push WIP

estimator/lwe_primal.py

@@ -300,10 +300,21 @@ def gaussian_heuristic_log_input(r):
 
         d = len(r)
         r = [log(x) for x in r]
-        for i, _ in enumerate(r):
-            if gaussian_heuristic_log_input(r[i:]) < D.stddev**2 * (d - i):
-                return ZZ(d - (i - 1))
-        return ZZ(2)
+
+
+        if len(r) > 8192:
+            for i in range(len(r) - 1000, len(r)):
+                print("i = {}".format(i))
+                if gaussian_heuristic_log_input(r[i:]) < D.stddev**2 * (d - i):
+                    return ZZ(d - (i - 1))
+            return ZZ(2)           
+
+        else:
+            for i, _ in enumerate(r):
+                print("i = {}".format(i))
+                if gaussian_heuristic_log_input(r[i:]) < D.stddev**2 * (d - i):
+                    return ZZ(d - (i - 1))
+            return ZZ(2)
 
     @staticmethod
     @cached_function
@@ -480,8 +491,11 @@ def cost_zeta(
             40, baseline_cost["beta"] + 1, precision=2, log_level=log_level + 1
         ) as it:
             for beta in it:
+                #print("beta = {}".format(beta))
                 it.update(f(beta))
+                #print(f(beta))
             for beta in it.neighborhood:
+                #print("beta = {}".format(beta))
                 it.update(f(beta))
             cost = it.y
 
@@ -490,9 +504,10 @@ def cost_zeta(
         # step 2. optimize d
         if cost and cost.get("tag", "XXX") != "usvp" and optimize_d:
             with local_minimum(
-                params.n, cost["d"] + cost["zeta"] + 1, log_level=log_level + 1
+                params.n, cost["d"] + cost["zeta"] + 1, log_level= log_level - 1
             ) as it:
                 for d in it:
+                    #print("d = {}".format(d))
                     it.update(f(beta=cost["beta"], d=d))
                 cost = it.y
             Logging.log("bdd", log_level, f"H2: {cost!r}")