Cool New Stuff in Recent Sage Releases

Now that Sage 4.3 was released, maybe it’s time to point out some of the cool recent developments. Of course the following list is very very biased.

• libSingular functions interface. We now have some code which makes it possible to call any function available in Singular using the libSingular C wrapper directly, like this.

  sage: P = PolynomialRing(GF(127),10,'x')
  sage: I = Ideal(P.random_element() for _ in range(3000))
  sage: from sage.libs.singular.function import singular_function, lib
  sage: groebner = singular_function('groebner')
  sage: %time groebner(I)
  CPU times: user 0.07 s, sys: 0.00 s, total: 0.08 s
  Wall time: 0.08 s
  [1]

  For comparison, the Singular pexpect interface needs almost two seconds for the same task (due to string parsing on both ends, IPC, etc.)

  sage:%time groebner_basis()
  CPU times: user 0.96 s, sys: 0.24 s, total: 1.21 s
  Wall time: 1.92 s
  [1]

  Michael Brickenstein wrote a lot of this code, so three cheers to him!

• linear algebra over F_2 got better. For once, we implemented vectors over F_2 on top of M4RI matrices (cf. #7715), which makes them much faster. Furthermore, we call more dedicated M4RI functions now instead of the generic slow functions available for all fields (cf. #3684). Finally, asymptotically fast matrix factorisation got faster again. However, we still didn’t switch to this implementation as the default implementation because of the slow-down for sparse-ish matrices: use the algorithm=’pluq’ option to force the new implementation.
• PolyBoRi was updated to version 0.6.3 and the interface received some considerable update too during a visit to Kaiserslautern. Please, please, please report any regressions etc. either to me, to [sage-support] or to [polybori-discuss]. didn’t make it, cf. #7271
• Linear Programming is now available in Sage (though it requires to install at least one optional package). Still, this opens up quite a few possibilities (cf. Nathann Cohen’s tutorial).