empty hmatrix-base

1 files changed, 0 insertions, 50 deletions

diff --git a/examples/minimize.hs b/examples/minimize.hs
deleted file mode 100644
index 19b2cb3..0000000
--- a/examples/minimize.hs
+++ /dev/null
@@ -1,50 +0,0 @@
--- the multidimensional minimization example in the GSL manual
-import Numeric.GSL
-import Numeric.LinearAlgebra
-import Graphics.Plot
-import Text.Printf(printf)
-
--- the function to be minimized
-f [x,y] = 10*(x-1)^2 + 20*(y-2)^2 + 30
-
--- exact gradient
-df [x,y] = [20*(x-1), 40*(y-2)]
-
--- a minimization algorithm which does not require the gradient
-minimizeS f xi = minimize NMSimplex2 1E-2 100 (replicate (length xi) 1) f xi
-
--- Numerical estimation of the gradient
-gradient f v = [partialDerivative k f v | k <- [0 .. length v -1]]
-
-partialDerivative n f v = fst (derivCentral 0.01 g (v!!n)) where
-    g x = f (concat [a,x:b])
-    (a,_:b) = splitAt n v
-
-disp = putStrLn . format "  " (printf "%.3f")
-
-allMethods :: (Enum a, Bounded a) => [a]
-allMethods = [minBound .. maxBound]
-
-test method = do
-    print method
-    let (s,p) = minimize method 1E-2 30 [1,1] f [5,7]
-    print s
-    disp p
-
-testD method = do
-    print method
-    let (s,p) = minimizeD method 1E-3 30 1E-2 1E-4 f df [5,7]
-    print s
-    disp p
-
-testD' method = do
-    putStrLn $ show method ++ " with estimated gradient"
-    let (s,p) = minimizeD method 1E-3 30 1E-2 1E-4 f (gradient f) [5,7]
-    print s
-    disp p
-
-main = do
-    mapM_ test [NMSimplex, NMSimplex2]
-    mapM_ testD allMethods
-    testD' ConjugateFR
-    mplot $ drop 3 . toColumns . snd $ minimizeS f [5,7]