Coleman Integration in Larger Characteristic
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1 Coleman Integration in Larger Characteristic ANTS XIII University of Wisconsin, Madison Alex J. Best 17/7/2018 Boston University
2 The big picture Kedlaya Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
3 The big picture CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
4 The big picture BaBrKe Coleman integration CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
5 The big picture Ba-BaTu BaBrKe Coleman integration CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
6 The big picture Ba-BaTu BaBrKe Coleman integration Ha CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Õ( p) Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
7 The big picture Ba-BaTu BaBrKe Coleman integration Mi-ArBeCoMaTr Ha CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Õ( p) Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
8 The big picture Be Ba-BaTu BaBrKe Coleman integration Mi-ArBeCoMaTr Ha CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Õ( p) Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
9 The big picture Be Ba-BaTu BaBrKe Coleman integration Mi-ArBeCoMaTr Ha CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Õ( p) Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
10 The big picture Be Ba-BaTu BaBrKe Coleman integration Mi-ArBeCoMaTr Ha CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Õ( p) Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more...
11 The big picture Be Ba-BaTu BaBrKe Coleman integration Mi-ArBeCoMaTr Ha CaDeVe-Go-GaGu-Sh-Tu More curves Kedlaya Õ( p) Arul, Balakrishnan, Best, Bradshaw, Castryk, Costa, Denef, Gaudry, Gurel, Harvey, Kedlaya, Magner, Minzlaff, Shieh, Triantafillou, Tuitman, Vercauteren, and more... There is (at least) one dimension missing: Small p!
12 Motivation (Explicit) Coleman integration is a central tool in (non-abelian) Chabauty and can be applied to find: heights, torsion points and regulators also, but, algorithms lag behind the related ones for zeta functions.
13 Motivation (Explicit) Coleman integration is a central tool in (non-abelian) Chabauty and can be applied to find: heights, torsion points and regulators also, but, algorithms lag behind the related ones for zeta functions. Longer term goals: Adapt descendants of Kedlaya s algorithm to compute (iterated) Coleman integrals, e.g.:
14 Motivation (Explicit) Coleman integration is a central tool in (non-abelian) Chabauty and can be applied to find: heights, torsion points and regulators also, but, algorithms lag behind the related ones for zeta functions. Longer term goals: Adapt descendants of Kedlaya s algorithm to compute (iterated) Coleman integrals, e.g.: Larger characteristic (Harvey)
15 Motivation (Explicit) Coleman integration is a central tool in (non-abelian) Chabauty and can be applied to find: heights, torsion points and regulators also, but, algorithms lag behind the related ones for zeta functions. Longer term goals: Adapt descendants of Kedlaya s algorithm to compute (iterated) Coleman integrals, e.g.: Larger characteristic (Harvey) Average polynomial time (Harvey)
16 Motivation (Explicit) Coleman integration is a central tool in (non-abelian) Chabauty and can be applied to find: heights, torsion points and regulators also, but, algorithms lag behind the related ones for zeta functions. Longer term goals: Adapt descendants of Kedlaya s algorithm to compute (iterated) Coleman integrals, e.g.: Larger characteristic (Harvey) Average polynomial time (Harvey) Effectiver average polynomial time (Harvey-Sutherland)
17 Motivation (Explicit) Coleman integration is a central tool in (non-abelian) Chabauty and can be applied to find: heights, torsion points and regulators also, but, algorithms lag behind the related ones for zeta functions. Longer term goals: Adapt descendants of Kedlaya s algorithm to compute (iterated) Coleman integrals, e.g.: Larger characteristic (Harvey) Average polynomial time (Harvey) Effectiver average polynomial time (Harvey-Sutherland) Applications to rational points, combining congruence information for many primes, 1-step (Mordell-Weil) sieving.
18 Coleman integration Throughout we take X /Z p a genus g odd degree hyperelliptic curve, and p an odd prime. We pick a lift of the Frobenius map, φ : X X, and write A (resp. A loc (X )) for overconvergent (resp. locally analytic) functions on X.
19 Coleman integration Throughout we take X /Z p a genus g odd degree hyperelliptic curve, and p an odd prime. We pick a lift of the Frobenius map, φ : X X, and write A (resp. A loc (X )) for overconvergent (resp. locally analytic) functions on X. Theorem (Coleman) There is a Q p -linear map x b : Ω1 Q A p A loc (X ) for which: x d = id: Ω 1 A Q p Ω 1 loc FTC b x b x b x φ ω = φ ω d = id: A A loc b Frobenius equivariance
20 Reduction to reduction Balakrishnan-Bradshaw-Kedlaya reduce the problem of computing all Coleman integrals of basis differentials ω i of HdR 1 (X ) between X and a point x X (Q p ), to: 1. Finding tiny integrals between nearby points, 2. Writing φ ω i df i = j a ijω j and evaluating the primitive f i for a point P near x, for each i.
21 Reduction to reduction Balakrishnan-Bradshaw-Kedlaya reduce the problem of computing all Coleman integrals of basis differentials ω i of HdR 1 (X ) between X and a point x X (Q p ), to: 1. Finding tiny integrals between nearby points, 2. Writing φ ω i df i = j a ijω j and evaluating the primitive f i for a point P near x, for each i. Applying φ to the basis x i dx/2y for i = 0,..., 2g 1 gives φ ω i N 1 j=0 (2g+1)j r=0 B j,r x p(i+r+1) 1 p(2j+1)+1 dx y 2y (mod p N ) B j,r Z p are in terms of coefficients of the curve and binomial coefficients.
22 Kedlaya s algorithm Theorem (Kedlaya) The action of φ on HMW 1 (X ) (which determines the zeta function of X ) can be computed in time Õ(p).
23 Kedlaya s algorithm Theorem (Kedlaya) The action of φ on HMW 1 (X ) (which determines the zeta function of X ) can be computed in time Õ(p). Theorem (Harvey) If p > (2g + 1)(2N 1) the action of φ on H 1 MW (X ) can be computed in time Õ( p).
24 Kedlaya s algorithm Theorem (Kedlaya) The action of φ on HMW 1 (X ) (which determines the zeta function of X ) can be computed in time Õ(p). Theorem (Harvey) If p > (2g + 1)(2N 1) the action of φ on HMW 1 (X ) can be computed in time Õ( p). The problem solved here is almost the same: determining a ij s.t. φ ω i j a ij ω j image(d).
25 Primitive technology Revised problem Computing f along with ω df when reducing degree. For vanilla Kedlaya this is easy, the reduction procedure is transparent, whenever we subtract dg to reduce, add g onto f. For faster variants, this is not so simple!
26 The reduction process Harvey uses horizontal and vertical reductions to find the action of Frobenius on cohomology,
27 The reduction process Harvey uses horizontal and vertical reductions to find the action of Frobenius on cohomology, abstractly we have: Spaces of differentials W t, indexed by degree, each of dimension 2g. Goal Reduce all differentials from W t to a cohomologous one in W 0, write in terms of fixed basis of W 0.
28 The reduction process Harvey uses horizontal and vertical reductions to find the action of Frobenius on cohomology, abstractly we have: Spaces of differentials W t, indexed by degree, each of dimension 2g. Goal Reduce all differentials from W t to a cohomologous one in W 0, write in terms of fixed basis of W 0. Relations in the de Rham cohomology linear maps R(t): W t W t 1 t, with R(t)ω ω.
29 The reduction process Harvey uses horizontal and vertical reductions to find the action of Frobenius on cohomology, abstractly we have: Spaces of differentials W t, indexed by degree, each of dimension 2g. Goal Reduce all differentials from W t to a cohomologous one in W 0, write in terms of fixed basis of W 0. Relations in the de Rham cohomology linear maps R(t): W t W t 1 t, with R(t)ω ω. Want to evaluate W t ω R(1)R(2) R(t 1)R(t)ω W 0
30 O( p) Key fact Entries of R(t) are fractions of linear functions of t, with Z p coefficients; work of Bostan-Gaudry-Schost (& Harvey) = products can be interpolated R(a, b) = R(a + 1) R(b) R(a t, b + t) This is what gives a Õ( p) algorithm.
31 O( p) Key fact Entries of R(t) are fractions of linear functions of t, with Z p coefficients; work of Bostan-Gaudry-Schost (& Harvey) = products can be interpolated R(a, b) = R(a + 1) R(b) R(a t, b + t) This is what gives a Õ( p) algorithm. We also want an evaluation of the primitive f ω for which ω df ω = R(t)ω. We can keep this extra data throughout the recurrence as f ω is linear in ω.
32 O( p) Key fact Entries of R(t) are fractions of linear functions of t, with Z p coefficients; work of Bostan-Gaudry-Schost (& Harvey) = products can be interpolated R(a, b) = R(a + 1) R(b) R(a t, b + t) This is what gives a Õ( p) algorithm. We also want an evaluation of the primitive f ω for which ω df ω = R(t)ω. We can keep this extra data throughout the recurrence as f ω is linear in ω. Vital remark We must use evaluations of primitives here, instead of trying to compute f as a power series.
33 A problem and a solution Stumbling block This is no longer linear in the index t! You cannot apply BGS to evaluate this recurrence faster.
34 A problem and a solution Stumbling block This is no longer linear in the index t! You cannot apply BGS to evaluate this recurrence faster. Horner to the rescue! Instead of computing a series N i=0 a ix i by computing sequentially ( N ) a i x i we can instead compute i=t t=n,n 1,...,0 (( ((a N )x + a N 1 )x + )x + a 0 ) from the inside to the out. This is an iterated composition of linear functions, each of which is linear in the index t.
35 Explicit recurrence In matrix form we augment the (numerators of) the reduction matrices: y 2t dx/2y x 2g 1 y 2t dx/2y f (P) y 2(t 1) dx/2y (2t 1)r 0,0 + 2s 0,0 (2t 1)r 2g 1,0 + 2s 2g 1, x 2g 1 y 2(t 1) dx/2y (2t 1)r 0,2g 1 + 2s 0,2g 1 (2t 1)r 2g 1,2g 1 + 2s 2g 1,2g 1 f (P) S 0 (x) S 2g 1 (x) y 2 D V (t) so that we keep in memory a vector v W t Q p which gives the evaluation at the end.
36 Many integrals simultaneously We may wish to do this with multiple points in several residue disks. Instead of repeating the whole procedure (repeating computing the Frobenius matrix), augment with many points. y 2t dx/2y x 2g 1 y 2t dx/2y f (P1) f (PL) y 2(t 1) dx/y (2t 1)r 0,0 + 2s 0,0 (2t 1)r 2g 1,0 + 2s 2g 1, x 2g 1 y 2(t 1) dx/y (2t 1)r 0,2g 1 + 2s 0,2g 1 (2t 1)r 2g 1,2g 1 + 2s 2g 1,2g 1 f (P1) S 0(x(P 1)) S 2g 1(x(P 1)) y 2 (P 1)D V (t) f (PL) S 0(x(P L)) S 2g 1(x(P L)) 0 y(p L) 2 D V (t)
37 Many integrals simultaneously We may wish to do this with multiple points in several residue disks. Instead of repeating the whole procedure (repeating computing the Frobenius matrix), augment with many points. y 2t dx/2y x 2g 1 y 2t dx/2y f (P1) f (PL) y 2(t 1) dx/y (2t 1)r 0,0 + 2s 0,0 (2t 1)r 2g 1,0 + 2s 2g 1, x 2g 1 y 2(t 1) dx/y (2t 1)r 0,2g 1 + 2s 0,2g 1 (2t 1)r 2g 1,2g 1 + 2s 2g 1,2g 1 f (P1) S 0(x(P 1)) S 2g 1(x(P 1)) y 2 (P 1)D V (t) f (PL) S 0(x(P L)) S 2g 1(x(P L)) 0 y(p L) 2 D V (t) Note This matrix and its iterates have the same fixed form, when running BGS don t try and interpolate entries that are always 0 better run time.
38 Thanks for listening! Questions/comments?
39 Some amusing factoids Core algorithm is potentially more general? Evaluation of primitives is integration, but maybe of a different type? It is faster to evaluate the power series this way than to evaluate the power series you get from vanilla Kedlaya, even when multiple points are needed computing the power series is no use.
Coleman Integration in Larger Characteristic
Coleman Integration in Larger Characteristic ANTS XIII University of Wisconsin, Madison Alex J. Best 17/7/2018 Boston University The big picture Be Ba-BaTu BaBrKe Coleman integration Mi-ArBeCoMaTr Ha CaDeVe-Go-GaGu-Sh-Tu
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