opti production avec contraintes

opti_prod.py

@@ -3,6 +3,10 @@ import matplotlib.pyplot as plt
 import PyQt5 as qt
 
 ptot = 1000 # [MW]
+p2max = 400 # [MW]
+p23max = 500
+t21 = 0.4545
+t22 = 0.8182
 
 def C1(x):
     return 30*x + 0.01*x**2
@@ -11,7 +15,13 @@ def C2(x):
     return 20*x + 0.02*x**2
 
 def f(x):
-    return C1(x[0]) + C2(x[1]) + x[-1] * (ptot - x[0] - x[1])
+    return C1(x[0]) + C2(x[1]) + x[2] * (ptot - x[0] - x[1])
+
+def f2(x):
+    return f(x[0:3]) - abs(x[3]) * (p2max - x[1])
+
+def f3(x):
+    return f(x[0:3]) - abs(x[3]) * (p23max - t21 * x[0] - t22 * x[1])
 
 def grad(f, x, h=1e-4):
     res = []
@@ -27,21 +37,27 @@ def norm(x):
     return np.sqrt(n)
 
 def g(x):
-    return norm(grad(f, x))
+    return norm(grad(f, x, h=1e-5))
+
+def g2(x):
+    return norm(grad(f2, x, h=1e-6))
 
-def minize(f, x0, h=1e-4, step=1e-1, tol=1e-8, N=1e4, echo=False):
+def g3(x):
+    return norm(grad(f3, x, h=1e-6))
+
+def minimize(f, x0, h=1e-4, step=1e-1, tol=1e-8, N=1e4, echo=False):
     x = x0
     g = grad(f, x, h)
-    print(g)
     n = 0
     prev = norm(g) + 2*tol
-    print(prev, norm(g), abs(norm(g) - prev))
+
     while abs(norm(g) - prev) > tol:
         n += 1
         prev = norm(g)
         for i in range(len(x)):
             x[i] -= g[i] * step
         g = grad(f, x, h)
+
         if (n % 100 == 0) and echo:
             print("Itération ", n)
             print("norm(g) = ", norm(g))
@@ -50,11 +66,22 @@ def minize(f, x0, h=1e-4, step=1e-1, tol=1e-8, N=1e4, echo=False):
             print("g = ", g)
         if n > N:
             return x
-    
     return x
 
-#print(f([500, 500, 40]))
-#print(f([450, 450, 35]))
-#print(C1(500))
-#print(C2(500))
-print(minize(g, [0, 0, 0]))
+def custom_minimize(f, x0):
+    res_app = minimize(f, x0, step=5e-1)
+    print(res_app)
+    res_app = minimize(f, res_app, step=1e-3, tol=1e-12, h=1e-5)
+    print(res_app)
+    res_app = minimize(f, res_app, step=1e-5, tol=1e-14, h=1e-5)
+    print(res_app)
+    res_app = minimize(f, res_app, step=1e-6, tol=1e-16, h=5e-6)
+    print(res_app)
+    return res_app
+
+
+print(minimize(g, [0, 0, 0]))
+
+custom_minimize(g2, [0, 0, 0, 0.01])
+
+custom_minimize(g3, [0, 0, 0, 0.01])