LinearAlgebra and GSL moved to Numeric

1 files changed, 213 insertions, 0 deletions

diff --git a/lib/Numeric/GSL/Minimization.hs b/lib/Numeric/GSL/Minimization.hs
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+{-# OPTIONS_GHC -fglasgow-exts #-}
+-----------------------------------------------------------------------------
+{- |
+Module      :  Numeric.GSL.Minimization
+Copyright   :  (c) Alberto Ruiz 2006
+License     :  GPL-style
+
+Maintainer  :  Alberto Ruiz (aruiz at um dot es)
+Stability   :  provisional
+Portability :  uses ffi
+
+Minimization of a multidimensional function Minimization of a multidimensional function using some of the algorithms described in:
+
+<http://www.gnu.org/software/gsl/manual/html_node/Multidimensional-Minimization.html>
+
+-}
+-----------------------------------------------------------------------------
+module Numeric.GSL.Minimization (
+    minimizeConjugateGradient,
+    minimizeNMSimplex
+) where
+
+
+import Data.Packed.Internal
+import Data.Packed.Matrix
+import Foreign
+import Complex
+
+
+-------------------------------------------------------------------------
+
+{- | The method of Nelder and Mead, implemented by /gsl_multimin_fminimizer_nmsimplex/. The gradient of the function is not required. This is the example in the GSL manual:
+
+@minimize f xi = minimizeNMSimplex f xi (replicate (length xi) 1) 1e-2 100
+\ 
+f [x,y] = 10*(x-1)^2 + 20*(y-2)^2 + 30
+\ 
+main = do
+    let (s,p) = minimize f [5,7]
+    print s
+    print p
+\ 
+\> main
+[0.9920430849306285,1.9969168063253164]
+0. 512.500    1.082 6.500    5.
+ 1. 290.625    1.372 5.250    4.
+ 2. 290.625    1.372 5.250    4.
+ 3. 252.500    1.372 5.500    1.
+ 4. 101.406    1.823 2.625 3.500
+ 5. 101.406    1.823 2.625 3.500
+ 6.     60.    1.823    0.    3.
+ 7.  42.275    1.303 2.094 1.875
+ 8.  42.275    1.303 2.094 1.875
+ 9.  35.684    1.026 0.258 1.906
+10.  35.664    0.804 0.588 2.445
+11.  30.680    0.467 1.258 2.025
+12.  30.680    0.356 1.258 2.025
+13.  30.539    0.285 1.093 1.849
+14.  30.137    0.168 0.883 2.004
+15.  30.137    0.123 0.883 2.004
+16.  30.090    0.100 0.958 2.060
+17.  30.005 6.051e-2 1.022 2.004
+18.  30.005 4.249e-2 1.022 2.004
+19.  30.005 4.249e-2 1.022 2.004
+20.  30.005 2.742e-2 1.022 2.004
+21.  30.005 2.119e-2 1.022 2.004
+22.  30.001 1.530e-2 0.992 1.997
+23.  30.001 1.259e-2 0.992 1.997
+24.  30.001 7.663e-3 0.992 1.997@
+
+The path to the solution can be graphically shown by means of:
+
+@'GSL.Plot.mplot' $ drop 3 ('toColumns' p)@
+
+-}
+minimizeNMSimplex :: ([Double] -> Double) -- ^ function to minimize
+          -> [Double]            -- ^ starting point
+          -> [Double]            -- ^ sizes of the initial search box
+          -> Double              -- ^ desired precision of the solution
+          -> Int                 -- ^ maximum number of iterations allowed
+          -> ([Double], Matrix Double)
+          -- ^ solution vector, and the optimization trajectory followed by the algorithm
+minimizeNMSimplex f xi sz tol maxit = unsafePerformIO $ do
+    let xiv = fromList xi
+        szv = fromList sz
+        n   = dim xiv
+    fp <- mkVecfun (iv (f.toList))
+    rawpath <- createMIO maxit (n+3)
+                         (c_minimizeNMSimplex fp tol maxit // vec xiv // vec szv) 
+                         "minimizeNMSimplex" [xiv,szv]
+    let it = round (rawpath @@> (maxit-1,0))
+        path = takeRows it rawpath
+        [sol] = toLists $ dropRows (it-1) path
+    freeHaskellFunPtr fp
+    return (drop 3 sol, path)
+
+
+foreign import ccall "gsl-aux.h minimize"
+    c_minimizeNMSimplex:: FunPtr (Int -> Ptr Double -> Double) -> Double -> Int
+                       -> TVVM
+
+----------------------------------------------------------------------------------
+
+{- | The Fletcher-Reeves conjugate gradient algorithm /gsl_multimin_fminimizer_conjugate_fr/. This is the example in the GSL manual:
+
+@minimize = minimizeConjugateGradient 1E-2 1E-4 1E-3 30
+f [x,y] = 10*(x-1)^2 + 20*(y-2)^2 + 30
+\ 
+df [x,y] = [20*(x-1), 40*(y-2)]
+\  
+main = do
+    let (s,p) = minimize f df [5,7]
+    print s
+    print p
+\ 
+\> main
+[1.0,2.0]
+ 0. 687.848 4.996 6.991
+ 1. 683.555 4.989 6.972
+ 2. 675.013 4.974 6.935
+ 3. 658.108 4.944 6.861
+ 4. 625.013 4.885 6.712
+ 5. 561.684 4.766 6.415
+ 6. 446.467 4.528 5.821
+ 7. 261.794 4.053 4.632
+ 8.  75.498 3.102 2.255
+ 9.  67.037 2.852 1.630
+10.  45.316 2.191 1.762
+11.  30.186 0.869 2.026
+12.     30.    1.    2.@
+
+The path to the solution can be graphically shown by means of:
+
+@'GSL.Plot.mplot' $ drop 2 ('toColumns' p)@
+
+-}     
+minimizeConjugateGradient ::
+       Double        -- ^ initial step size
+    -> Double        -- ^ minimization parameter   
+    -> Double        -- ^ desired precision of the solution (gradient test)
+    -> Int           -- ^ maximum number of iterations allowed
+    -> ([Double] -> Double) -- ^ function to minimize
+    -> ([Double] -> [Double])      -- ^ gradient
+    -> [Double]             -- ^ starting point
+    -> ([Double], Matrix Double)        -- ^ solution vector, and the optimization trajectory followed by the algorithm
+minimizeConjugateGradient istep minimpar tol maxit f df xi = unsafePerformIO $ do
+    let xiv = fromList xi
+        n = dim xiv
+        f' = f . toList
+        df' = (fromList . df . toList)
+    fp <- mkVecfun (iv f')
+    dfp <- mkVecVecfun (aux_vTov df')
+    print "entro"
+    rawpath <- createMIO maxit (n+2)
+                         (c_minimizeConjugateGradient fp dfp istep minimpar tol maxit // vec xiv)
+                         "minimizeDerivV" [xiv]
+    print "salgo"
+    let it = round (rawpath @@> (maxit-1,0))
+        path = takeRows it rawpath
+        sol = toList $ cdat $ dropColumns 2 $ dropRows (it-1) path
+    freeHaskellFunPtr fp
+    freeHaskellFunPtr dfp
+    return (sol,path)
+
+
+foreign import ccall "gsl-aux.h minimizeWithDeriv"
+    c_minimizeConjugateGradient :: FunPtr (Int -> Ptr Double -> Double)
+                                -> FunPtr (Int -> Ptr Double -> Ptr Double -> IO ())
+                                -> Double -> Double -> Double -> Int
+                                -> TVM
+
+---------------------------------------------------------------------
+iv :: (Vector Double -> Double) -> (Int -> Ptr Double -> Double)
+iv f n p = f (createV n copy "iv" []) where
+    copy n q = do 
+        copyArray q p n
+        return 0
+
+-- | conversion of Haskell functions into function pointers that can be used in the C side
+foreign import ccall "wrapper"
+    mkVecfun :: (Int -> Ptr Double -> Double)
+             -> IO( FunPtr (Int -> Ptr Double -> Double))
+
+-- | another required conversion
+foreign import ccall "wrapper"
+    mkVecVecfun :: (Int -> Ptr Double -> Ptr Double -> IO ())
+                -> IO (FunPtr (Int -> Ptr Double -> Ptr Double->IO()))
+
+aux_vTov :: (Vector Double -> Vector Double) -> (Int -> Ptr Double -> Ptr Double -> IO())
+aux_vTov f n p r = g where
+    V {fptr = pr, ptr = t} = f x
+    x = createV n copy "aux_vTov" []
+    copy n q = do
+        copyArray q p n
+        return 0
+    g = withForeignPtr pr $ \_ -> copyArray r t n
+
+--------------------------------------------------------------------
+
+createV n fun msg ptrs = unsafePerformIO $ do
+    r <- createVector n
+    fun // vec r // check msg ptrs
+    return r
+
+createM r c fun msg ptrs = unsafePerformIO $ do
+    r <- createMatrix RowMajor r c
+    fun // mat cdat r // check msg ptrs
+    return r
+
+createMIO r c fun msg ptrs = do
+    r <- createMatrix RowMajor r c
+    fun // mat cdat r // check msg ptrs
+    return r