diff --git a/exercice_1/conjugate_gradient.py b/exercice_1/conjugate_gradient.py
index e69de29..5f2e7a4 100644
--- a/exercice_1/conjugate_gradient.py
+++ b/exercice_1/conjugate_gradient.py
@@ -0,0 +1,192 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct  9 13:49:52 2018
+
+@author: masc
+"""
+
+import numpy as np
+
+def trans(*args):
+    args = np.array(*args)
+    if args.ndim == 1:
+        transverse = args.T
+    elif args.ndim != 1:
+        transverse = np.einsum('ij->ji', args)
+    return transverse
+def dot(a, b):   
+    arg1 = np.array(a)
+    arg2 = np.array(b)
+    if arg1.ndim == 1:
+        if arg2.ndim == 1:
+            dot = np.einsum('j, j', arg1, arg2)
+    elif arg1.ndim == 2:
+        if arg2.ndim == 1:
+            dot = np.einsum('ij, j', arg1, arg2)
+    else:
+        dot = np.dot(arg1, arg2)
+    return dot
+
+def conjugate_gradient(A, b, x):
+# initialisation  
+    r = b - np.dot(A, x) 
+    p = r
+    i = 1
+    print("### initialisation ###","\ninitial residual\t", np.dot(r.T,r))
+    
+    while True:        
+        alpha = np.dot(r.T, r) / np.dot(np.dot(p.T, A), p)
+        x = x + np.dot(alpha, p)
+        r_new = r - np.dot(np.dot(alpha,A), p)
+        beta = np.dot(r_new.T, r_new) / np.dot(r.T, r)
+        p_new = r_new + np.dot(beta, p)
+    
+        r = r_new
+        p = p_new
+        if np.dot(r.T,r) < 1e-9:#np.isclose(r_k, np.zeros(r_k.shape)).all():
+            print("### Iteration finishes: r*r < 1e9 ###")
+            print("final r \t\t", r)
+            print("final r*r \t\t",np.dot(r, r))
+            print("nit \t\t\t", i)
+            print("final x \t\t", x)
+            break
+    
+        print("residual \t\t", np.dot(r.T,r))
+        print("x \t\t\t", x)
+        print("")
+              
+        i += 1
+
+if __name__ == '__main__':
+    A = [[5,1],[1,5]]#[np.linspace(-5, 5, 100)]#[[3, -1], [-1, 3]]
+    b = [3,1]
+    x = [0,0]
+    conjugate_gradient(A=A,b=b,x=x)
+
+
+
+
+
+
+
+
+
+
+
+
+#def conj_grad(A, b, x):
+#    tol = 1e-9
+#    r = b - dot(A,x)
+#    p = r
+#    rN = dot(r, r)
+#    while True:
+#        Ap = dot(A, p)
+#        alpha = rN / dot(trans(p), Ap)
+#        x = x - alpha * p
+#        r = r - alpha * Ap
+#        rnewN = dot(r,r)
+#        print("\n")
+#        print(rnewN)
+#
+#        
+#        if rnew < tol:
+#            print(i)
+#            break
+#        beta = rnewN / rN
+#        rN = rnewN
+#        p = beta * p - r 
+#        
+#
+#A = [[np.linspace(-5, 5, 100)], [np.linspace(-5, 5, 100)]]#[[3, -1], [-1, 3]]
+#b = [1,0]
+#
+#x = [1,1]
+#
+#d = []
+#r = []
+#alpha = []
+#
+#r_k = np.dot(A, x) - b
+#d_k = -r_k
+#
+#i = 1
+#while True:
+#    print("iterations:",i)
+#    
+#    alpha_k = np.dot(r_k, r_k) / np.dot(np.dot(d_k, Q), d_k)
+#    x = x + np.dot(alpha_k, d_k)
+#    r_k1 = r_k + np.dot(np.dot(alpha_k,Q), d_k)
+#    beta_k1 = np.dot(r_k1, r_k1) / np.dot(r_k, r_k)
+#    d_k1 = -r_k1 + np.dot(beta_k1, d_k)
+#
+#    r_k = r_k1
+#    d_k = d_k1
+#    
+#    if np.isclose(r_k, np.zeros(r_k.shape)).all():
+#        print("Breaking!")
+#        print("final r_k:", r_k)
+#        print("final x:", x)
+#        break
+#
+#    print("r_k:",r_k)
+#    print("x:", x)
+#    i += 1
+#
+#
+#
+#       
+##        
+#        
+#        
+##    rTr = np.einsum('ij->ji', r) * r
+##    for i in b:
+##        pT = np.einsum('ij->ji', p)
+##        Ap = np.einsum('k,k->', A, p)
+##        alpha = rTr / (np.einsum('k,k->', pT, Ap))
+##        x = x + np.einsum('k,k->', alpha, p)
+##        r = r - np.einsum('k,k->', alpha, Ap)
+##        rsnew = np.einsum('ij->ji', r) * r
+##        
+##    return x
+##def conjugate_gradient(A, b, x=None, **kwargs):
+##    tol = 1e-9
+##    n = len(b)
+##    if not x:
+##        x = np.ones(n)
+##    r = b - np.einsum('k,k->', A, x)#np.dot(A, x)
+##    p = r
+##    r_k_norm = np.dot(r, r)
+##    for i in range(2*n):
+##        rT = r
+##        pT = p#np.einsum('ij->ji', p) 
+##        Ap = np.einsum('k,k->', A, p)
+##        rTr = np.einsum('k,k->', rT, r)
+##        print(pT.shape); print(Ap.shape)
+####        pTAp = np.einsum('k,k->', pT, Ap)
+###        alpha = rTr / pTAp
+###        x += alpha * p
+###        r += alpha * Ap
+###        r_kplus1_norm = np.einsum('k,k->', r, r)
+###        beta = r_kplus1_norm / rTr
+###        r_k_norm = r_kplus1_norm
+###        if r_kplus1_norm < tol:
+###            print('Iterations:', i)
+###            print(r_kplus1_norm)
+###            break
+###        p = beta * p - r
+###    return x
+##
+### x_ph = tf.placeholder('float32', [None, None])
+### r = tf.matmul(A, x_ph) - b
+##
+#if __name__ == '__main__':
+#    n = 100
+#    P = np.random.normal(size=[n,n]) #(-5,5,n)
+#    A = np.dot(P.T, P)
+#    b = np.ones(n)
+#    x = ones(n)
+#
+#    t1 = time.time()
+#    print('start')
+#    x = conj_grad(A=A, b=b, x=x)
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