feat: added chsh-primal.py

implementation/chsh-primal.py

@@ -0,0 +1,111 @@
+from math import sqrt
+import numpy as np
+from itertools import product
+import collections
+from gurobipy import *
+
+# (x,y) domain and player names;
+domain_xy = [0,1]
+domain_ab = [-1,1]
+players = ['a','b']
+
+p = collections.defaultdict(int)
+i = 0
+for x,y in product(domain_xy, repeat=2):
+    for a,b in product(domain_ab, repeat=2):
+        p[a,b,x,y] = i
+        i += 1
+
+print(p)
+
+# <ψ| A_i x B_j |ψ> = 1/sqrt(2) * ij
+A1 = np.zeros([4,16])
+B1 = np.zeros([4])
+i = 0
+for x,y in product(domain_xy, repeat=2):
+    for a,b in product(domain_ab, repeat=2):
+        A1[i,p[a,b,x,y]] = a*b
+    B1[i] = (-1)**(x*y) / sqrt(2)
+    i += 1
+
+# <ψ| A_i x B_j |ψ> = 1
+A2 = np.zeros([4,16])
+B2 = np.zeros([4])
+i = 0
+for x,y in product(domain_xy, repeat=2):
+    for a,b in product(domain_ab, repeat=2):
+        A2[i,p[a,b,x,y]] = 1
+    B2[i] = 1
+    i += 1
+
+# <ψ| A_i x 1_B |ψ> = 0
+A3 = np.zeros([4,16])
+B3 = np.zeros([4])
+i=0
+for x,y in product(domain_xy, repeat=2):
+    for a,b in product(domain_ab, repeat=2):
+        A3[i,p[a,b,x,y]] = a
+    B3[i] = 0
+    i += 1
+
+# <ψ| 1_A x B_j |ψ> = 0
+A4 = np.zeros([4,16])
+B4 = np.zeros([4])
+i=0
+for x,y in product(domain_xy, repeat=2):
+    for a,b in product(domain_ab, repeat=2):
+        A4[i,p[a,b,x,y]] = b
+    B4[i] = 0
+    i += 1
+
+# Constraint Coefficients
+A = np.concatenate((A1,A2,A3,A4), axis=0)
+
+# Values
+B = np.concatenate((B1,B2,B3,B4), axis=None)
+
+print(A)
+print(B)
+
+# Solve linear system
+X = np.linalg.solve(A,B)
+
+# P vector
+P = X
+
+print(X)
+
+# linear program
+m = Model()
+
+def vec_d_lambda(l):
+    dl = []
+    for x,y in product(domain_xy, repeat=2):
+        for a,b in product(domain_ab, repeat=2):
+            dl.append(int(l[x] == a and l[y+2] == b))
+    return dl
+
+lambdas = []
+for x,y in product(domain_xy, repeat=2):
+    for a,b in product(domain_ab, repeat=2):
+        lambdas.append((a,b,x,y))
+
+mu_lambda = [m.addVar(name=f"mu_{i}",vtype="C") for i in range(len(lambdas))]
+
+Q = m.addVar(name="Q",vtype="C")
+
+D_l = [vec_d_lambda(l) for l in lambdas]
+
+for i, p in enumerate(P):
+    m.addConstr(Q*p + (1-Q) - quicksum(mu_lambda[j] * D_l[j][i] for j in range(len(lambdas))) <= 0)
+
+m.addConstr(quicksum(mu_lambda[i] for i in range(len(lambdas))) == 1)
+
+for i in range(len(lambdas)):
+    m.addConstr(mu_lambda[i] >= 0)
+
+m.update()
+
+m.setObjective(Q, GRB.MINIMIZE)
+
+m.optimize()