Miscellaneous \(p\)-adic functions¶
\(p\)-adic functions from ell_rational_field.py, moved here to reduce crowding in that file.
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sage.schemes.elliptic_curves.padics.matrix_of_frobenius(self, p, prec=20, check=False, check_hypotheses=True, algorithm='auto')¶ Returns the matrix of Frobenius on the Monsky Washnitzer cohomology of the elliptic curve.
INPUT:
p- prime (= 5) for which \(E\) is good and ordinaryprec- (relative) \(p\)-adic precision for result (default 20)check- boolean (default: False), whether to perform a consistency check. This will slow down the computation by a constant factor 2. (The consistency check is to verify that its trace is correct to the specified precision. Otherwise, the trace is used to compute one column from the other one (possibly after a change of basis).)check_hypotheses- boolean, whether to check that this is a curve for which the \(p\)-adic sigma function makes sensealgorithm- one of “standard”, “sqrtp”, or “auto”. This selects which version of Kedlaya’s algorithm is used. The “standard” one is the one described in Kedlaya’s paper. The “sqrtp” one has better performance for large \(p\), but only works when \(p > 6N\) (\(N=\) prec). The “auto” option selects “sqrtp” whenever possible.Note that if the “sqrtp” algorithm is used, a consistency check will automatically be applied, regardless of the setting of the “check” flag.
OUTPUT: a matrix of \(p\)-adic number to precision
precSee also the documentation of padic_E2.
EXAMPLES:
sage: E = EllipticCurve('37a1') sage: E.matrix_of_frobenius(7) [ 2*7 + 4*7^2 + 5*7^4 + 6*7^5 + 6*7^6 + 7^8 + 4*7^9 + 3*7^10 + 2*7^11 + 5*7^12 + 4*7^14 + 7^16 + 2*7^17 + 3*7^18 + 4*7^19 + 3*7^20 + O(7^21) 2 + 3*7 + 6*7^2 + 7^3 + 3*7^4 + 5*7^5 + 3*7^7 + 7^8 + 3*7^9 + 6*7^13 + 7^14 + 7^16 + 5*7^17 + 4*7^18 + 7^19 + O(7^20)] [ 2*7 + 3*7^2 + 7^3 + 3*7^4 + 6*7^5 + 2*7^6 + 3*7^7 + 5*7^8 + 3*7^9 + 2*7^11 + 6*7^12 + 5*7^13 + 4*7^16 + 4*7^17 + 6*7^18 + 6*7^19 + 4*7^20 + O(7^21) 6 + 4*7 + 2*7^2 + 6*7^3 + 7^4 + 6*7^7 + 5*7^8 + 2*7^9 + 3*7^10 + 4*7^11 + 7^12 + 6*7^13 + 2*7^14 + 6*7^15 + 5*7^16 + 4*7^17 + 3*7^18 + 2*7^19 + O(7^20)] sage: M = E.matrix_of_frobenius(11,prec=3); M [ 9*11 + 9*11^3 + O(11^4) 10 + 11 + O(11^3)] [ 2*11 + 11^2 + O(11^4) 6 + 11 + 10*11^2 + O(11^3)] sage: M.det() 11 + O(11^4) sage: M.trace() 6 + 10*11 + 10*11^2 + O(11^3) sage: E.ap(11) -5
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sage.schemes.elliptic_curves.padics.padic_E2(self, p, prec=20, check=False, check_hypotheses=True, algorithm='auto')¶ Returns the value of the \(p\)-adic modular form \(E2\) for \((E, \omega)\) where \(\omega\) is the usual invariant differential \(dx/(2y + a_1 x + a_3)\).
INPUT:
p- prime (= 5) for which \(E\) is good and ordinaryprec- (relative) p-adic precision (= 1) for resultcheck- boolean, whether to perform a consistency check. This will slow down the computation by a constant factor 2. (The consistency check is to compute the whole matrix of frobenius on Monsky-Washnitzer cohomology, and verify that its trace is correct to the specified precision. Otherwise, the trace is used to compute one column from the other one (possibly after a change of basis).)check_hypotheses- boolean, whether to check that this is a curve for which the p-adic sigma function makes sensealgorithm- one of “standard”, “sqrtp”, or “auto”. This selects which version of Kedlaya’s algorithm is used. The “standard” one is the one described in Kedlaya’s paper. The “sqrtp” one has better performance for large \(p\), but only works when \(p > 6N\) (\(N=\) prec). The “auto” option selects “sqrtp” whenever possible.Note that if the “sqrtp” algorithm is used, a consistency check will automatically be applied, regardless of the setting of the “check” flag.
OUTPUT: p-adic number to precision prec
Note
If the discriminant of the curve has nonzero valuation at p, then the result will not be returned mod \(p^\text{prec}\), but it still will have prec digits of precision.
Todo
Once we have a better implementation of the “standard” algorithm, the algorithm selection strategy for “auto” needs to be revisited.
AUTHORS:
- David Harvey (2006-09-01): partly based on code written by Robert Bradshaw at the MSRI 2006 modular forms workshop
ACKNOWLEDGMENT: - discussion with Eyal Goren that led to the trace trick.
EXAMPLES: Here is the example discussed in the paper “Computation of p-adic Heights and Log Convergence” (Mazur, Stein, Tate):
sage: EllipticCurve([-1, 1/4]).padic_E2(5) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + 4*5^10 + 2*5^11 + 2*5^12 + 2*5^14 + 3*5^15 + 3*5^16 + 3*5^17 + 4*5^18 + 2*5^19 + O(5^20)
Let’s try to higher precision (this is the same answer the MAGMA implementation gives):
sage: EllipticCurve([-1, 1/4]).padic_E2(5, 100) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + 4*5^10 + 2*5^11 + 2*5^12 + 2*5^14 + 3*5^15 + 3*5^16 + 3*5^17 + 4*5^18 + 2*5^19 + 4*5^20 + 5^21 + 4*5^22 + 2*5^23 + 3*5^24 + 3*5^26 + 2*5^27 + 3*5^28 + 2*5^30 + 5^31 + 4*5^33 + 3*5^34 + 4*5^35 + 5^36 + 4*5^37 + 4*5^38 + 3*5^39 + 4*5^41 + 2*5^42 + 3*5^43 + 2*5^44 + 2*5^48 + 3*5^49 + 4*5^50 + 2*5^51 + 5^52 + 4*5^53 + 4*5^54 + 3*5^55 + 2*5^56 + 3*5^57 + 4*5^58 + 4*5^59 + 5^60 + 3*5^61 + 5^62 + 4*5^63 + 5^65 + 3*5^66 + 2*5^67 + 5^69 + 2*5^70 + 3*5^71 + 3*5^72 + 5^74 + 5^75 + 5^76 + 3*5^77 + 4*5^78 + 4*5^79 + 2*5^80 + 3*5^81 + 5^82 + 5^83 + 4*5^84 + 3*5^85 + 2*5^86 + 3*5^87 + 5^88 + 2*5^89 + 4*5^90 + 4*5^92 + 3*5^93 + 4*5^94 + 3*5^95 + 2*5^96 + 4*5^97 + 4*5^98 + 2*5^99 + O(5^100)
Check it works at low precision too:
sage: EllipticCurve([-1, 1/4]).padic_E2(5, 1) 2 + O(5) sage: EllipticCurve([-1, 1/4]).padic_E2(5, 2) 2 + 4*5 + O(5^2) sage: EllipticCurve([-1, 1/4]).padic_E2(5, 3) 2 + 4*5 + O(5^3)
TODO: With the old(-er), i.e., = sage-2.4 p-adics we got \(5 + O(5^2)\) as output, i.e., relative precision 1, but with the newer p-adics we get relative precision 0 and absolute precision 1.
sage: EllipticCurve([1, 1, 1, 1, 1]).padic_E2(5, 1) O(5)
Check it works for different models of the same curve (37a), even when the discriminant changes by a power of p (note that E2 depends on the differential too, which is why it gets scaled in some of the examples below):
sage: X1 = EllipticCurve([-1, 1/4]) sage: X1.j_invariant(), X1.discriminant() (110592/37, 37) sage: X1.padic_E2(5, 10) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + O(5^10)
sage: X2 = EllipticCurve([0, 0, 1, -1, 0]) sage: X2.j_invariant(), X2.discriminant() (110592/37, 37) sage: X2.padic_E2(5, 10) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + O(5^10)
sage: X3 = EllipticCurve([-1*(2**4), 1/4*(2**6)]) sage: X3.j_invariant(), X3.discriminant() / 2**12 (110592/37, 37) sage: 2**(-2) * X3.padic_E2(5, 10) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + O(5^10)
sage: X4 = EllipticCurve([-1*(7**4), 1/4*(7**6)]) sage: X4.j_invariant(), X4.discriminant() / 7**12 (110592/37, 37) sage: 7**(-2) * X4.padic_E2(5, 10) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + O(5^10)
sage: X5 = EllipticCurve([-1*(5**4), 1/4*(5**6)]) sage: X5.j_invariant(), X5.discriminant() / 5**12 (110592/37, 37) sage: 5**(-2) * X5.padic_E2(5, 10) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + O(5^10)
sage: X6 = EllipticCurve([-1/(5**4), 1/4/(5**6)]) sage: X6.j_invariant(), X6.discriminant() * 5**12 (110592/37, 37) sage: 5**2 * X6.padic_E2(5, 10) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + O(5^10)
Test check=True vs check=False:
sage: EllipticCurve([-1, 1/4]).padic_E2(5, 1, check=False) 2 + O(5) sage: EllipticCurve([-1, 1/4]).padic_E2(5, 1, check=True) 2 + O(5) sage: EllipticCurve([-1, 1/4]).padic_E2(5, 30, check=False) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + 4*5^10 + 2*5^11 + 2*5^12 + 2*5^14 + 3*5^15 + 3*5^16 + 3*5^17 + 4*5^18 + 2*5^19 + 4*5^20 + 5^21 + 4*5^22 + 2*5^23 + 3*5^24 + 3*5^26 + 2*5^27 + 3*5^28 + O(5^30) sage: EllipticCurve([-1, 1/4]).padic_E2(5, 30, check=True) 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + 5^8 + 3*5^9 + 4*5^10 + 2*5^11 + 2*5^12 + 2*5^14 + 3*5^15 + 3*5^16 + 3*5^17 + 4*5^18 + 2*5^19 + 4*5^20 + 5^21 + 4*5^22 + 2*5^23 + 3*5^24 + 3*5^26 + 2*5^27 + 3*5^28 + O(5^30)
Here’s one using the \(p^{1/2}\) algorithm:
sage: EllipticCurve([-1, 1/4]).padic_E2(3001, 3, algorithm="sqrtp") 1907 + 2819*3001 + 1124*3001^2 + O(3001^3)
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sage.schemes.elliptic_curves.padics.padic_height(self, p, prec=20, sigma=None, check_hypotheses=True)¶ Compute the cyclotomic p-adic height.
The equation of the curve must be minimal at \(p\).
INPUT:
p- prime = 5 for which the curve has semi-stable reductionprec- integer = 1, desired precision of resultsigma- precomputed value of sigma. If not supplied, this function will call padic_sigma to compute it.check_hypotheses- boolean, whether to check that this is a curve for which the p-adic height makes sense
OUTPUT: A function that accepts two parameters:
- a Q-rational point on the curve whose height should be computed
- optional boolean flag ‘check’: if False, it skips some input checking, and returns the p-adic height of that point to the desired precision.
- The normalization (sign and a factor 1/2 with respect to some other normalizations that appear in the literature) is chosen in such a way as to make the p-adic Birch Swinnerton-Dyer conjecture hold as stated in [Mazur-Tate-Teitelbaum].
AUTHORS:
- Jennifer Balakrishnan: original code developed at the 2006 MSRI graduate workshop on modular forms
- David Harvey (2006-09-13): integrated into Sage, optimised to speed up repeated evaluations of the returned height function, addressed some thorny precision questions
- David Harvey (2006-09-30): rewrote to use division polynomials for computing denominator of \(nP\).
- David Harvey (2007-02): cleaned up according to algorithms in “Efficient Computation of p-adic Heights”
- Chris Wuthrich (2007-05): added supersingular and multiplicative heights
EXAMPLES:
sage: E = EllipticCurve("37a") sage: P = E.gens()[0] sage: h = E.padic_height(5, 10) sage: h(P) 5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + O(5^10)
An anomalous case:
sage: h = E.padic_height(53, 10) sage: h(P) 26*53^-1 + 30 + 20*53 + 47*53^2 + 10*53^3 + 32*53^4 + 9*53^5 + 22*53^6 + 35*53^7 + 30*53^8 + 17*53^9 + O(53^10)
Boundary case:
sage: E.padic_height(5, 3)(P) 5 + 5^2 + O(5^3)
A case that works the division polynomial code a little harder:
sage: E.padic_height(5, 10)(5*P) 5^3 + 5^4 + 5^5 + 3*5^8 + 4*5^9 + O(5^10)
Check that answers agree over a range of precisions:
sage: max_prec = 30 # make sure we get past p^2 # long time sage: full = E.padic_height(5, max_prec)(P) # long time sage: for prec in range(1, max_prec): # long time ....: assert E.padic_height(5, prec)(P) == full # long time
A supersingular prime for a curve:
sage: E = EllipticCurve('37a') sage: E.is_supersingular(3) True sage: h = E.padic_height(3, 5) sage: h(E.gens()[0]) (3 + 3^3 + O(3^6), 2*3^2 + 3^3 + 3^4 + 3^5 + 2*3^6 + O(3^7)) sage: E.padic_regulator(5) 5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + 5^10 + 3*5^11 + 3*5^12 + 5^13 + 4*5^14 + 5^15 + 2*5^16 + 5^17 + 2*5^18 + 4*5^19 + O(5^20) sage: E.padic_regulator(3, 5) (3 + 2*3^2 + 3^3 + O(3^4), 3^2 + 2*3^3 + 3^4 + O(3^5))
A torsion point in both the good and supersingular cases:
sage: E = EllipticCurve('11a') sage: P = E.torsion_subgroup().gen(0).element(); P (5 : 5 : 1) sage: h = E.padic_height(19, 5) sage: h(P) 0 sage: h = E.padic_height(5, 5) sage: h(P) 0
The result is not dependent on the model for the curve:
sage: E = EllipticCurve([0,0,0,0,2^12*17]) sage: Em = E.minimal_model() sage: P = E.gens()[0] sage: Pm = Em.gens()[0] sage: h = E.padic_height(7) sage: hm = Em.padic_height(7) sage: h(P) == hm(Pm) True
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sage.schemes.elliptic_curves.padics.padic_height_pairing_matrix(self, p, prec=20, height=None, check_hypotheses=True)¶ Computes the cyclotomic \(p\)-adic height pairing matrix of this curve with respect to the basis self.gens() for the Mordell-Weil group for a given odd prime p of good ordinary reduction.
INPUT:
p- prime = 5prec- answer will be returned modulo \(p^{\mathrm{prec}}\)height- precomputed height function. If not supplied, this function will call padic_height to compute it.check_hypotheses- boolean, whether to check that this is a curve for which the p-adic height makes sense
OUTPUT: The p-adic cyclotomic height pairing matrix of this curve to the given precision.
Todo
remove restriction that curve must be in minimal Weierstrass form. This is currently required for E.gens().
AUTHORS:
- David Harvey, Liang Xiao, Robert Bradshaw, Jennifer Balakrishnan: original implementation at the 2006 MSRI graduate workshop on modular forms
- David Harvey (2006-09-13): cleaned up and integrated into Sage, removed some redundant height computations
EXAMPLES:
sage: E = EllipticCurve("37a") sage: E.padic_height_pairing_matrix(5, 10) [5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + O(5^10)]
A rank two example:
sage: e =EllipticCurve('389a') sage: e._set_gens([e(-1, 1), e(1,0)]) # avoid platform dependent gens sage: e.padic_height_pairing_matrix(5,10) [ 3*5 + 2*5^2 + 5^4 + 5^5 + 5^7 + 4*5^9 + O(5^10) 5 + 4*5^2 + 5^3 + 2*5^4 + 3*5^5 + 4*5^6 + 5^7 + 5^8 + 2*5^9 + O(5^10)] [5 + 4*5^2 + 5^3 + 2*5^4 + 3*5^5 + 4*5^6 + 5^7 + 5^8 + 2*5^9 + O(5^10) 4*5 + 2*5^4 + 3*5^6 + 4*5^7 + 4*5^8 + O(5^10)]
An anomalous rank 3 example:
sage: e = EllipticCurve("5077a") sage: e._set_gens([e(-1,3), e(2,0), e(4,6)]) sage: e.padic_height_pairing_matrix(5,4) [4 + 3*5 + 4*5^2 + 4*5^3 + O(5^4) 4 + 4*5^2 + 2*5^3 + O(5^4) 3*5 + 4*5^2 + 5^3 + O(5^4)] [ 4 + 4*5^2 + 2*5^3 + O(5^4) 3 + 4*5 + 3*5^2 + 5^3 + O(5^4) 2 + 4*5 + O(5^4)] [ 3*5 + 4*5^2 + 5^3 + O(5^4) 2 + 4*5 + O(5^4) 1 + 3*5 + 5^2 + 5^3 + O(5^4)]
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sage.schemes.elliptic_curves.padics.padic_height_via_multiply(self, p, prec=20, E2=None, check_hypotheses=True)¶ Computes the cyclotomic p-adic height.
The equation of the curve must be minimal at \(p\).
INPUT:
p- prime = 5 for which the curve has good ordinary reductionprec- integer = 2, desired precision of resultE2- precomputed value of E2. If not supplied, this function will call padic_E2 to compute it. The value supplied must be correct mod \(p^(prec-2)\) (or slightly higher in the anomalous case; see the code for details).check_hypotheses- boolean, whether to check that this is a curve for which the p-adic height makes sense
OUTPUT: A function that accepts two parameters:
- a Q-rational point on the curve whose height should be computed
- optional boolean flag ‘check’: if False, it skips some input checking, and returns the p-adic height of that point to the desired precision.
- The normalization (sign and a factor 1/2 with respect to some other normalizations that appear in the literature) is chosen in such a way as to make the p-adic Birch Swinnerton-Dyer conjecture hold as stated in [Mazur-Tate-Teitelbaum].
AUTHORS:
- David Harvey (2008-01): based on the padic_height() function, using the algorithm of”Computing p-adic heights via point multiplication”
EXAMPLES:
sage: E = EllipticCurve("37a") sage: P = E.gens()[0] sage: h = E.padic_height_via_multiply(5, 10) sage: h(P) 5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + O(5^10)
An anomalous case:
sage: h = E.padic_height_via_multiply(53, 10) sage: h(P) 26*53^-1 + 30 + 20*53 + 47*53^2 + 10*53^3 + 32*53^4 + 9*53^5 + 22*53^6 + 35*53^7 + 30*53^8 + 17*53^9 + O(53^10)
Supply the value of E2 manually:
sage: E2 = E.padic_E2(5, 8) sage: E2 2 + 4*5 + 2*5^3 + 5^4 + 3*5^5 + 2*5^6 + O(5^8) sage: h = E.padic_height_via_multiply(5, 10, E2=E2) sage: h(P) 5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + O(5^10)
Boundary case:
sage: E.padic_height_via_multiply(5, 3)(P) 5 + 5^2 + O(5^3)
Check that answers agree over a range of precisions:
sage: max_prec = 30 # make sure we get past p^2 # long time sage: full = E.padic_height(5, max_prec)(P) # long time sage: for prec in range(2, max_prec): # long time ....: assert E.padic_height_via_multiply(5, prec)(P) == full # long time
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sage.schemes.elliptic_curves.padics.padic_lseries¶ A decorator that creates a cached version of an instance method of a class.
Note
For proper behavior, the method must be a pure function (no side effects). Arguments to the method must be hashable or transformed into something hashable using
keyor they must definesage.structure.sage_object.SageObject._cache_key().EXAMPLES:
sage: class Foo(object): ....: @cached_method ....: def f(self, t, x=2): ....: print('computing') ....: return t**x sage: a = Foo()
The example shows that the actual computation takes place only once, and that the result is identical for equivalent input:
sage: res = a.f(3, 2); res computing 9 sage: a.f(t = 3, x = 2) is res True sage: a.f(3) is res True
Note, however, that the
CachedMethodis replaced by aCachedMethodCallerorCachedMethodCallerNoArgsas soon as it is bound to an instance or class:sage: P.<a,b,c,d> = QQ[] sage: I = P*[a,b] sage: type(I.__class__.gens) <type 'sage.misc.cachefunc.CachedMethodCallerNoArgs'>
So, you would hardly ever see an instance of this class alive.
The parameter
keycan be used to pass a function which creates a custom cache key for inputs. In the following example, this parameter is used to ignore thealgorithmkeyword for caching:sage: class A(object): ....: def _f_normalize(self, x, algorithm): return x ....: @cached_method(key=_f_normalize) ....: def f(self, x, algorithm='default'): return x sage: a = A() sage: a.f(1, algorithm="default") is a.f(1) is a.f(1, algorithm="algorithm") True
The parameter
do_picklecan be used to enable pickling of the cache. Usually the cache is not stored when pickling:sage: class A(object): ....: @cached_method ....: def f(self, x): return None sage: import __main__ sage: __main__.A = A sage: a = A() sage: a.f(1) sage: len(a.f.cache) 1 sage: b = loads(dumps(a)) sage: len(b.f.cache) 0
When
do_pickleis set, the pickle contains the contents of the cache:sage: class A(object): ....: @cached_method(do_pickle=True) ....: def f(self, x): return None sage: __main__.A = A sage: a = A() sage: a.f(1) sage: len(a.f.cache) 1 sage: b = loads(dumps(a)) sage: len(b.f.cache) 1
Cached methods can not be copied like usual methods, see trac ticket #12603. Copying them can lead to very surprising results:
sage: class A: ....: @cached_method ....: def f(self): ....: return 1 sage: class B: ....: g=A.f ....: def f(self): ....: return 2 sage: b=B() sage: b.f() 2 sage: b.g() 1 sage: b.f() 1
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sage.schemes.elliptic_curves.padics.padic_regulator(self, p, prec=20, height=None, check_hypotheses=True)¶ Computes the cyclotomic \(p\)-adic regulator of this curve.
INPUT:
p- prime = 5prec- answer will be returned modulo \(p^{\mathrm{prec}}\)height- precomputed height function. If not supplied, this function will call padic_height to compute it.check_hypotheses- boolean, whether to check that this is a curve for which the p-adic height makes sense
OUTPUT: The p-adic cyclotomic regulator of this curve, to the requested precision.
If the rank is 0, we output 1.
Todo
Remove restriction that curve must be in minimal Weierstrass form. This is currently required for E.gens().
AUTHORS:
- Liang Xiao: original implementation at the 2006 MSRI graduate workshop on modular forms
- David Harvey (2006-09-13): cleaned up and integrated into Sage, removed some redundant height computations
- Chris Wuthrich (2007-05-22): added multiplicative and supersingular cases
- David Harvey (2007-09-20): fixed some precision loss that was occurring
EXAMPLES:
sage: E = EllipticCurve("37a") sage: E.padic_regulator(5, 10) 5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + O(5^10)
An anomalous case:
sage: E.padic_regulator(53, 10) 26*53^-1 + 30 + 20*53 + 47*53^2 + 10*53^3 + 32*53^4 + 9*53^5 + 22*53^6 + 35*53^7 + 30*53^8 + O(53^9)
An anomalous case where the precision drops some:
sage: E = EllipticCurve("5077a") sage: E.padic_regulator(5, 10) 5 + 5^2 + 4*5^3 + 2*5^4 + 2*5^5 + 2*5^6 + 4*5^7 + 2*5^8 + 5^9 + O(5^10)
Check that answers agree over a range of precisions:
sage: max_prec = 30 # make sure we get past p^2 # long time sage: full = E.padic_regulator(5, max_prec) # long time sage: for prec in range(1, max_prec): # long time ....: assert E.padic_regulator(5, prec) == full # long time
A case where the generator belongs to the formal group already (trac ticket #3632):
sage: E = EllipticCurve([37,0]) sage: E.padic_regulator(5,10) 2*5^2 + 2*5^3 + 5^4 + 5^5 + 4*5^6 + 3*5^8 + 4*5^9 + O(5^10)
The result is not dependent on the model for the curve:
sage: E = EllipticCurve([0,0,0,0,2^12*17]) sage: Em = E.minimal_model() sage: E.padic_regulator(7) == Em.padic_regulator(7) True
Allow a Python int as input:
sage: E = EllipticCurve('37a') sage: E.padic_regulator(int(5)) 5 + 5^2 + 5^3 + 3*5^6 + 4*5^7 + 5^9 + 5^10 + 3*5^11 + 3*5^12 + 5^13 + 4*5^14 + 5^15 + 2*5^16 + 5^17 + 2*5^18 + 4*5^19 + O(5^20)
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sage.schemes.elliptic_curves.padics.padic_sigma(self, p, N=20, E2=None, check=False, check_hypotheses=True)¶ Computes the p-adic sigma function with respect to the standard invariant differential \(dx/(2y + a_1 x + a_3)\), as defined by Mazur and Tate, as a power series in the usual uniformiser \(t\) at the origin.
The equation of the curve must be minimal at \(p\).
INPUT:
p- prime = 5 for which the curve has good ordinary reductionN- integer = 1, indicates precision of result; see OUTPUT section for descriptionE2- precomputed value of E2. If not supplied, this function will call padic_E2 to compute it. The value supplied must be correct mod \(p^{N-2}\).check- boolean, whether to perform a consistency check (i.e. verify that the computed sigma satisfies the definingdifferential equation- note that this does NOT guarantee correctness of all the returned digits, but it comes pretty close :-))check_hypotheses- boolean, whether to check that this is a curve for which the p-adic sigma function makes sense
OUTPUT: A power series \(t + \cdots\) with coefficients in \(\ZZ_p\).
The output series will be truncated at \(O(t^{N+1})\), and the coefficient of \(t^n\) for \(n \geq 1\) will be correct to precision \(O(p^{N-n+1})\).
In practice this means the following. If \(t_0 = p^k u\), where \(u\) is a \(p\)-adic unit with at least \(N\) digits of precision, and \(k \geq 1\), then the returned series may be used to compute \(\sigma(t_0)\) correctly modulo \(p^{N+k}\) (i.e. with \(N\) correct \(p\)-adic digits).
ALGORITHM: Described in “Efficient Computation of p-adic Heights” (David Harvey), which is basically an optimised version of the algorithm from “p-adic Heights and Log Convergence” (Mazur, Stein, Tate).
Running time is soft-\(O(N^2 \log p)\), plus whatever time is necessary to compute \(E_2\).
AUTHORS:
- David Harvey (2006-09-12)
- David Harvey (2007-02): rewrote
EXAMPLES:
sage: EllipticCurve([-1, 1/4]).padic_sigma(5, 10) O(5^11) + (1 + O(5^10))*t + O(5^9)*t^2 + (3 + 2*5^2 + 3*5^3 + 3*5^6 + 4*5^7 + O(5^8))*t^3 + O(5^7)*t^4 + (2 + 4*5^2 + 4*5^3 + 5^4 + 5^5 + O(5^6))*t^5 + O(5^5)*t^6 + (2 + 2*5 + 5^2 + 4*5^3 + O(5^4))*t^7 + O(5^3)*t^8 + (1 + 2*5 + O(5^2))*t^9 + O(5)*t^10 + O(t^11)
Run it with a consistency check:
sage: EllipticCurve("37a").padic_sigma(5, 10, check=True) O(5^11) + (1 + O(5^10))*t + O(5^9)*t^2 + (3 + 2*5^2 + 3*5^3 + 3*5^6 + 4*5^7 + O(5^8))*t^3 + (3 + 2*5 + 2*5^2 + 2*5^3 + 2*5^4 + 2*5^5 + 2*5^6 + O(5^7))*t^4 + (2 + 4*5^2 + 4*5^3 + 5^4 + 5^5 + O(5^6))*t^5 + (2 + 3*5 + 5^4 + O(5^5))*t^6 + (4 + 3*5 + 2*5^2 + O(5^4))*t^7 + (2 + 3*5 + 2*5^2 + O(5^3))*t^8 + (4*5 + O(5^2))*t^9 + (1 + O(5))*t^10 + O(t^11)
Boundary cases:
sage: EllipticCurve([1, 1, 1, 1, 1]).padic_sigma(5, 1) (1 + O(5))*t + O(t^2) sage: EllipticCurve([1, 1, 1, 1, 1]).padic_sigma(5, 2) (1 + O(5^2))*t + (3 + O(5))*t^2 + O(t^3)
Supply your very own value of E2:
sage: X = EllipticCurve("37a") sage: my_E2 = X.padic_E2(5, 8) sage: my_E2 = my_E2 + 5**5 # oops!!! sage: X.padic_sigma(5, 10, E2=my_E2) O(5^11) + (1 + O(5^10))*t + O(5^9)*t^2 + (3 + 2*5^2 + 3*5^3 + 4*5^5 + 2*5^6 + 3*5^7 + O(5^8))*t^3 + (3 + 2*5 + 2*5^2 + 2*5^3 + 2*5^4 + 2*5^5 + 2*5^6 + O(5^7))*t^4 + (2 + 4*5^2 + 4*5^3 + 5^4 + 3*5^5 + O(5^6))*t^5 + (2 + 3*5 + 5^4 + O(5^5))*t^6 + (4 + 3*5 + 2*5^2 + O(5^4))*t^7 + (2 + 3*5 + 2*5^2 + O(5^3))*t^8 + (4*5 + O(5^2))*t^9 + (1 + O(5))*t^10 + O(t^11)
Check that sigma is “weight 1”.
sage: f = EllipticCurve([-1, 3]).padic_sigma(5, 10) sage: g = EllipticCurve([-1*(2**4), 3*(2**6)]).padic_sigma(5, 10) sage: t = f.parent().gen() sage: f(2*t)/2 (1 + O(5^10))*t + (4 + 3*5 + 3*5^2 + 3*5^3 + 4*5^4 + 4*5^5 + 3*5^6 + 5^7 + O(5^8))*t^3 + (3 + 3*5^2 + 5^4 + 2*5^5 + O(5^6))*t^5 + (4 + 5 + 3*5^3 + O(5^4))*t^7 + (4 + 2*5 + O(5^2))*t^9 + O(5)*t^10 + O(t^11) sage: g O(5^11) + (1 + O(5^10))*t + O(5^9)*t^2 + (4 + 3*5 + 3*5^2 + 3*5^3 + 4*5^4 + 4*5^5 + 3*5^6 + 5^7 + O(5^8))*t^3 + O(5^7)*t^4 + (3 + 3*5^2 + 5^4 + 2*5^5 + O(5^6))*t^5 + O(5^5)*t^6 + (4 + 5 + 3*5^3 + O(5^4))*t^7 + O(5^3)*t^8 + (4 + 2*5 + O(5^2))*t^9 + O(5)*t^10 + O(t^11) sage: f(2*t)/2 -g O(t^11)
Test that it returns consistent results over a range of precision:
sage: max_N = 30 # get up to at least p^2 # long time sage: E = EllipticCurve([1, 1, 1, 1, 1]) # long time sage: p = 5 # long time sage: E2 = E.padic_E2(5, max_N) # long time sage: max_sigma = E.padic_sigma(p, max_N, E2=E2) # long time sage: for N in range(3, max_N): # long time ....: sigma = E.padic_sigma(p, N, E2=E2) # long time ....: assert sigma == max_sigma
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sage.schemes.elliptic_curves.padics.padic_sigma_truncated(self, p, N=20, lamb=0, E2=None, check_hypotheses=True)¶ Computes the p-adic sigma function with respect to the standard invariant differential \(dx/(2y + a_1 x + a_3)\), as defined by Mazur and Tate, as a power series in the usual uniformiser \(t\) at the origin.
The equation of the curve must be minimal at \(p\).
This function differs from padic_sigma() in the precision profile of the returned power series; see OUTPUT below.
INPUT:
p- prime = 5 for which the curve has good ordinary reductionN- integer = 2, indicates precision of result; see OUTPUT section for descriptionlamb- integer = 0, see OUTPUT section for descriptionE2- precomputed value of E2. If not supplied, this function will call padic_E2 to compute it. The value supplied must be correct mod \(p^{N-2}\).check_hypotheses- boolean, whether to check that this is a curve for which the p-adic sigma function makes sense
OUTPUT: A power series \(t + \cdots\) with coefficients in \(\ZZ_p\).
The coefficient of \(t^j\) for \(j \geq 1\) will be correct to precision \(O(p^{N - 2 + (3 - j)(lamb + 1)})\).
ALGORITHM: Described in “Efficient Computation of p-adic Heights” (David Harvey, to appear in LMS JCM), which is basically an optimised version of the algorithm from “p-adic Heights and Log Convergence” (Mazur, Stein, Tate), and “Computing p-adic heights via point multiplication” (David Harvey, still draft form).
Running time is soft-\(O(N^2 \lambda^{-1} \log p)\), plus whatever time is necessary to compute \(E_2\).
AUTHOR:
- David Harvey (2008-01): wrote based on previous padic_sigma function
EXAMPLES:
sage: E = EllipticCurve([-1, 1/4]) sage: E.padic_sigma_truncated(5, 10) O(5^11) + (1 + O(5^10))*t + O(5^9)*t^2 + (3 + 2*5^2 + 3*5^3 + 3*5^6 + 4*5^7 + O(5^8))*t^3 + O(5^7)*t^4 + (2 + 4*5^2 + 4*5^3 + 5^4 + 5^5 + O(5^6))*t^5 + O(5^5)*t^6 + (2 + 2*5 + 5^2 + 4*5^3 + O(5^4))*t^7 + O(5^3)*t^8 + (1 + 2*5 + O(5^2))*t^9 + O(5)*t^10 + O(t^11)
Note the precision of the \(t^3\) coefficient depends only on \(N\), not on lamb:
sage: E.padic_sigma_truncated(5, 10, lamb=2) O(5^17) + (1 + O(5^14))*t + O(5^11)*t^2 + (3 + 2*5^2 + 3*5^3 + 3*5^6 + 4*5^7 + O(5^8))*t^3 + O(5^5)*t^4 + (2 + O(5^2))*t^5 + O(t^6)
Compare against plain padic_sigma() function over a dense range of N and lamb
sage: E = EllipticCurve([1, 2, 3, 4, 7]) # long time sage: E2 = E.padic_E2(5, 50) # long time sage: for N in range(2, 10): # long time ....: for lamb in range(10): # long time ....: correct = E.padic_sigma(5, N + 3*lamb, E2=E2) # long time ....: compare = E.padic_sigma_truncated(5, N=N, lamb=lamb, E2=E2) # long time ....: assert compare == correct # long time