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|
{- |
Module : Numeric.GSL.Root
Copyright : (c) Alberto Ruiz 2009
License : GPL
Maintainer : Alberto Ruiz (aruiz at um dot es)
Stability : provisional
Portability : uses ffi
Multidimensional root finding.
<http://www.gnu.org/software/gsl/manual/html_node/Multidimensional-Root_002dFinding.html>
The example in the GSL manual:
@import Numeric.GSL
import Numeric.LinearAlgebra(format)
import Text.Printf(printf)
rosenbrock a b [x,y] = [ a*(1-x), b*(y-x^2) ]
disp = putStrLn . format \" \" (printf \"%.3f\")
main = do
let (sol,path) = root Hybrids 1E-7 30 (rosenbrock 1 10) [-10,-5]
print sol
disp path
\> main
[1.0,1.0]
0.000 -10.000 -5.000 11.000 -1050.000
1.000 -3.976 24.827 4.976 90.203
2.000 -3.976 24.827 4.976 90.203
3.000 -3.976 24.827 4.976 90.203
4.000 -1.274 -5.680 2.274 -73.018
5.000 -1.274 -5.680 2.274 -73.018
6.000 0.249 0.298 0.751 2.359
7.000 0.249 0.298 0.751 2.359
8.000 1.000 0.878 -0.000 -1.218
9.000 1.000 0.989 -0.000 -0.108
10.000 1.000 1.000 0.000 0.000
@
-}
-----------------------------------------------------------------------------
module Numeric.GSL.Root (
root, RootMethod(..),
rootJ, RootMethodJ(..),
) where
import Data.Packed.Internal
import Data.Packed.Matrix
import Foreign
import Foreign.C.Types(CInt)
import Numeric.GSL.Internal
-------------------------------------------------------------------------
data RootMethod = Hybrids
| Hybrid
| DNewton
| Broyden
deriving (Enum,Eq,Show,Bounded)
-- | Nonlinear multidimensional root finding using algorithms that do not require
-- any derivative information to be supplied by the user.
-- Any derivatives needed are approximated by finite differences.
root :: RootMethod
-> Double -- ^ maximum residual
-> Int -- ^ maximum number of iterations allowed
-> ([Double] -> [Double]) -- ^ function to minimize
-> [Double] -- ^ starting point
-> ([Double], Matrix Double) -- ^ solution vector and optimization path
root method epsabs maxit fun xinit = rootGen (fi (fromEnum method)) fun xinit epsabs maxit
rootGen m f xi epsabs maxit = unsafePerformIO $ do
let xiv = fromList xi
n = dim xiv
fp <- mkVecVecfun (aux_vTov (checkdim1 n . fromList . f . toList))
rawpath <- vec xiv $ \xiv' ->
createMIO maxit (2*n+1)
(c_root m fp epsabs (fi maxit) // xiv')
"root"
let it = round (rawpath @@> (maxit-1,0))
path = takeRows it rawpath
[sol] = toLists $ dropRows (it-1) path
freeHaskellFunPtr fp
return (take n $ drop 1 sol, path)
foreign import ccall "root"
c_root:: CInt -> FunPtr TVV -> Double -> CInt -> TVM
-------------------------------------------------------------------------
data RootMethodJ = HybridsJ
| HybridJ
| Newton
| GNewton
deriving (Enum,Eq,Show,Bounded)
-- | Nonlinear multidimensional root finding using both the function and its derivatives.
rootJ :: RootMethodJ
-> Double -- ^ maximum residual
-> Int -- ^ maximum number of iterations allowed
-> ([Double] -> [Double]) -- ^ function to minimize
-> ([Double] -> [[Double]]) -- ^ Jacobian
-> [Double] -- ^ starting point
-> ([Double], Matrix Double) -- ^ solution vector and optimization path
rootJ method epsabs maxit fun jac xinit = rootJGen (fi (fromEnum method)) fun jac xinit epsabs maxit
rootJGen m f jac xi epsabs maxit = unsafePerformIO $ do
let xiv = fromList xi
n = dim xiv
fp <- mkVecVecfun (aux_vTov (checkdim1 n . fromList . f . toList))
jp <- mkVecMatfun (aux_vTom (checkdim2 n . fromLists . jac . toList))
rawpath <- vec xiv $ \xiv' ->
createMIO maxit (2*n+1)
(c_rootj m fp jp epsabs (fi maxit) // xiv')
"root"
let it = round (rawpath @@> (maxit-1,0))
path = takeRows it rawpath
[sol] = toLists $ dropRows (it-1) path
freeHaskellFunPtr fp
freeHaskellFunPtr jp
return (take n $ drop 1 sol, path)
foreign import ccall "rootj"
c_rootj:: CInt -> FunPtr TVV -> FunPtr TVM -> Double -> CInt -> TVM
-------------------------------------------------------
checkdim1 n v
| dim v == n = v
| otherwise = error $ "Error: "++ show n
++ " components expected in the result of the function supplied to root"
checkdim2 n m
| rows m == n && cols m == n = m
| otherwise = error $ "Error: "++ show n ++ "x" ++ show n
++ " Jacobian expected in rootJ"
|
