/gnumeric
user/anddt/office/sigil
user/anddt/system/FontAwesome
user/anddt/system/conky
user/anddt/system/fakeroot
user/anddt/system/hplip-plugin
user/anddt/system/postgresql
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willysr
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Lorenzo Trevisan
00a702a66e
academic/dakota: Updated for version 6.17.0.
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1 年之前 | |
|---|---|---|
| .. | ||
| README | a5edcc03f1 academic/dakota: Updated for version 6.16.0. | 2 年之前 |
| dakota.SlackBuild | 00a702a66e academic/dakota: Updated for version 6.17.0. | 1 年之前 |
| dakota.csh | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 7 年之前 |
| dakota.info | 00a702a66e academic/dakota: Updated for version 6.17.0. | 1 年之前 |
| dakota.sh | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 7 年之前 |
| doinst.sh | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 7 年之前 |
| slack-desc | 2b3ebf4023 academic/dakota: Added (optimization toolkit). | 7 年之前 |
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 (e.g. export MAKEFLAGS="-j$(nproc)" at bash prompt).
analysis codes and iteration methods. Dakota contains algorithms for
optimization with gradient and nongradient-based methods; uncertainty
quantification with sampling, reliability, stochastic expansion,
and epistemic methods; parameter estimation with nonlinear least
squares methods; and sensitivity/variance analysis with design of
experiments and parameter study capabilities. These capabilities may
be used on their own or as components within advanced strategies such
as surrogate-based optimization, mixed integer nonlinear programming,
or optimization under uncertainty.
Optional dependency: openmpi (for distributed memory parallel
capabilities)
You can build with multiple jobs by setting the MAKEFLAGS environment
variable (e.g. export MAKEFLAGS="-j$(nproc)" at bash prompt).