L-series

Project Leader

William

Group Members

William, Cassie, Amy, Lola

Getting Going on Development on *your* laptop (not *.sagenb.org)

1. Download this file: http://sage.math.washington.edu/home/wstein/days/33/eulerprod.py

2. Type in the notebook: attach /exact/path/to/eulerprod.py

3. Note that elliptic curves over Q(sqrt(5)) won't work if you don't have psage installed.

Project Description

For L-series lovers: Getting the doctest coverage to 100% on this might be a good project:

• http://code.google.com/p/purplesage/source/browse/psage/lseries/eulerprod.py

See this worksheet for an example.

That may discover "issues" (bugs), which I would likely have to fix, but would also be fun because one gets to come up with lots of creative examples of L-series all over the place. Also, the top of that file has a todo list for new features to implement -- most would be bad projects, but one which would be good would be to make it so the Lseries object can use Lcalc (Rubinstein's program) to compute L-series instead of Dokchitser. This would be a good project, because it would mainly involve thinking about the annoying mathematics involved in going between normalizing L-series with the center of the critical strip at 1/2 versus not doing that. Also, it is all pure Python, so easier to get going.

Anyway, I'd say 1 could be a good project for people who know the basics of L-series, but want to get a much more concrete feel for them. In fact, instead of just trying to get coverage to 100%, writing a *tutorial* for computing with L-series using that package would be really nice. E.g., one could walk through how to find missing information, create new L-series classes, etc.

Specific Concrete Projects

• Move eulerprod.py into Sage itself. This would at least involve:
  1. Clean up the imports at the top of the file.
  2. Do something with: "from psage.ellcurve.lseries.helper import extend_multiplicatively_generic"; it is the main dependency. It's only a page of code.
  3. Graceful failure when
• Doctest coverage to 100%
• Fix bugs that are found when doctesting.
• Use rubinstein _lcalc (see line 1116)

• Math question: given that you have a_p for Norm(p) <= B, how many bits of precision do we get? Basically, invert the function L.number_of_coefficients(prec=50)

• Write an L-functions tutorial: http://sagemath.org/doc/thematic_tutorials This does not have to be long. It has to start somewhere. Small and focused first is fine!

• Speed of elliptic curves over number fields (smalljac).
• Make it so we use exactly one GP session for *all* of the Dokchitser L-functions
• Symmetric powers (and modular degree -- see trac 9758)
• Triple product L-functions: Gross-Kudla, Zhang, etc -- see the code in triple_prod/triple.py
• Make it so we use exactly one GP session for *all* of the Dokchitser L-functions
• Tensor products
• Genus 2 curves, via smalljac and genus2reduction
• Fast L-series of elliptic curves over number fields (not just sqrt(5)), via smalljac
• Inverse of number_of_coefficients function.

Work in progress

L-series tutorial: http://wiki.sagemath.org/days33/lfunction/tutorial

Triple product L-function code: http://wiki.sagemath.org/days33/lfunction/tripleproduct