Revert pkglint-induced nonsense.

3 files changed, 56 insertions, 30 deletions

diff --git a/math/arpack/DESCR b/math/arpack/DESCR
index 3205bcda348..ded552c3db2 100644
--- a/math/arpack/DESCR
+++ b/math/arpack/DESCR
@@ -1,23 +1,35 @@
-ARPACK is a collection of Fortran77 subroutines designed to solve large scale
-eigenvalue problems. The package is designed to compute a few eigenvalues and
-corresponding eigenvectors of a general n by n matrix A. It is most appropriate
-for large sparse or structured matrices A where structured means that a
-matrix-vector product w <- Av requires order n rather than the usual order n**2
-floating point operations. This software is based upon an algorithmic variant of
-the Arnoldi process called the Implicitly Restarted Arnoldi Method (IRAM). When
-the matrix A is symmetric it reduces to a variant of the Lanczos process called
-the Implicitly Restarted Lanczos Method (IRLM). These variants may be viewed as
-a synthesis of the Arnoldi/Lanczos process with the Implicitly Shifted QR
-technique that is suitable for large scale problems. For many standard problems,
-a matrix factorization is not required. Only the action of the matrix on a
-vector is needed. ARPACK software is capable of solving large scale symmetric,
-nonsymmetric, and generalized eigenproblems from significant application areas.
-The software is designed to compute a few (k) eigenvalues with user specified
-features such as those of largest real part or largest magnitude.  Storage
-requirements are on the order of n*k locations. No auxiliary storage is
-required. A set of Schur basis vectors for the desired k-dimensional eigen-space
-is computed which is numerically orthogonal to working precision. Numerically accurate eigenvectors are available on request. Important Features:
- o Reverse Communication Interface.
- o Single and Double Precision Real Arithmetic Versions for Symmetric,
-   Non-symmetric, Standard or Generalized Problems.
-   ...and more!
+ARPACK is a collection of Fortran77 subroutines designed to solve large
+scale eigenvalue problems.
+
+The package is designed to compute a few eigenvalues and corresponding
+eigenvectors of a general n by n matrix A. It is most appropriate for large
+sparse or structured matrices A where structured means that a matrix-vector
+product w <- Av requires order n rather than the usual order n**2 floating
+point operations. This software is based upon an algorithmic variant of the
+Arnoldi process called the Implicitly Restarted Arnoldi Method (IRAM). When
+the matrix A is symmetric it reduces to a variant of the Lanczos process
+called the Implicitly Restarted Lanczos Method (IRLM). These variants may be
+viewed as a synthesis of the Arnoldi/Lanczos process with the Implicitly
+Shifted QR technique that is suitable for large scale problems. For many
+standard problems, a matrix factorization is not required. Only the action
+of the matrix on a vector is needed.  ARPACK software is capable of solving
+large scale symmetric, nonsymmetric, and generalized eigenproblems from
+significant application areas. The software is designed to compute a few (k)
+eigenvalues with user specified features such as those of largest real part
+or largest magnitude.  Storage requirements are on the order of n*k locations.
+No auxiliary storage is required. A set of Schur basis vectors for the desired
+k-dimensional eigen-space is computed which is numerically orthogonal to working
+precision. Numerically accurate eigenvectors are available on request.
+
+Important Features:
+
+    o  Reverse Communication Interface.
+    o  Single and Double Precision Real Arithmetic Versions for Symmetric,
+       Non-symmetric, Standard or Generalized Problems.
+    o  Single and Double Precision Complex Arithmetic Versions for Standard
+       or Generalized Problems.
+    o  Routines for Banded Matrices - Standard or Generalized Problems.
+    o  Routines for The Singular Value Decomposition.
+    o  Example driver routines that may be used as templates to implement
+       numerous Shift-Invert strategies for all problem types, data types
+       and precision.
diff --git a/math/eigen2/DESCR b/math/eigen2/DESCR
index f6a63080ae5..38332b7f76d 100644
--- a/math/eigen2/DESCR
+++ b/math/eigen2/DESCR
@@ -8,17 +8,24 @@ related algorithms. It is:
   o both plain matrices/vectors and abstract expressions.
   o both column-major (the default) and row-major matrix storage.
   o both basic matrix/vector manipulation and many more advanced, specialized
-    modules providing algorithms for linear algebra, geometry, quaternions, or
-    advanced array manipulation.
+    modules providing algorithms for linear algebra, geometry, quaternions,
+    or advanced array manipulation.
 * Fast.
   o Expression templates allow to intelligently remove temporaries and enable
     lazy evaluation, when that is appropriate -- Eigen takes care of this
     automatically and handles aliasing too in most cases.
   o Explicit vectorization is performed for the SSE (2 and later) and AltiVec
-    instruction sets, with graceful fallback to non-vectorized code. Expression
-    templates allow to perform these optimizations globally for whole
-    expressions.
+    instruction sets, with graceful fallback to non-vectorized code.
+    Expression templates allow to perform these optimizations globally for
+    whole expressions.
   o With fixed-size objects, dynamic memory allocation is avoided, and the
     loops are unrolled when that makes sense.
   o For large matrices, special attention is paid to cache-friendliness.
-...and more!
+* Elegant. The API is extremely clean and expressive, thanks to expression
+  templates. Implementing an algorithm on top of Eigen feels like just copying
+  pseudocode. You can use complex expressions and still rely on Eigen to
+  produce optimized code: there is no need for you to manually decompose
+  expressions into small steps.
+* Compiler-friendy. Eigen has very reasonable compilation times at least with
+  GCC, compared to other C++ libraries based on expression templates and heavy
+  metaprogramming. Eigen is also standard C++ and supports various compilers.
diff --git a/math/eigen3/DESCR b/math/eigen3/DESCR
index b6dbd3900c7..60e78c89451 100644
--- a/math/eigen3/DESCR
+++ b/math/eigen3/DESCR
@@ -21,4 +21,11 @@ related algorithms. It is:
   o With fixed-size objects, dynamic memory allocation is avoided, and the
     loops are unrolled when that makes sense.
   o For large matrices, special attention is paid to cache-friendliness.
-...and more!
+* Elegant. The API is extremely clean and expressive, thanks to expression
+  templates. Implementing an algorithm on top of Eigen feels like just copying
+  pseudocode. You can use complex expressions and still rely on Eigen to
+  produce optimized code: there is no need for you to manually decompose
+  expressions into small steps.
+* Compiler-friendy. Eigen has very reasonable compilation times at least with
+  GCC, compared to other C++ libraries based on expression templates and heavy
+  metaprogramming. Eigen is also standard C++ and supports various compilers.