Simple tabulation dates back to Zobrist in 1970 who used it for game playing programs. Keys are viewed as consisting of c characters from some alphabet Φ. We initialize c tables h0, ... , hc-1 mapping characters to random hash values. A key x = (x0, ... , xc-1) is hashed to h0[x0]⊕· · ·⊕hc-1[xc-1], where ⊕ denotes bit-wise exclusive-or. The scheme is extremely fast when the character hash tables hi are in cache. Simple tabulation hashing is not even 4-independent, but we show here that if we apply it twice, then we do get high independence. First we hash to some intermediate keys that are 6 times longer than the original keys, and then we hash the intermediate keys to the final hash values. The intermediate keys have d = 6c characters from Φ. We can then view the hash function as a highly unbalanced bipartite graph with keys on one side, each with edges to d output characters on the other side. We show that this graph has nice expansion properties, and from that it follows that if we perform another level of simple tabulation on the intermediate keys, then the composition is a highly independent hash function. More precisely, the independence we get is |Φ|Ω(1 c). In our O-notation, we view both |Φ| and c is going to infinity, but with c much smaller than |Φ|. Our space is O(c|Φ|) and the hash function is evaluated in O(c) time. Siegel [FOCS'89, SICOMP'04] has proved that with this space, if the hash function is evaluated in o(c) time, then the independence can only be o(c), so our evaluation time is best possible for Ω(c) independence-our independence is much higher if c = |Φ|o(1 c). Siegel used O(c)c evaluation time to get the same independence with similar space. Siegel's main focus was c = O(1), but we are exponentially faster when c = ω(1). Applying our scheme recursively, we can increase our independence to |Φ|Ω(1) with o(clogc) evaluation time. Compared with Siegel's scheme this is both faster and higher independence. Siegel states about his scheme that it is “far too slow for any practical application”. Our scheme is trivial to implement, and it does provide realistic implementations of 100-independent hashing for, say, 32-bit and 64-bit keys.
Klassen and Woelfel @cite_10 focus mostly on @math , where for arbitrary @math they get @math . For general @math , their bound is @math .
@cite_10
2010163765 30524954 2951669000 1997911818
We establish a lower bound for the formula size of quolynomials over arbitrary fields. Our basic formula operations are addition, subtraction, multiplication and division. The proof is based on Neciporuk’s [Soviet Math. Doklady, 7 (1966), pp. 999–1000] lower bound for Boolean functions and uses formal power series. This result immediately yields a lower bound for the formula size of rational functions over infinite fields. We also show how to adapt Neciporuk’s method to rational functions over finite fields. These results are then used to show that, over any field, the @math determinant function has formula size at least @math . We thus have an algebraic analogue to the @math lower bound for the Boolean determinant due to Kloss [Soviet Math. Doklady, 7 (1966), pp. 1537–1540]. In this paper, we prove tight lower bounds on the smallest degree of a nonzero polynomial in the ideal generated by @math or @math in the polynomial ring @math , @math are coprime, which is called over @math . The immunity of @math is lower bounded by @math , which is achievable when @math is a multiple of @math ; the immunity of @math is exactly @math for every @math and @math . Our result improves the previous bound @math by Green. We observe how immunity over @math is related to @math circuit lower bound. For example, if the immunity of @math over @math is lower bounded by @math , and @math , then @math requires @math circuit of exponential size to compute. For any real number @math , we nearly completely characterize the space complexity of estimating @math for @math matrices @math in which each row and each column has @math non-zero entries and whose entries are presented one at a time in a data stream model. Here the @math are the singular values of @math , and when @math , @math is the @math -th power of the Schatten @math -norm. We show that when @math is not an even integer, to obtain a @math -approximation to @math with constant probability, any @math -pass algorithm requires @math bits of space, where @math as @math and @math is a constant independent of @math . However, when @math is an even integer, we give an upper bound of @math bits of space, which holds even in the turnstile data stream model. The latter is optimal up to @math factors. Our results considerably strengthen lower bounds in previous work for arbitrary (not necessarily sparse) matrices @math : the previous best lower bound was @math for @math , @math for @math and @math for @math . We note for @math , while our lower bound for even integers is the same, for other @math in this range our lower bound is @math , which is considerably stronger than the previous @math for small enough constant @math . We obtain similar near-linear lower bounds for Ky-Fan norms, SVD entropy, eigenvalue shrinkers, and M-estimators, many of which could have been solvable in logarithmic space prior to our work. All nonabelian finite simple groups of rank @math over a field of size @math , with the possible exception of the Ree groups @math , have presentations with at most @math relations and bit-length @math . Moreover, @math and @math have presentations with 3 generators @math 7 relations and bit-length @math , while @math has a presentation with 7 generators, @math relations and bit-length @math