1. padics
Lesson: SVN + HG only mix together well if you're only creating new files
Status:
 Code in place, still unexposed in SAGE, working on more doctests.
 Lazy padics currently under development
 Extension fields are next
Goals: Make (i)(iii) production ready
Problems: (a) Extensions may be extremely slow and may require FiniteField, PolyQuotientRing optimization to fix.
People: David Roe (DR), David Harvey (DH), Craig Citro (CC), Genya Zaytman (YZ)
Timeline:
 Done by AWS: DR/DH/CC/YZ
 Done by AWS: DR
 Unramified by AWS: CC/DR
 All of us will be at AWS
2. Symbolic manipulation
Problems:
 Hard to choose when to simplify
 Still dependant on Maxima for some things  will this limit us in the future? (consensus here seems to be probably not)
Status:
 Plotting still untouched, esp wrt speed (we think)
 We have equality working! Also, formal inequalities ready to go (in principle)
Goals:
 More doctests, production ready code
 Explore Mathematica, Maple and Maxima, add functionality. Go through a calculus textbook and make sure we can do all the problems.
 More user feedback on interface and features
 Rouche's Algorithm
 Turn function objects/symbolic expressions into C/SageX code.
 Make (v) part of a SAGEwide system
People: Bobby M, Nathan, Soroosh Yazdam, Robert Bradshaw
Timeline:
 Before 3/1, Initial Public Release
 Get a fair amount of this done by 3/8
 March/April (Nathan and Mathematica worksheets)
 Soroosh wants to do this when the code base is ready, research other programs by 4/1
 April/May
 Later
3. Low level optimization of SAGE infrastructure
Goals:
 Optimize the code that SageX generates: make Pythonesque syntax fast in lieu of ugly, nasty, Python/C API calls (to avoid coredumps, it's hard to read, easy to write bugs and you feel guilty when you write it)
 Speed up SAGE integer construction by changing to handwritten C in SageX
 Integer pool?
Problem: In (ii) Tying handwritten C into SageX in a few specific places when SageX is nice but slow. We don't necessarily want to make this easy, and don't know exactly how we should change SageX in order to do this.
Status:
 Fairly far along in indexing into lists, but goal (i) is more long term. People can ask why is something slow, look at the generated C code and see if there's a better way to do it. Make a wiki page.
 Have code written, trying to get it in place. Understand what's going on. See Problem.
 Still thinking about it.
People: Robert Bradshaw, Martin, David Harvey (sick of this project now)
Timeline:
 List indexing: 48 hours (Robert). Make a wiki page: more as it comes along.
 Figure out problem and implement solution by 3/1
 After 2.
 Look at schedule for Google summer of code. Python Software Foundation wiki  describe this project, by 4/1.
4. 3D plot embedded Java applet
Goal: Make it work and easy to install, even if your name isn't Robert.
Problem: Notebook server chokes on huge files. Maybe rewrite notebook backend in twisted.
People: Tom Boothby
Status: Has twisted server working, but it's not tied in to the notebook client.
5. VMWare
Goal: Sage in a VMWare Player. Compressed VMWare Image should fit on a CD.
People: Marcus, William
Timeline: By AWS
6. Linear Algebra

Sparse 
Dense 
GF(2) 
(A) 
(B) 
Numerical 
(C) 
(D) 
Exact 
(E) 
(F) 
A) M4R (Method of Four Russians)
Remark: Do as dense up to certain size, ie A contained in B up to some size
Goal: Get M4R into SAGE
Goal: Get M4R into SAGE.
Example current timings:

SAGE 
Linbox 
Bard Gaussian Elim 
Bard M4R 
Magma 
6000x6000 

15s 
4s 
1.7s 
2.23s 
3000x3000 
20s 
5s 
0.420s 
0.2s 
0.32s 
Status:
Goal: (i) Get M4R into SAGE
 (ii) Figure out where to draw the line
People: Martin
Timeline: Once B is done
Goal: Get M4R into SAGE.
Example current timings:

SAGE 
Linbox 
Bard Gaussian Elim 
Bard M4R 
Magma 
6000x6000 

15s 
4s 
1.7s 
2.23s 
3000x3000 
20s 
5s 
0.420s 
0.2s 
0.32s 
People: Martin
Status: Now understand M4R
Timeline: Soon. 3/15
C) Waiting on Suvrit Sra, otherwise clueless. Could do using Suvrit's (or other) libraries if this stalls.
Goal: Implement something to compare to Suvrit
People: Josh
Status: Have a little bit, but it's not very good.
Timeline: Incremental
D) Matrixvector now BLAS (like everyone)
Using numpy for everything else seems to be the best plan
Goal: Convert
People: Josh
Timeline: 2 weeks
E) (Sparse integer vectors) and (Sparse rational vectors): working, not optimized. Both in progress
 (Sparse integer matrices) and (Sparse rational matrices): close
Status: Some benchmarks
Goal:
 Rational sparse echelon via multimodular working
 Optimize
Timeline:
 72 hours
 By 3/10
Remark: Still behind Magma because of mod p, but linbox may help.
F) Status: Lots of benchmarks. Much faster than two weeks ago. Linbox integration coming along nicely.
Remark: Magma may not be producing provably correct answers
Goal:
 Better prime selection in multimodular: maybe 4x speedup
 More specialized matrix classes
 Optimize matrixvector and vectormatrix for specialized classes
 A lot of optimization (motivated by modular symbols)
 Raw methods, much of this will have to be in specialized matrix classes. Maybe not do this (William is not currently convinced)? Look into this.
 Examples
Timeline:
 By 3/10
 Long term
7. Examples
They autotest, done by today  Rishi
Plan: If you want to put tests in a docstring that the user doesn't see