Merge pull request #112 from malb/issue-110

fix Issue 110
malb · May 8, 2024 · f18533a · f18533a
2 parents 027c60a + 56111bc
commit f18533a
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Showing 2 changed files with 26 additions and 2 deletions.
diff --git a/estimator/lwe_dual.py b/estimator/lwe_dual.py
@@ -591,6 +591,7 @@ def cost(
             k_fft,
             p,
         )
+
         rho, T_sample, _, beta_sieve = red_cost_model.short_vectors(
             beta, N=N, d=k_lat + m, sieve_dim=beta_sieve
         )

diff --git a/estimator/reduction.py b/estimator/reduction.py
@@ -390,6 +390,16 @@ def _short_vectors_sieve(self, beta, d, N=None, B=None, preprocess=True, sieve_d
             self.delta(sieve_dim) ** (sieve_dim - 1) * self.delta(beta) ** (1 - sieve_dim)
         )
 
+        # arbitrary choice
+        if c > 2**1000:
+            # set c = oo
+            return (
+                rho,
+                oo,
+                oo,
+                sieve_dim,
+            )
+
         return (
             rho,
             ceil(c) * self(beta, d),
@@ -892,8 +902,21 @@ def short_vectors(self, beta, d, N=None, preprocess=True, B=None, C=5.46, sieve_
         elif N is None:
             N = floor(2 ** (0.2075 * sieve_dim))  # pick something
 
-        c = N / floor(2 ** (0.2075 * sieve_dim))
-        sieve_cost = C * 2 ** (self.NN_AGPS[self.nn]["a"] * sieve_dim + self.NN_AGPS[self.nn]["b"])
+        c0 = RR(N)
+        c1 = RR(2 ** (0.2075 * sieve_dim))
+        c = c0 / floor(c1)
+        sieve_cost = C * 2 ** RR((self.NN_AGPS[self.nn]["a"] * sieve_dim + self.NN_AGPS[self.nn]["b"]))
+
+        # arbitrary choice
+        if c > 2**1000:
+            # set c = oo
+            return (
+                rho,
+                oo,
+                oo,
+                sieve_dim,
+            )
+
         return (
             rho,
             ceil(c) * (self(beta, d) + sieve_cost),
-Original file line number
+Diff line change
@@ Expand Up / @@ -591,6 +591,7 @@ def cost( @@
                 k_fft,
                 p,
             )
             rho, T_sample, _, beta_sieve = red_cost_model.short_vectors(
                 beta, N=N, d=k_lat + m, sieve_dim=beta_sieve
             )
@@ Expand Down @@