Modular polynomials for elliptic curves#
For a positive integer \(\ell\), the classical modular polynomial \(\Phi_\ell \in \ZZ[X,Y]\) is characterized by the property that its zero set is exactly the set of pairs of \(j\)-invariants connected by a cyclic \(\ell\)-isogeny.
AUTHORS:
Lorenz Panny (2023)
- sage.schemes.elliptic_curves.mod_poly.classical_modular_polynomial(l, j=None)[source]#
Return the classical modular polynomial \(\Phi_\ell\), either as a “generic” bivariate polynomial over \(\ZZ\), or as an “instantiated” modular polynomial where one variable has been replaced by the given \(j\)-invariant.
Generic polynomials are cached up to a certain size of \(\ell\), which significantly accelerates subsequent invocations with the same \(\ell\). The default bound is \(\ell \leq 100\), which can be adjusted using
classical_modular_polynomial.set_cache_bound()
with a different value. Beware that modular polynomials are very big objects and the amount of memory consumed by the cache will grow rapidly when the bound is set to a large value.INPUT:
l
– positive integer.j
– eitherNone
or a ring element:if
None
is given, the original modular polynomial is returned as an element of \(\ZZ[X,Y]\)if a ring element \(j \in R\) is given, the evaluation \(\Phi_\ell(j,Y)\) is returned as an element of the univariate polynomial ring \(R[Y]\)
ALGORITHMS:
The Kohel database
ClassicalModularPolynomialDatabase
EXAMPLES:
sage: classical_modular_polynomial(2) -X^2*Y^2 + X^3 + 1488*X^2*Y + 1488*X*Y^2 + Y^3 - 162000*X^2 + 40773375*X*Y - 162000*Y^2 + 8748000000*X + 8748000000*Y - 157464000000000 sage: j = Mod(1728, 419) sage: classical_modular_polynomial(3, j) Y^4 + 230*Y^3 + 84*Y^2 + 118*Y + 329
>>> from sage.all import * >>> classical_modular_polynomial(Integer(2)) -X^2*Y^2 + X^3 + 1488*X^2*Y + 1488*X*Y^2 + Y^3 - 162000*X^2 + 40773375*X*Y - 162000*Y^2 + 8748000000*X + 8748000000*Y - 157464000000000 >>> j = Mod(Integer(1728), Integer(419)) >>> classical_modular_polynomial(Integer(3), j) Y^4 + 230*Y^3 + 84*Y^2 + 118*Y + 329
Increasing the cache size can be useful for repeated invocations:
sage: %timeit classical_modular_polynomial(101) # not tested 6.11 s ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) sage: %timeit classical_modular_polynomial(101, GF(65537).random_element()) # not tested 5.43 s ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) sage: classical_modular_polynomial.set_cache_bound(150) # not tested sage: %timeit classical_modular_polynomial(101) # not tested The slowest run took 10.35 times longer than the fastest. This could mean that an intermediate result is being cached. 1.84 µs ± 1.84 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) sage: %timeit classical_modular_polynomial(101, GF(65537).random_element()) # not tested 59.8 ms ± 29.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
>>> from sage.all import * >>> %timeit classical_modular_polynomial(Integer(101)) # not tested 6.11 s ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) >>> %timeit classical_modular_polynomial(Integer(101), GF(Integer(65537)).random_element()) # not tested 5.43 s ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) >>> classical_modular_polynomial.set_cache_bound(Integer(150)) # not tested >>> %timeit classical_modular_polynomial(Integer(101)) # not tested The slowest run took 10.35 times longer than the fastest. This could mean that an intermediate result is being cached. 1.84 µs ± 1.84 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) >>> %timeit classical_modular_polynomial(Integer(101), GF(Integer(65537)).random_element()) # not tested 59.8 ms ± 29.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)