= The Road to LLL in SAGE = [[http://perso.ens-lyon.fr/damien.stehle/english.html|Damien Stehle]]'s fpLLL code is wrapped at [[http://trac.sagemath.org/sage_trac/ticket/723|#723]] or [[http://sage.math.washington.edu/home/malb/fplll.patch|fplll.patch]] respectively. For some problems, this gives quite good performance already: {{{#!python sage: B = MatrixSpace(IntegerRing(), 50, 51)(0) sage: for i in range(50): B[i,0] = ZZ.random_element(2**10000) ....: sage: for i in range(50): B[i,i+1] = 1 ....: sage: time C = B.LLL('fpLLL:fast') CPU times: user 9.54 s, sys: 0.00 s, total: 9.54 s Wall time: 9.56 sage: C.is_LLL_reduced() True sage: BM = B._magma_() sage: time CM = BM.LLL() CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s Wall time: 15.20 sage: time magma.eval("CM := LLL(%s:Fast:=1)"%BM.name()) CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s Wall time: 11.68 }}} However, the main strength of MAGMA's LLL is that it chooses a reasonably 'good' LLL implementation automatically. This is described in Damien Stehle's [[http://perso.ens-lyon.fr/damien.stehle/FPLLL_SURVEY.html|paper]] and timings can be found in some of his [[http://magma.maths.usyd.edu.au/Magma2006/talks/stehle.pdf|slides]]. For those examples SAGE seems to perform quite poorly. == LLL Heuristic == To develop a simple heuristic how to choose a LLL implementation, we thought about using the following benchmarking examples. All these examples are generated using Stehle's {{{generate.c}}}} code and follow his slides for dimensions and bitsizes. * 1000 dimensional matrices filled uniformly random with integers of 10, 100, or 1000 bits respectively. * matrices as they occur for the Knapsack problem with (dimension,bitsize) pairs of (10, 100000), (100,10000), (150,5000) * matrices as they appear for solving simultaneous Diophantine equations of (dim,bits) pairs (3, 128), (12, 10000), (76, 5000) * Ajtai (d, bits) (10, 7), (2, 13), (3, 11) * particular bad matrices with entries sized at 64, 128, and 500 bits. * NTRU (dim, bits, q) (10,100,12), (100,100,35), (5,1000,75)