目錄樹:
331a473f1a
11.0
12.0
12.1
12.2
13.0
13.1
13.37
14.0
14.1
14.2
15.0
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/ham/chirp
user/anddt/ham/hamlib
user/anddt/libraries/girara
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user/anddt/libraries/gtksourceview4
user/anddt/libraries/imlib2
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user/anddt/libraries/libnova
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user/anddt/misc/discount
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user/anddt/network/sshguard
user/anddt/network/strongswan
user/anddt/network/umurmur
user/anddt/network/yt-dlp
user/anddt/network/znc
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
user/anddt/system/testdisk
user/anddt/system/worker
user/anddt/system/xen
user/anddt/system/zzzfm
user/belka/updates
user/chris.willing/updates
user/dslackw/updates
user/elyk/updates
user/kingbeowulf/updates
user/mario/updates
user/ponce/updates
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willysr
wxWidgets-wip
GPG-KEY
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| PyAMG.SlackBuild | d92ea38d82 academic/PyAMG: Change i486 to i586 | 2 年之前 |
| PyAMG.info | 72a20a0b0e academic/PyAMG: Fix deps. | 2 年之前 |
| README | a37908e881 academic/PyAMG: Fix README. | 4 年之前 |
| slack-desc | 080ea62570 academic/PyAMG: Added (Algebraic Multigrid Library). | 9 年之前 |
README
PyAMG is a library of **Algebraic Multigrid (AMG)** solvers with a
convenient Python interface.
AMG is a multilevel technique for solving large-scale linear systems
with optimal or near-optimal efficiency. Unlike geometric multigrid,
AMG requires little or no geometric information about the underlying
problem and develops a sequence of coarser grids directly from the
input matrix. This feature is especially important for problems
discretized on unstructured meshes and irregular grids.
convenient Python interface.
AMG is a multilevel technique for solving large-scale linear systems
with optimal or near-optimal efficiency. Unlike geometric multigrid,
AMG requires little or no geometric information about the underlying
problem and develops a sequence of coarser grids directly from the
input matrix. This feature is especially important for problems
discretized on unstructured meshes and irregular grids.