Fix CI

Related: sagemath/sage#35473

estimator/lwe_dual.py

@@ -378,7 +378,7 @@ def __call__(
             >>> LWE.dual(params)
             rop: ≈2^103.4, mem: ≈2^63.9, m: 904, β: 251, d: 1928, ↻: 1, tag: dual
             >>> dual_hybrid(params)
-            rop: ≈2^92.1, mem: ≈2^78.2, m: 716, β: 170, d: 1464, ↻: 1989, ζ: 276, h1: 8, tag: dual_hybrid
+            rop: ≈2^91.6, mem: ≈2^77.2, m: 711, β: 168, d: 1456, ↻: ≈2^11.2, ζ: 279, h1: 8, tag: dual_hybrid
             >>> dual_hybrid(params, mitm_optimization=True)
             rop: ≈2^98.2, mem: ≈2^78.6, m: 728, k: 292, ↻: ≈2^18.7, β: 180, d: 1267, ζ: 485, h1: 17, tag: ...
 
@@ -399,7 +399,7 @@ def __call__(
             rop: ≈2^160.7, mem: ≈2^156.8, m: 1473, k: 25, ↻: 1, β: 456, d: 2472, ζ: 25, tag: dual_mitm_hybrid
 
             >>> dual_hybrid(schemes.NTRUHPS2048509Enc)
-            rop: ≈2^131.7, mem: ≈2^128.5, m: 436, β: 358, d: 906, ↻: 1, ζ: 38, tag: dual_hybrid
+            rop: ≈2^136.2, mem: ≈2^127.8, m: 434, β: 356, d: 902, ↻: 35, ζ: 40, h1: 19, tag: dual_hybrid
 
             >>> LWE.dual(schemes.CHHS_4096_67)
             rop: ≈2^206.9, mem: ≈2^137.5, m: ≈2^11.8, β: 616, d: 7779, ↻: 1, tag: dual

estimator/lwe_guess.py

@@ -7,7 +7,7 @@
 
 """
 
-from sage.all import binomial, ceil, e, exp, floor, log, oo, pi, round, RR, sqrt, ZZ
+from sage.all import binomial, ceil, e, exp, floor, log, oo, pi, QQ, round, RR, sqrt, ZZ
 
 from .conf import mitm_opt
 from .cost import Cost
@@ -133,7 +133,7 @@ def __call__(self, params, log_level=5, **kwds):
         Compare::
 
             >>> LWE.primal_hybrid(schemes.Kyber512.updated(Xs=ND.SparseTernary(512, 16)))
-            rop: ≈2^85.8, red: ≈2^84.8, svp: ≈2^84.8, β: 105, η: 2, ζ: 366, |S|: ≈2^85.1, d: 315, prob: ≈2^-23.4, ...
+            rop: ≈2^136.4, red: ≈2^136.3, svp: ≈2^131.1, β: 251, η: 2, ζ: 203, |S|: ≈2^152.6, d: 673, ...
 
         """
         params = LWEParameters.normalize(params)
@@ -241,7 +241,7 @@ def mitm_analytical(self, params: LWEParameters, success_probability=0.99):
 
         if params.Xs.is_sparse:
             h = params.Xs.hamming_weight
-            split_h = round(h * k / n)
+            split_h = QQ(h * k / n).round('down')
             success_probability_ = (
                 binomial(k, split_h) * binomial(n - k, h - split_h) / binomial(n, h)
             )
@@ -283,7 +283,7 @@ def cost(
 
             # we assume the hamming weight to be distributed evenly across the two parts
             # if not we can rerandomize on the coordinates and try again -> repeat
-            split_h = round(h * k / n)
+            split_h = QQ(h * k / n).round('down')
             size_tab = RR((sd_rng - 1) ** split_h * binomial(k, split_h))
             size_sea = RR((sd_rng - 1) ** (h - split_h) * binomial(n - k, h - split_h))
             success_probability_ = (

estimator/lwe_primal.py

@@ -557,13 +557,13 @@ def __call__(
             rop: ≈2^97.3, red: ≈2^96.3, svp: ≈2^96.3, β: 170, η: 142, ζ: 203, |S|: ≈2^16.3, d: 574, ...
 
             >>> LWE.primal_hybrid(schemes.Kyber512.updated(Xs=ND.SparseTernary(512, 16)), mitm = False, babai = True)
-            rop: ≈2^88.7, red: ≈2^88.0, svp: ≈2^87.2, β: 98, η: 2, ζ: 323, |S|: ≈2^39.7, d: 346, ...
+            rop: ≈2^102.4, red: ≈2^102.2, svp: ≈2^99.8, β: 216, η: 2, ζ: 203, |S|: ≈2^69.7, d: 632, prob: 0.002, ...
 
             >>> LWE.primal_hybrid(schemes.Kyber512.updated(Xs=ND.SparseTernary(512, 16)), mitm = True, babai = False)
-            rop: ≈2^74.1, red: ≈2^73.7, svp: ≈2^71.9, β: 104, η: 16, ζ: 320, |S|: ≈2^77.1, d: 359, ...
+            rop: ≈2^90.6, red: ≈2^89.7, svp: ≈2^89.6, β: 173, η: 138, ζ: 203, |S|: ≈2^36.4, d: 578, prob: 0.002, ...
 
             >>> LWE.primal_hybrid(schemes.Kyber512.updated(Xs=ND.SparseTernary(512, 16)), mitm = True, babai = True)
-            rop: ≈2^85.8, red: ≈2^84.8, svp: ≈2^84.8, β: 105, η: 2, ζ: 366, |S|: ≈2^85.1, d: 315, ...
+            rop: ≈2^136.4, red: ≈2^136.3, svp: ≈2^131.1, β: 251, η: 2, ζ: 203, |S|: ≈2^152.6, d: 673, ...
 
         TESTS:
 

estimator/nd.py

@@ -3,7 +3,7 @@
 from copy import copy
 from dataclasses import dataclass
 
-from sage.all import binomial, ceil, exp, log, oo, parent, pi, RealField, RR, sqrt
+from sage.all import binomial, ceil, exp, log, oo, parent, pi, QQ, RealField, RR, sqrt
 
 
 def stddevf(sigma):
@@ -253,7 +253,7 @@ def support_size(self, fraction=1.0):
             )
 
         b = 2 * t * sigmaf(self.stddev) + 1
-        return (2 * b + 1)**n
+        return RR(2.0 * b + 1)**n
 
 
 def DiscreteGaussianAlpha(alpha, q, mean=0, n=None):
@@ -338,10 +338,10 @@ def __hash__(self):
         EXAMPLE::
 
             >>> from estimator import *
-            >>> hash(ND.Uniform(-10, 10)) == hash(("Uniform", 10, 10, None))
+            >>> hash(ND.Uniform(-10, 10)) == hash(("Uniform", (-10, 10), None))
             True
         """
-        return hash(("Uniform", self.a, self.b, self.n))
+        return hash(("Uniform", self.bounds, self.n))
 
     def support_size(self, fraction=1.0):
         """
@@ -435,9 +435,9 @@ def split_balanced(self, new_n, new_hw=None):
         n, hw = len(self), self.hamming_weight
         if new_hw is None:
             # Most likely split has same density: new_hw / new_n = hw / n.
-            new_hw = int(round(hw * new_n / n))
+            new_hw = int(QQ(hw * new_n / n).round('down'))
 
-        new_p = int(round((new_hw * self.p) / hw))
+        new_p = int((QQ(new_hw * self.p) / hw).round('down'))
         new_m = new_hw - new_p
         return (
             SparseTernary(new_n, new_p, new_m),

estimator/ntru_primal.py

@@ -369,15 +369,15 @@ def __call__(
 
             >>> NTRU.primal_hybrid(schemes.NTRUHPS2048509Enc.updated(Xs=ND.SparseTernary(508,16)),
             ... mitm = False, babai = True)
-            rop: ≈2^88.0, red: ≈2^87.4, svp: ≈2^86.4, β: 98, η: 2, ζ: 318, |S|: ≈2^39.6, d: 328, prob: ≈2^-27.9, ...
+            rop: ≈2^101.6, red: ≈2^101.5, svp: ≈2^96.5, β: 205, η: 2, ζ: 197, |S|: ≈2^64.1, d: 596, prob: ≈2^-11.7, ...
 
             >>> NTRU.primal_hybrid(schemes.NTRUHPS2048509Enc.updated(Xs=ND.SparseTernary(508,16)),
             ... mitm = True, babai = False)
-            rop: ≈2^80.1, red: ≈2^79.7, svp: ≈2^78.3, β: 170, η: 22, ζ: 254, |S|: ≈2^103.7, d: 495, prob: 0.708, ...
+            rop: ≈2^89.6, red: ≈2^88.9, svp: ≈2^88.2, β: 172, η: 135, ζ: 197, |S|: ≈2^36.1, d: 555, prob: 0.003, ...
 
             >>> NTRU.primal_hybrid(schemes.NTRUHPS2048509Enc.updated(Xs=ND.SparseTernary(508,16)),
             ... mitm = True, babai = True)
-            rop: ≈2^85.1, red: ≈2^84.1, svp: ≈2^84.0, β: 105, η: 2, ζ: 363, |S|: ≈2^85.0, d: 294, prob: ≈2^-22.9, ...
+            rop: ≈2^134.5, red: ≈2^134.4, svp: ≈2^129.2, β: 248, η: 2, ζ: 197, |S|: ≈2^151.2, d: 645, ...
 
         TESTS:
 

estimator/reduction.py

@@ -515,8 +515,8 @@ def __call__(self, beta, d, B=None):
 
             >>> from sage.all import var, find_fit
             >>> dim = [100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250]
-            >>> nodes = [39.0, 44.0, 49.0, 54.0, 60.0, 66.0, 72.0, 78.0, 84.0, 96.0, \
-                         99.0, 105.0, 111.0, 120.0, 127.0, 134.0]
+            >>> nodes = [39.0, 44.0, 49.0, 54.0, 60.0, 66.0, 72.0, 78.0, 84.0, 96.0 ]
+            >>> nodes += [ 99.0, 105.0, 111.0, 120.0, 127.0, 134.0]  # couldn't use \\ breaks stuff
             >>> times = [c + log(200,2).n() for c in nodes]
             >>> T = list(zip(dim, nodes))
             >>> var("a,b,c,beta")

estimator/util.py

@@ -443,8 +443,9 @@ def batch_estimate(params, algorithm, jobs=1, log_level=0, catch_exceptions=True
 
         >>> from estimator import LWE
         >>> from estimator.schemes import Kyber512
-        >>> _ = batch_estimate(Kyber512, [LWE.primal_usvp, LWE.primal_bdd])
-        >>> _ = batch_estimate(Kyber512, [LWE.primal_usvp, LWE.primal_bdd], jobs=2)
+        >>> from estimator.util import batch_estimate
+        >>> _ = batch_estimate(Kyber512, [LWE.primal_usvp, LWE.primal_bdd], log_level=1)
+        >>> _ = batch_estimate(Kyber512, [LWE.primal_usvp, LWE.primal_bdd], jobs=2, log_level=1)
 
     """