From 043555c25049c2bd4ce8c2b9adf10096312e8f9f Mon Sep 17 00:00:00 2001
From: Gaetan D <Gaetan@doueneau-pc2.crans.org>
Date: Tue, 10 Jan 2017 21:47:58 +0100
Subject: [PATCH] done

---
 new_separation.py        |  2 +-
 rapport/presentation.tex | 11 +++++--
 testperfs.py             | 70 +++++++++++++++++++++++++++++++++++++++-
 3 files changed, 78 insertions(+), 5 deletions(-)

diff --git a/new_separation.py b/new_separation.py
index 96c870f..baec8aa 100644
--- a/new_separation.py
+++ b/new_separation.py
@@ -372,4 +372,4 @@ def test_separation_on_file (filename):
     
     
     
-test_separation_on_file("brazil58")
+test_separation_on_file("hk48")
diff --git a/rapport/presentation.tex b/rapport/presentation.tex
index 04bf76a..a133822 100644
--- a/rapport/presentation.tex
+++ b/rapport/presentation.tex
@@ -440,7 +440,7 @@
 
 \ds
 
-Mais des performances immondes.
+Mais ce n'est pas très rapide (inversion de matrice à chaque étape, notemment).
 
 \end{frame}
 
@@ -453,13 +453,18 @@ Mais des performances immondes.
     
     \ds
     
-    Ce n'est pas très efficace. Ni très précis.
+    $24$s pour répondre à 100 instances (200 contraintes, 100 variables).
     
-    \ds
+    \es
 
     \begin{block}{\texttt{CVXPy}}
         Interface élégante de \texttt{CVXopt} associée à Python. Très efficace.
     \end{block}
+    
+    \ds
+    
+    $4$s pour les 100 instances. Peu d'erreurs de faisabilité.
+
 \end{frame}
 
 \begin{frame}
diff --git a/testperfs.py b/testperfs.py
index 3093260..65f3d1a 100644
--- a/testperfs.py
+++ b/testperfs.py
@@ -2,6 +2,39 @@ from lineartools import invert
 import numpy as np
 import random
 import time
+import scipy.optimize as scopt
+import cvxpy as cvx
+
+
+
+
+def vrai_simplexe (A,b,c):
+    AA = np.copy(np.array(A, dtype = float))
+    bb = np.copy(np.array(b, dtype = float))
+    cc = np.copy(np.array(c, dtype = float))
+    res = scopt.linprog(c=cc, A_eq=AA, b_eq=bb, 
+                        bounds=(0,None), method='simplex', callback=None,
+                        options={ "maxiter" : 100000})
+    # logging.debug ("Résultat de l'appel à simplexe : {}".format (res))
+    return res
+    
+    
+def simpl_cvx(A,b,c):
+    
+    m,n = A.shape
+    assert(n == len(c) and (m==len(b)))
+    
+    x = cvx.Variable(n)
+    objective = cvx.Minimize(cvx.sum_entries(c*x))
+    
+    constraints = [0 <= x, A*x == b]
+    
+    prob = cvx.Problem(objective, constraints)
+    opt = prob.solve()
+    s = prob.status
+    
+    return({"status":s})
+    
 
 def testinv():
 
@@ -23,8 +56,43 @@ def testinv():
             b = time.clock()
             sumlin += b-a
     print("=============")
-    print("Numpy :"sumnp)
+    print("Numpy :",sumnp)
     print("Gauss :",sumlin)
+    
+    return
         
     
+def testsimpl():
+    
+    sumnp = 0
+    sumlin = 0
+    
+    sumopt = 0
+    sumcvx = 0
+    
+    for i in range(1000):
+        print("Iteration",i)
+        m = 100
+        n = 200
+        
+        A = np.random.rand(m,n)*10 - 5*np.ones((m,n))
+        b = np.random.rand(m)*2
+        c = np.random.rand(n)
+        
+        t =time.clock()
+        res = vrai_simplexe(A,b,c)
+        tt = time.clock()
+        sumopt += tt - t
+        print("Optimize",res["message"])
+        
+        t = time.clock()
+        res = simpl_cvx(A,b,c)
+        tt = time.clock()
+        sumcvx += tt - t
+        print("CVX",res["status"])
+        
+    print("=============")
+    print("Optimize :",sumopt)
+    print("CVX :",sumcvx)
+    return
     
-- 
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