Lecture 2: Gram-Schmidt Vectors and the LLL Algorithm

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1 NYU, Fall 2016 Lattces Mn Course Lecture 2: Gram-Schmdt Vectors and the LLL Algorthm Lecturer: Noah Stephens-Davdowtz 2.1 The Shortest Vector Problem In our last lecture, we consdered short solutons to modular equatons. We showed how to fnd such solutons n two dmensons, and we proved that relatvely short solutons always exst. We then generalzed to arbtrary lattces, L := {a 1 b a n b n : a Z} for arbtrary lnearly ndependent bass vectors b 1,..., b n. We defned λ 1 (L) to be the length of the shortest non-zero vector (n the Eucldean norm) and showed Mnkowsk s bound, λ 1 (L) n det(l) 1/n. But, we are computer scentsts. So, we re not only nterested n the exstence of short lattce vectors; we want algorthms that fnd them. We therefore defne the followng computatonal problem, whch s the central computatonal problem on lattces. Defnton 2.1. For any approxmaton factor γ = γ(n) 1, the γ-shortest Vector Problem (γ-svp) s defned as follows. The nput s a bass for a lattce L R n. The goal s to output a non-zero lattce vector x L \ {0} wth x γ λ 1 (L). SVP s extremely well-studed for many dfferent approxmaton factors. It turns out to be NP-hard (under randomzed reductons) for any constant approxmaton factor, and t s hard for approxmaton factors up to n c/ log log n for some constant c > 0 under reasonable complexty-theoretc assumptons [Mc01, Kho05, HR12]. Even for polynomal approxmaton factors γ = n C for any constant C, the fastest known algorthm for γ-svp has runnng tme that s exponental n the dmenson! In ths lecture, we wll show the celebrated LLL algorthm, whch effcently solves γ-svp for γ = 2 O(n). Ths mght not sound very mpressve, but t s undoubtedly the most mportant result n the study of computatonal lattce problems. 2.2 The Gram-Schmdt Orthogonalzaton In order to present the LLL algorthm, we need to ntroduce the Gram-Schmdt orthogonalzaton (whch wll turn out to be useful throughout ths course). We start wth two natural defntons. Gven any set S R n, we wrte S for the subspace of vectors perpendcular to all vectors n S. For any subspace W R n and x R n, we wrte π W (x) to be the orthogonal projecton of x onto W. In other words, π W (x) s the unque vector n W such that x π W (x) s n W. Formally, π W (x) := x v, x v / v 2, where the v are any orthogonal bass of W, but the pcture should be clearer than the formal defnton: 2-1

2 W x π W (x) x π W (x) Gven a bass (b 1,..., b n ), we defne ts Gram-Schmdt orthogonalzaton (or just the Gram-Schmdt vectors) ( b 1,..., b n ) such that b := π {b1,...,b 1 } (b ).. In other words, b s the component of b that s orthogonal to the precedng vectors n the bass. Note that the Gram-Schmdt vectors are mutually orthogonal wth b 1 = b 1. 1 And, notce that the Gram-Schmdt vectors depend crucally on the order of the bass vectors. Clam 2.2. For any lattce L R n wth bass (b 1,..., b n ), λ 1 (L) mn b. Proof. Let x = a b L be a shortest non-zero vector. Snce x s non-zero, t has a non-zero coordnate. Let j be maxmal such that a j 0. Then, snce a = 0 for all > j, x π {b1,...,b j 1 } (x) = a j b j b j mn b, where we have used the fact that a j s a non-zero nteger. It s often convenent to rotate our coordnates so that the Gram-Schmdt vectors are algned wth the standard bass. In these coordnates, our bass (b 1,..., b n ) becomes upper trangular, b 1 µ 1,2 b 1 µ 1,3 b 1 µ 1,n b 1 0 b 2 µ 2,3 b 2 µ 2,n b b 3 µ 3,n b 3. (2.1) b n 1 Some authors choose to work wth the Gram-Schmdt orthonormalzaton, whch n our notaton s ( b 1/ b 1,..., b n/ b n ). I.e., they normalze so that the Gram-Schmdt vectors have unt length. We ntentonally do not do ths, as the lengths of the Gram-Schmdt vectors wll play a key role n our study of bases. 2-2

3 By conventon, we wrte the off-dagonal entres as µ,j b. I.e., for < j, we defne µ,j := b, b j b 2. (2.2) In partcular, note that we can trvally modfy the bass to make µ,j 1/2 by smply subtractng an nteger multple of b from b j. We say that a bass s sze-reduced f µ,j 1/2 for all < j. The above representaton mmedately shows that det(l) = b. Combnng ths wth Mnkowsk s theorem from the prevous lecture and Clam 2.2, we have mn b λ 1 (L) n ( 1/n b ). (2.3) One farly natural noton of a good bass for a lattce s one that yelds relatvely tght bounds n Eq. (2.3). In partcular, such a bass mmedately mples a good estmate for λ 1 (L). (And, wth a bt more work, one can show that t yelds a short vector.) Clearly, Eq. (2.3) becomes tghter f the Gram-Schmdt vectors have relatvely smlar lengths. So, ths suggests that we mght prefer bases whose Gram-Schmdt vectors all have roughly the same length. We also have the bound mn b λ 1 (L) b 1, (2.4) snce b 1 = b 1 s a non-zero lattce vector. In other words, the frst vector n our bass wll be a good soluton to SVP f the lengths of the Gram-Schmdt vectors decay slowly. I.e., max b 1 / b bounds the rato of b 1 to λ 1 (L). Ths decay rate of the Gram-Schmdt vectors, max b 1 / b (and the smlar expresson max j b j / b ) turns out to be a very good way to judge a bass, and t leads naturally to the LLL algorthm. (We wll later see that the performance of Baba s nearestplane algorthm also depends on ths rate.) In partcular, as we wll see, the LLL algorthm works by attemptng to control ths decay. 2.3 The LLL Algorthm LLL-reduced bases The dea behnd the LLL algorthm, due to Lenstra, Lenstra, and Lovász [LLL82], s qute smple and elegant. Instead of lookng at the global decay of the Gram-Schmdt vectors b 1 / b, we consder the local decay b / b +1. We try to mprove ths local decay by swappng adjacent vectors n the bass. I.e., we swtch the places of the vectors b and b +1 f b / b +1 would become sgnfcantly lower as a result. By only consderng adjacent pars of vectors, the LLL algorthm effectvely reduces the problem of fndng a good lattce bass to a smple two-dmensonal lattce problem. So, let s look at the two-dmensonal case, wth a bass (b 1, b 2 ) and correspondng Gram- Schmdt vectors ( b 1, b 2 ). Let b 1 and b 2 be the Gram-Schmdt vectors of the swapped 2-3

4 bass (b 2, b 1 ). (I.e., b 1 = b 2 and b 2 = π b 2 (b 1 ).) We would lke to know when the decay of the frst bass s no worse than the decay of the second bass. I.e., when do we have b 1 / b 2 b 1 / b 2? Note that det(l) = b 1 b 2 = b 1 b 2. After rearrangng a bt, we see that b 1 / b 2 b 1 / b 2 f and only f b 1 b 1. Recallng that b 1 = b 1 and b 1 = b 2, we see that swappng wll fal to mprove the decay f and only f b 1 b 2. That s a very smple condton! To extend ths to hgher dmensons, t s convenent to wrte ths smple condton usng the µ,j notaton from Eq. (2.2). In ths notaton, b 2 2 = b µ 2 1,2 b 1 2. So, we see that a swap wll fal to mprove the decay of the Gram-Schmdt vectors f and only f b 1 2 b µ 2 1,2 b 1 2. Lenstra, Lenstra, and Lovász extended ths condton to an n-dmensonal bass (b 1,..., b n ) and added a relaxaton parameter δ (1/4, 1] to obtan the Lovász condton: (It s common to take δ = 3/4.) δ b 2 b µ 2,+1 b 2. (2.5) Defnton 2.3. For δ (1/4, 1], we say that a bass (b 1,..., b n ) s δ-lll reduced (or just δ-lll) f (1) t s sze-reduced (.e., µ,j 1/2 for all < j); and (2) t satsfes the Lovász condton n Eq. (2.5). The next theorem shows that a δ-lll bass yelds a relatvely short vector. (It also shows why we take δ > 1/4.) Theorem 2.4. If a bass (b 1,..., b n ) s δ-lll reduced for some δ (1/4, 1], then b 1 (δ 1/4) n/2 λ 1 (L). Proof. By Eq. (2.5), we have δ b 2 b µ 2,+1 b 2 b b 2, where we have used the fact that the bass s sze-reduced. After rearrangng, we have b 2 (δ 1/4) 1 b +1 2, and applyng ths repeatedly, we see that b 1 2 (δ 1/4) n b 2 for all. The result then follows from Clam The actual algorthm Of course, Theorem 2.4 s only nterestng f we can fnd LLL-reduced bases. A very smple algorthm actually suffces! Here s the full LLL algorthm, whch takes as nput a bass (b 1,..., b n ): 2-4

5 1. Sze-reduce the bass. 2. If the bass s δ-lll-reduced, halt and output the bass. 3. Otherwse, pck any ndex such that the Lovasz condton n Eq. (2.5) s not satsfed, and swtch the postons of b and b Repeat. Theorem 2.5. The LLL algorthm termnates n tme poly(s)/ log(1/δ), where S s the bt length of the nput bass. Proof. We assume for smplcty that the nput bass s ntegral. (If the nput s ratonal, then ths s wthout loss of generalty, as we can smply scale the bass by the least common multple of the denomnators n the nput. If there are rratonal entres n the nput bass, then one needs to frst choose a representaton of rratonal numbers.) We wll not bother to prove that the numbers encountered by the algorthm reman bounded by 2 poly(s). (I.e., we wll only bound the arthmetc complexty of the algorthm.) Consder the rather strange potental functon Notce that Φ(b 1,..., b n ) := Φ(b 1,..., b n ) = n b 1 b 2 b. (2.6) =1 n b j n j+1 = det(l) n j=1 n b j 1 j gets larger as the later Gram-Schmdt vectors get shorter. So, we can thnk of Φ as some sort of measure of the decay of the Gram-Schmdt vectors. We observe some basc propertes about Φ. If the b are ntegral, then Φ(b 1,..., b n ) s a product of determnants of ntegral lattces, so that Φ(b 1,..., b n ) 1. 2 Furthermore, Φ(b 1,..., b n ) 2 poly(s) for the nput bass. Fnally, note that Φ only depends on the Gram-Schmdt vectors, so that sze-reducton does not affect Φ. It therefore suffces to show that Φ(B )/Φ(B) poly(δ), where B := (b 1,..., b +1, b,..., b n ) s the bass that results from applyng a sngle swappng step of the LLL algorthm to B := (b 1,..., b n ). Let the Gram-Schmdt vectors of B be ( b 1,..., b n). Notce that for j, the lattce generated by the frst j vectors of B s the same as the lattce generated by the frst j vectors of B. So, the determnants of these prefxes must be the same. I.e., b 1 b j = b 1 b j. It follows from Eq. (2.6) that Φ(B ) Φ(B) = b 1 b b 1 b = b b. 2 Here, we are glossng over the subtlety of what we mean by the determnant of a lattce spanned by a bass B = (b 1,..., b d ) for d < n. Formally, we can take the square root of the determnant of the Gram matrx B T B. Snce the Gram matrx s ntegral, the determnant must be the square root of an nteger. j=1 2-5

6 Fnally, we note that b = b + µ,+1 b+1. Therefore, b 2 = b 2 + µ 2,+1 b Snce the algorthm swapped b and b +1, we must have Therefore, Φ(B )/Φ(B) < δ, as needed. b 2 = b 2 + µ 2,+1 b +1 2 < δ b 2. The followng corollary s an mmedate consequence of Theorems 2.4 and 2.5. Corollary 2.6. For any constant δ (1/4, 1), there s an effcent algorthm that solves γ-svp wth γ := (δ 1/4) n/2. For completeness, we note that a slghtly better algorthm s known the so-called Block Korkne-Zolotarev algorthm, or BKZ. The BKZ algorthm generalzes the LLL algorthm by consderng k-tuples of adjacent vectors n the bass, as opposed to just pars. Theorem 2.7 ([Sch87, AKS01]). For any constant C > 0, there s an effcent algorthm that solves γ-svp wth γ := 2 Cn log log n/ log n. More generally, for any 2 k n, there s an algorthm that solves γ k -SVP n tme 2 O(k) tmes a polynomal n the nput length wth γ k := k n/k A note on the mportance of ths algorthm An approxmaton factor that s exponental n the dmenson mght not seem very mpressve. But, the LLL algorthm has found nnumerable applcatons. Lenstra, Lenstra, and Lovász orgnally used t to gve an effcent algorthm for factorng ratonal polynomals an applcaton wth obvous mportance. The LLL algorthm s also used to solve computatonal lattce problems and nteger programmng exactly n low dmensons; to fnd nteger relatons and near-nteger relatons of rratonal numbers; to dentfy unknown constants from ther decmal approxmatons; etc. And, t s used as a subroutne n a huge number of lattce algorthms. In the next lecture, we wll see how to use the LLL algorthm to break a large class of knapsack-based encrypton schemes. References [AKS01] Mklós Ajta, Rav Kumar, and D. Svakumar. A seve algorthm for the shortest lattce vector problem. In STOC, pages , [HR12] Ishay Havv and Oded Regev. Tensor-based hardness of the shortest vector problem to wthn almost polynomal factors. Theory of Computng, 8(23): , Prelmnary verson n STOC 07. [Kho05] Subhash Khot. Hardness of approxmatng the shortest vector problem n lattces. Journal of the ACM, 52(5): , September Prelmnary verson n FOCS 04. [LLL82] A. K. Lenstra, H. W. Lenstra, Jr., and L. Lovász. Factorng polynomals wth ratonal coeffcents. Math. Ann., 261(4): ,

7 [Mc01] Danele Mccanco. The shortest vector problem s NP-hard to approxmate to wthn some constant. SIAM Journal on Computng, 30(6): , March Prelmnary verson n FOCS [Sch87] C.P. Schnorr. A herarchy of polynomal tme lattce bass reducton algorthms. Theoretcal Computer Scence, 53(23): ,

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