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| = Sage 3.1.2 Release Tour = Sage 3.1.2 was released on XXX, 2008. For the official, comprehensive release notes, see the HISTORY.txt file that comes with the release. For the latest changes see sage-3.1.2.txt. == Doctest Coverage Hits 60% == * Mike Hansen wrote doctests for almost all pexpect interfaces, which will ensure greater stability across the board. == Hidden Markov Models == * William Stein wrote Cython bindings for the GHMM C library for computing with Hidden Markov Models, which are a statistical tool that is important in machine learning, natural language processing, bioinformatics, and other areas. GHMM is also now included standard in Sage. == Notebook Bugs == * Many bugs introduced in 3.1.1 were fixed by Mike Hansen and Timothy Clemans. * A new testing procedure was implemented, hopefully preventing regressions like in 3.1.1 in the future. == New Structures for Partition Refinement == Robert Miller * Hypergraphs (i.e. incidence structures) -- this includes simplicial complexes and block designs * Matrices -- the automorphism group of a matrix is the set of column permutations which leave the (unordered) set of rows unchanged == Improved Dense Linear Algebra over GF(2) == * M4RI (http://m4ri.sagemath.org) was updated to the newest upstream release which * provides much improved performance for multiplication (see [http://m4ri.sagemath.org/performance.html M4RI's "performance" page]), * provides improved performance for elimination, * contains several build and bugfixes. * hashs and matrix pickling was much improved (Martin Albrecht) '''Before''' {{{ sage: A = random_matrix(GF(2),10000,10000) sage: A.set_immutable() sage: %time _ = hash(A) CPU times: user 3.96 s, sys: 0.62 s, total: 4.58 s sage: A = random_matrix(GF(2),2000,2000) sage: %time _ = loads(dumps(A)) CPU times: user 4.00 s, sys: 0.07 s, total: 4.07 s }}} '''After''' {{{ sage: A = random_matrix(GF(2),10000,10000) sage: A.set_immutable() sage: %time _ = hash(A) CPU times: user 0.02 s, sys: 0.00 s, total: 0.02 s sage: A = random_matrix(GF(2),2000,2000) sage: %time _ = loads(dumps(A)) CPU times: user 1.35 s, sys: 0.01 s, total: 1.37 s }}} * dense matrices over $\mathbb{F}_2$ can now be written to/read from 1-bit PNG images (Martin Albrecht) == New PolyBoRi Version (0.5) and Improved Interface == * PolyBoRi was upgraded from 0.3 to 0.5rc (Tim Abbott, Michael Abshoff, Martin Albrecht) * {{{mq.SR}}} now returns PolyBoRi equation systems if asked to * support for boolean polynomial interpolation was added '''Example''' First we create a random-ish boolean polynomial. {{{ sage: B.<a,b,c,d,e,f> = BooleanPolynomialRing(6) sage: f = a*b*c*e + a*d*e + a*f + b + c + e + f + 1 }}} Now we find interpolation points mapping to zero and to one. {{{ sage: zeros = set([(1, 0, 1, 0, 0, 0), (1, 0, 0, 0, 1, 0), \ (0, 0, 1, 1, 1, 1), (1, 0, 1, 1, 1, 1), \ (0, 0, 0, 0, 1, 0), (0, 1, 1, 1, 1, 0), \ (1, 1, 0, 0, 0, 1), (1, 1, 0, 1, 0, 1)]) sage: ones = set([(0, 0, 0, 0, 0, 0), (1, 0, 1, 0, 1, 0), \ (0, 0, 0, 1, 1, 1), (1, 0, 0, 1, 0, 1), \ (0, 0, 0, 0, 1, 1), (0, 1, 1, 0, 1, 1), \ (0, 1, 1, 1, 1, 1), (1, 1, 1, 0, 1, 0)]) sage: [f(*p) for p in zeros] [0, 0, 0, 0, 0, 0, 0, 0] sage: [f(*p) for p in ones] [1, 1, 1, 1, 1, 1, 1, 1] }}} Finally, we find the lexicographically smallest interpolation polynomial using PolyBoRi . {{{ sage: g = B.interpolation_polynomial(zeros, ones); g b*f + c + d*f + d + e*f + e + 1 }}} {{{ sage: [g(*p) for p in zeros] [0, 0, 0, 0, 0, 0, 0, 0] sage: [g(*p) for p in ones] [1, 1, 1, 1, 1, 1, 1, 1] }}} == QEPCAD Interface == == Developer's Handbook == * John H Palmieri rewrote/rearranged large parts of the 'Programming Guide' (now 'Developer's Guide') which should make getting started easier for new developers. == Improved 64-bit OSX Support == == Fast Numerical Integration == == GAP Meataxe Interface == * In the {{{module matrix_group}}}, the method {{{module_composition_factors}}} interfaces with GAP's [http://www.gap-system.org/Manuals/doc/htm/ref/CHAP067.htm Meataxe] implementation. This will return decomposition information for a G-module, for any matrix group G over a finite field. == Better SymPy Integration == * Ondrej Cetrik implemented more conversions from Sage native types to SymPy native types. == Faster Determinants of Dense Matrices over Multivariate Polynomial Rings == * Martin Albrecht modified Sage to use Singular '''Before''' {{{ sage: P.<x,y> = QQ[] sage: C = random_matrix(P,8,8) sage: %time d = C.det() CPU times: user 2.78 s, sys: 0.02 s, total: 2.80 s }}} '''After''' {{{ sage: P.<x,y> = QQ[] sage: C = random_matrix(P,8,8) sage: %time d = C.det() CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s }}} * a discussion about this issue can be found on [http://groups.google.com/group/sage-devel/browse_thread/thread/7aa1bd1e945ff372/ sage-devel] == Real Number Inputs Improved == * Robert Bradshaw improved real number input so that the precision is preserved better: '''Before''' {{{ sage: RealField(256)(1.2) 1.199999999999999955591079014993738383054733276367187500000000000000000000000 }}} '''After''' {{{ sage: RealField(256)(1.2) 1.200000000000000000000000000000000000000000000000000000000000000000000000000 }}} |
