performance improvements

alleviates malb#75 somewhat

estimator/lwe_primal.py

@@ -22,7 +22,6 @@
 from .conf import red_cost_model as red_cost_model_default
 from .conf import red_shape_model as red_shape_model_default
 from .conf import red_simulator as red_simulator_default
-from fpylll.util import gaussian_heuristic
 
 
 class PrimalUSVP:
@@ -84,7 +83,6 @@ def cost_gsa(
         red_cost_model=red_cost_model_default,
         log_level=None,
     ):
-
         delta = deltaf(beta)
         xi = PrimalUSVP._xi_factor(params.Xs, params.Xe)
         m = min(2 * ceil(sqrt(params.n * log(params.q) / log(delta))), m)
@@ -262,9 +260,21 @@ def svp_dimension(cls, r, D):
         :param r: squared Gram-Schmidt norms
 
         """
+        from math import lgamma, log, exp, pi
+
+        def ball_log_vol(n):
+            return (n / 2.0) * log(pi) - lgamma(n / 2.0 + 1)
+
+        def gaussian_heuristic_log_input(r):
+            n = len(list(r))
+            log_vol = sum(r)
+            log_gh = 1.0 / n * (log_vol - 2 * ball_log_vol(n))
+            return exp(log_gh)
+
         d = len(r)
+        r = [log(x) for x in r]
         for i, _ in enumerate(r):
-            if gaussian_heuristic(r[i:]) < D.stddev**2 * (d - i):
+            if gaussian_heuristic_log_input(r[i:]) < D.stddev**2 * (d - i):
                 return ZZ(d - (i - 1))
         return ZZ(2)
 

estimator/prob.py

@@ -40,9 +40,9 @@ def babai(r, norm):
     Babai probability following [EPRINT:Wun16]_.
 
     """
-    R = [RR(sqrt(t) / (2 * norm)) for t in r]
+    denom = float(2 * norm) ** 2
     T = RealDistribution("beta", ((len(r) - 1) / 2, 1.0 / 2))
-    probs = [1 - T.cum_distribution_function(1 - s ** 2) for s in R]
+    probs = [1 - T.cum_distribution_function(1 - r_ / denom) for r_ in r]
     return prod(probs)
 
 
@@ -103,7 +103,7 @@ def amplify(target_success_probability, success_probability, majority=False):
     try:
         if majority:
             eps = success_probability / 2
-            return ceil(2 * log(2 - 2 * target_success_probability) / log(1 - 4 * eps ** 2))
+            return ceil(2 * log(2 - 2 * target_success_probability) / log(1 - 4 * eps**2))
         else:
             # target_success_probability = 1 - (1-success_probability)^trials
             return ceil(log(1 - target_success_probability) / log(1 - success_probability))
@@ -121,7 +121,7 @@ def amplify_sigma(target_advantage, sigma, q):
 
     """
     try:
-        sigma = sum(sigma_ ** 2 for sigma_ in sigma).sqrt()
+        sigma = sum(sigma_**2 for sigma_ in sigma).sqrt()
     except TypeError:
         pass
     advantage = float(exp(-float(pi) * (float(sigma / q) ** 2)))