conjugateGradient 1.3 → 1.4

raw patch · 3 files changed

+18/−16 lines, 3 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• Math/ConjugateGradient.hs +7/−4
• RELEASENOTES +8/−4
• conjugateGradient.cabal +3/−8

@@ -61,7 +61,9 @@ import System.Random               (Random, RandomGen, randomRs) import Numeric                     (showFFloat) --- | A sparse vector containing elements of type 'a'. (For our purposes, the elements will be either 'Float's or 'Double's.)+-- | A sparse vector containing elements of type 'a'. For our purposes, the elements will be either 'Float's or 'Double's. Only+-- the indices that contain non-@0@ elements should be given for efficiency purposes. (Nothing will break if you put in+-- elements that are @0@'s, it's just not as efficient.) type SV a = IM.IntMap a  -- | A sparse matrix is an int-map containing sparse row-vectors:@@ -71,8 +73,8 @@ --     * The matrix is implicitly assumed to be @nxn@, indexed by keys @(0, 0)@ to @(n-1, n-1)@. -- --     * When constructing a sparse-matrix, only put in rows that have a non-@0@ element in them for efficiency.---       (Nothing will break if you put in rows that have all @0@'s in them, it's just not as efficient.) Note---       that you have to give all the non-0 elements: Even though the matrix must be symmetric for the algorithm+--+--     * Note that you have to give all the non-0 elements: Even though the matrix must be symmetric for the algorithm --       to work, the matrix should contain all the non-@0@ elements, not just the upper (or the lower)-triangle. -- --     * Make sure the keys of the int-map is a subset of @[0 .. n-1]@, both for the row-indices and the indices of the vectors representing the sparse-rows.@@ -199,7 +201,8 @@ We represent sparse matrices and vectors using 'IM.IntMap's. In a sparse vector, we only populate those elements that are non-@0@. In a sparse matrix, we only populate those rows that contain a non-@0@ element. This leads to an efficient representation for sparse matrices and vectors, where the space usage is proportional to number of non-@0@ elements. Strictly speaking, putting non-@0@ elements-would not break the algorithms we use, but clearly they would be less efficient.+would not break the algorithms we use, but clearly they would be less efficient. Note that all non-@0@ rows should be present in the sparse+matrix: Even if we only use symmetric matrices, the algorithm still requires all rows to be present, not just the upper (or the lower)-triangle.  Indexing starts at @0@, and is assumed to be non-negative, corresponding to the row numbers. -}

@@ -1,9 +1,16 @@ Hackage: <http://hackage.haskell.org/package/conjugateGradient> GitHub:  <http://github.com/LeventErkok/conjugateGradient> -Latest Hackage released version: 1.3+Latest Hackage released version: 1.4 +Version 1.4, 2013-04-16 ======================================================================+  - Fix github source location+  - Clarify that the entire matrix should be given: Even though+    we assume it's symmetric, the algorithm needs all non-0 elements+    to be present; not just the upper (or the lower)-triangle.++====================================================================== Version 1.3, 2013-04-16   - Instead of returning an error-bound, throw an error if     no convergence is reached after 10^6 iterations. This is@@ -11,9 +18,6 @@     iterations typically indicates the input matrix is not     symmetric and positive-definite.   - Tighten import lists and the example-  - Clarify that the entire matrix should be given: Even though-    we assume it's symmetric, the algorithm needs all non-0 elements-    to be present; not just the upper (or the lower)-triangle.  ====================================================================== Version 1.2, 2013-04-15

@@ -1,14 +1,9 @@ Name:          conjugateGradient-Version:       1.3+Version:       1.4 Category:      Math Synopsis:      Sparse matrix linear-equation solver Description:   Sparse matrix linear-equation solver, using the conjugate gradient algorithm. Note that the-               technique only applies to matrices that are:-               .-                  * Symmetric-               .-                  * Positive-definite-               .+               technique only applies to matrices that are symmetric and positive-definite.                See <http://en.wikipedia.org/wiki/Conjugate_gradient_method> for details.                .                The conjugate gradient method can handle very large sparse matrices, where direct@@ -29,7 +24,7 @@  source-repository head     type:       git-    location:   git://github.com/LeventErkok/ConjugateGradient.git+    location:   git://github.com/LeventErkok/conjugateGradient.git  Library   default-language: Haskell2010