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authorB. Watson <yalhcru@gmail.com>2020-10-11 15:35:52 -0400
committerWilly Sudiarto Raharjo <willysr@slackbuilds.org>2020-10-17 09:36:37 +0700
commita37908e881a0fcf2aef608cacbccbb2954ab36eb (patch)
tree498b0c2e4642f7947b9928027b0e7f182bf27951 /academic
parenta9e9805cf5780070c4117217e08f8c93323d0acf (diff)
academic/PyAMG: Fix README.
Signed-off-by: B. Watson <yalhcru@gmail.com> Signed-off-by: Willy Sudiarto Raharjo <willysr@slackbuilds.org>
Diffstat (limited to 'academic')
-rw-r--r--academic/PyAMG/README16
1 files changed, 8 insertions, 8 deletions
diff --git a/academic/PyAMG/README b/academic/PyAMG/README
index c72262fa351c7..f5a2ac3fe5d90 100644
--- a/academic/PyAMG/README
+++ b/academic/PyAMG/README
@@ -1,9 +1,9 @@
-PyAMG is a library of **Algebraic Multigrid (AMG)** solvers with a convenient
-Python interface.
+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.
+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.