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|
{-# LANGUAGE FlexibleContexts #-}
{- |
Module : Numeric.GSL.Minimization
Copyright : (c) Alberto Ruiz 2006-9
License : GPL
Maintainer : Alberto Ruiz
Stability : provisional
Minimization of a multidimensional function using some of the algorithms described in:
<http://www.gnu.org/software/gsl/manual/html_node/Multidimensional-Minimization.html>
The example in the GSL manual:
@
f [x,y] = 10*(x-1)^2 + 20*(y-2)^2 + 30
main = do
let (s,p) = minimize NMSimplex2 1E-2 30 [1,1] f [5,7]
print s
print p
@
>>> main
[0.9920430849306288,1.9969168063253182]
0.000 512.500 1.130 6.500 5.000
1.000 290.625 1.409 5.250 4.000
2.000 290.625 1.409 5.250 4.000
3.000 252.500 1.409 5.500 1.000
...
22.000 30.001 0.013 0.992 1.997
23.000 30.001 0.008 0.992 1.997
The path to the solution can be graphically shown by means of:
@'Graphics.Plot.mplot' $ drop 3 ('toColumns' p)@
Taken from the GSL manual:
The vector Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a quasi-Newton method which builds up an approximation to the second derivatives of the function f using the difference between successive gradient vectors. By combining the first and second derivatives the algorithm is able to take Newton-type steps towards the function minimum, assuming quadratic behavior in that region.
The bfgs2 version of this minimizer is the most efficient version available, and is a faithful implementation of the line minimization scheme described in Fletcher's Practical Methods of Optimization, Algorithms 2.6.2 and 2.6.4. It supercedes the original bfgs routine and requires substantially fewer function and gradient evaluations. The user-supplied tolerance tol corresponds to the parameter \sigma used by Fletcher. A value of 0.1 is recommended for typical use (larger values correspond to less accurate line searches).
The nmsimplex2 version is a new O(N) implementation of the earlier O(N^2) nmsimplex minimiser. It calculates the size of simplex as the rms distance of each vertex from the center rather than the mean distance, which has the advantage of allowing a linear update.
-}
module Numeric.GSL.Minimization (
minimize, minimizeV, MinimizeMethod(..),
minimizeD, minimizeVD, MinimizeMethodD(..),
uniMinimize, UniMinimizeMethod(..),
minimizeNMSimplex,
minimizeConjugateGradient,
minimizeVectorBFGS2
) where
import Numeric.LinearAlgebra.HMatrix hiding(step)
import Numeric.GSL.Internal
import Foreign.Ptr(Ptr, FunPtr, freeHaskellFunPtr)
import Foreign.C.Types
import System.IO.Unsafe(unsafePerformIO)
------------------------------------------------------------------------
{-# DEPRECATED minimizeNMSimplex "use minimize NMSimplex2 eps maxit sizes f xi" #-}
minimizeNMSimplex f xi szs eps maxit = minimize NMSimplex eps maxit szs f xi
{-# DEPRECATED minimizeConjugateGradient "use minimizeD ConjugateFR eps maxit step tol f g xi" #-}
minimizeConjugateGradient step tol eps maxit f g xi = minimizeD ConjugateFR eps maxit step tol f g xi
{-# DEPRECATED minimizeVectorBFGS2 "use minimizeD VectorBFGS2 eps maxit step tol f g xi" #-}
minimizeVectorBFGS2 step tol eps maxit f g xi = minimizeD VectorBFGS2 eps maxit step tol f g xi
-------------------------------------------------------------------------
data UniMinimizeMethod = GoldenSection
| BrentMini
| QuadGolden
deriving (Enum, Eq, Show, Bounded)
-- | Onedimensional minimization.
uniMinimize :: UniMinimizeMethod -- ^ The method used.
-> Double -- ^ desired precision of the solution
-> Int -- ^ maximum number of iterations allowed
-> (Double -> Double) -- ^ function to minimize
-> Double -- ^ guess for the location of the minimum
-> Double -- ^ lower bound of search interval
-> Double -- ^ upper bound of search interval
-> (Double, Matrix Double) -- ^ solution and optimization path
uniMinimize method epsrel maxit fun xmin xl xu = uniMinimizeGen (fi (fromEnum method)) fun xmin xl xu epsrel maxit
uniMinimizeGen m f xmin xl xu epsrel maxit = unsafePerformIO $ do
fp <- mkDoublefun f
rawpath <- createMIO maxit 4
(c_uniMinize m fp epsrel (fi maxit) xmin xl xu)
"uniMinimize"
let it = round (rawpath `atIndex` (maxit-1,0))
path = takeRows it rawpath
[sol] = toLists $ dropRows (it-1) path
freeHaskellFunPtr fp
return (sol !! 1, path)
foreign import ccall safe "uniMinimize"
c_uniMinize:: CInt -> FunPtr (Double -> Double) -> Double -> CInt -> Double -> Double -> Double -> TM Res
data MinimizeMethod = NMSimplex
| NMSimplex2
deriving (Enum,Eq,Show,Bounded)
-- | Minimization without derivatives
minimize :: MinimizeMethod
-> Double -- ^ desired precision of the solution (size test)
-> Int -- ^ maximum number of iterations allowed
-> [Double] -- ^ sizes of the initial search box
-> ([Double] -> Double) -- ^ function to minimize
-> [Double] -- ^ starting point
-> ([Double], Matrix Double) -- ^ solution vector and optimization path
-- | Minimization without derivatives (vector version)
minimizeV :: MinimizeMethod
-> Double -- ^ desired precision of the solution (size test)
-> Int -- ^ maximum number of iterations allowed
-> Vector Double -- ^ sizes of the initial search box
-> (Vector Double -> Double) -- ^ function to minimize
-> Vector Double -- ^ starting point
-> (Vector Double, Matrix Double) -- ^ solution vector and optimization path
minimize method eps maxit sz f xi = v2l $ minimizeV method eps maxit (fromList sz) (f.toList) (fromList xi)
where v2l (v,m) = (toList v, m)
ww2 w1 o1 w2 o2 f = w1 o1 $ \a1 -> w2 o2 $ \a2 -> f a1 a2
minimizeV method eps maxit szv f xiv = unsafePerformIO $ do
let n = size xiv
fp <- mkVecfun (iv f)
rawpath <- ww2 vec xiv vec szv $ \xiv' szv' ->
createMIO maxit (n+3)
(c_minimize (fi (fromEnum method)) fp eps (fi maxit) // xiv' // szv')
"minimize"
let it = round (rawpath `atIndex` (maxit-1,0))
path = takeRows it rawpath
sol = flatten $ dropColumns 3 $ dropRows (it-1) path
freeHaskellFunPtr fp
return (sol, path)
foreign import ccall safe "gsl-aux.h minimize"
c_minimize:: CInt -> FunPtr (CInt -> Ptr Double -> Double) -> Double -> CInt -> TV(TV(TM Res))
----------------------------------------------------------------------------------
data MinimizeMethodD = ConjugateFR
| ConjugatePR
| VectorBFGS
| VectorBFGS2
| SteepestDescent
deriving (Enum,Eq,Show,Bounded)
-- | Minimization with derivatives.
minimizeD :: MinimizeMethodD
-> Double -- ^ desired precision of the solution (gradient test)
-> Int -- ^ maximum number of iterations allowed
-> Double -- ^ size of the first trial step
-> Double -- ^ tol (precise meaning depends on method)
-> ([Double] -> Double) -- ^ function to minimize
-> ([Double] -> [Double]) -- ^ gradient
-> [Double] -- ^ starting point
-> ([Double], Matrix Double) -- ^ solution vector and optimization path
-- | Minimization with derivatives (vector version)
minimizeVD :: MinimizeMethodD
-> Double -- ^ desired precision of the solution (gradient test)
-> Int -- ^ maximum number of iterations allowed
-> Double -- ^ size of the first trial step
-> Double -- ^ tol (precise meaning depends on method)
-> (Vector Double -> Double) -- ^ function to minimize
-> (Vector Double -> Vector Double) -- ^ gradient
-> Vector Double -- ^ starting point
-> (Vector Double, Matrix Double) -- ^ solution vector and optimization path
minimizeD method eps maxit istep tol f df xi = v2l $ minimizeVD
method eps maxit istep tol (f.toList) (fromList.df.toList) (fromList xi)
where v2l (v,m) = (toList v, m)
minimizeVD method eps maxit istep tol f df xiv = unsafePerformIO $ do
let n = size xiv
f' = f
df' = (checkdim1 n . df)
fp <- mkVecfun (iv f')
dfp <- mkVecVecfun (aux_vTov df')
rawpath <- vec xiv $ \xiv' ->
createMIO maxit (n+2)
(c_minimizeD (fi (fromEnum method)) fp dfp istep tol eps (fi maxit) // xiv')
"minimizeD"
let it = round (rawpath `atIndex` (maxit-1,0))
path = takeRows it rawpath
sol = flatten $ dropColumns 2 $ dropRows (it-1) path
freeHaskellFunPtr fp
freeHaskellFunPtr dfp
return (sol,path)
foreign import ccall safe "gsl-aux.h minimizeD"
c_minimizeD :: CInt
-> FunPtr (CInt -> Ptr Double -> Double)
-> FunPtr (TV (TV Res))
-> Double -> Double -> Double -> CInt
-> TV (TM Res)
---------------------------------------------------------------------
checkdim1 n v
| size v == n = v
| otherwise = error $ "Error: "++ show n
++ " components expected in the result of the gradient supplied to minimizeD"
|
