2 files changed, 20 insertions, 45 deletions

diff --git a/examples/minimize.hs b/examples/minimize.hs
index 11643c9..19b2cb3 100644
--- a/examples/minimize.hs
+++ b/examples/minimize.hs
@@ -4,20 +4,14 @@ import Numeric.LinearAlgebra
 import Graphics.Plot
 import Text.Printf(printf)
 
--- the function to be minimized 
+-- the function to be minimized
 f [x,y] = 10*(x-1)^2 + 20*(y-2)^2 + 30
 
--- its gradient
+-- exact gradient
 df [x,y] = [20*(x-1), 40*(y-2)]
 
--- the conjugate gradient method
-minimizeCG = minimizeConjugateGradient 1E-2 1E-4 1E-3 30
-
--- the BFGS2 method
-minimizeBFGS2 = minimizeVectorBFGS2 1E-2 1E-2 1E-3 30
-
 -- a minimization algorithm which does not require the gradient
-minimizeS f xi = minimizeNMSimplex f xi (replicate (length xi) 1) 1E-2 100
+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]]
@@ -26,47 +20,31 @@ partialDerivative n f v = fst (derivCentral 0.01 g (v!!n)) where
     g x = f (concat [a,x:b])
     (a,_:b) = splitAt n v
 
-main = do
-    putStrLn "BFGS2 with true gradient"
-    let (s,p) = minimizeBFGS2 f df [5,7]
-    print s -- solution
-    disp p -- evolution of the algorithm
-    let [x,y] = drop 2 (toColumns p)
-    mplot [x,y]  -- path from the starting point to the solution
-
-    putStrLn "conjugate gradient with true gradient"
-    let (s,p) = minimizeCG f df [5,7]
-    print s
-    disp p
-    let [x,y] = drop 2 (toColumns p)
-    mplot [x,y]
-
-    putStrLn "conjugate gradient with estimated gradient"
-    let (s,p) = minimizeCG f (gradient f) [5,7]
-    print s
-    disp p
-    mplot $ drop 2 (toColumns p)
-
-    putStrLn "without gradient, using the NM Simplex method"
-    let (s,p) = minimizeS f [5,7]
-    print s
-    disp p
-    mplot $ drop 3 (toColumns p)
-
-    putStrLn "-------------------------"
-    mapM_ test [NMSimplex,NMSimplex2]
-    mapM_ testd [ConjugateFR .. SteepestDescent]
-
 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
+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]
diff --git a/examples/root.hs b/examples/root.hs
index 2a24f0f..8546ff5 100644
--- a/examples/root.hs
+++ b/examples/root.hs
@@ -28,7 +28,4 @@ main = do
     test DNewton
     test Broyden
 
-    testJ HybridsJ
-    testJ HybridJ
-    testJ Newton
-    testJ GNewton
+    mapM_ testJ [HybridsJ .. GNewton]