add examples

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diff --git a/examples/minimize.hs b/examples/minimize.hs
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+-- the multidimensional minimization example in the GSL manual
+import GSL
+import LinearAlgebra
+import Graphics.Plot
+
+-- the function to be minimized 
+f [x,y] = 10*(x-1)^2 + 20*(y-2)^2 + 30
+
+-- its gradient
+df [x,y] = [20*(x-1), 40*(y-2)]
+
+-- the conjugate gradient method
+minimizeCG = minimizeConjugateGradient 1E-2 1E-4 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
+
+-- 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
+
+main = do
+    -- conjugate gradient with true gradient
+    let (s,p) = minimizeCG f df [5,7]
+    print s -- solution
+    dispR 2 p -- evolution of the algorithm
+    let [x,y] = drop 2 (toColumns p)
+    mplot [x,y]  -- path from the starting point to the solution
+
+    -- conjugate gradient with estimated gradient
+    let (s,p) = minimizeCG f (gradient f) [5,7]
+    print s
+    dispR 2 p
+    mplot $ drop 2 (toColumns p)
+
+    -- without gradient, using the NM Simplex method
+    let (s,p) = minimizeS f [5,7]
+    print s
+    dispR 2 p
+    mplot $ drop 3 (toColumns p)