We study the problem of obtaining deterministic black-box polynomial identity testing algorithms (PIT) for algebraic branching programs (ABPs) that are read-once and oblivious. This class has an deterministic white-box polynomial identity testing algorithm (due to Raz and Shpilka), but prior to this work there was no known such black-box algorithm. The main result of this work gives the first quasi-polynomial sized hitting sets for size S circuits from this class, when the order of the variables is known. As our hitting set is of size exp(lg^2 S), this is analogous (in the terminology of boolean pseudorandomness) to a seed-length of lg^2 S, which is the seed length of the pseudorandom generators of Nisan and Impagliazzo-Nisan-Wigderson for read-once oblivious boolean branching programs. Our results are stronger for branching programs of bounded width, where we give a hitting set of size exp(lg^2 S lglg S), corresponding to a seed length of lg^2 S lglg S. This is in stark contrast to the known results for read-once oblivious boolean branching programs of bounded width, where no pseudorandom generator (or hitting set) with seed length o(lg^2 S) is known. In follow up work, we strengthened a result of Mulmuley, and showed that derandomizing a particular case of the Noether Normalization Lemma is reducible to black-box PIT of read-once oblivious ABPs. Using the results of the present work, this gives a derandomization of Noether Normalization in that case, which Mulmuley conjectured would difficult due to its relations to problems in algebraic geometry. We also show that several other circuit classes can be black-box reduced to read-once oblivious ABPs, including set-multilinear ABPs (a generalization of depth-3 set-multilinear formulas), non-commutative ABPs (generalizing non-commutative formulas), and (semi-)diagonal depth-4 circuits (as introduced by Saxena).
However, despite the similarities to the boolean regime, our results improve when the branching programs have bounded width. Specifically, our hitting sets ( thm:small width ) achieve a seed length of @math , and it has been a long-standing open problem (see [Open Problem 8.6] Vadhan12 ) to achieve a seed length (for pseudorandom generators, or even hitting sets) of @math for boolean read-once oblivious branching programs of constant width. Despite much recent work (see @cite_31 @cite_22 @cite_41 @cite_49 @cite_7 @cite_10 @cite_2 @cite_27 ), such seed-lengths are only known for branching programs that are restricted even further, such as regular or permutation branching programs. It is an interesting question as to whether any insights of this paper can achieve a similar seed length in the boolean regime, or in general whether there are any formal connections between algebraic and boolean pseudorandomness for read-once oblivious branching programs.
@cite_22 @cite_7 @cite_41 @cite_27 @cite_49 @cite_2 @cite_31 @cite_10
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We prove the existence of a polynomial time computable pseudorandom generator that @math -fools constant width regular read-once branching programs of length @math using a seed of length @math . The previous best pseudorandom generator for regular branching programs used a seed of length @math , and was due to Braverman et. al. and Brody and Verbin (FOCS 2010). Our pseudorandom generator is the INW generator due to Impagliazzo et. al. (STOC 1994). Our work shares some similarity with the recent work of Kouck ' y et. al. (STOC 2011)who get similar seed length for permutation branching programs. However, our work proceeds by analyzing the eigenvalues of the stochastic matrices that arise in the transitions of the branching program which arguably makes the technique more robust. As a corollary of our techniques, we present new results on the small biased spaces for group products'' problem by Meka and Zuckerman (RANDOM 2009). We get a pseudorandom generator with seed length @math . Previously, using the result of Kouck ' y et. al., it was possible to get a seed length of @math for this problem. We present an iterative approach to constructing pseudorandom generators, based on the repeated application of mild pseudorandom restrictions. We use this template to construct pseudorandom generators for combinatorial rectangles and read-once CNFs and a hitting set generator for width-3 branching programs, all of which achieve near-optimal seed-length even in the low-error regime: We get seed-length O(log (n epsilon)) for error epsilon. Previously, only constructions with seed-length O( ^ 3 2 n) or O( ^2 n) were known for these classes with polynomially small error. The (pseudo)random restrictions we use are milder than those typically used for proving circuit lower bounds in that we only set a constant fraction of the bits at a time. While such restrictions do not simplify the functions drastically, we show that they can be derandomized using small-bias spaces. Recently, an interest in constructing pseudorandom or hitting set generators for restricted branching programs has increased, which is motivated by the fundamental problem of derandomizing space bounded computations. Such constructions have been known only in the case of width 2 and in very restricted cases of bounded width. In our previous work, we have introduced a so-called richness condition which is, in a certain sense, sufficient for a set to be a hitting set for read-once branching programs of width 3. In this paper, we prove that, for a suitable constant C, any almost C log n-wise independent set satisfies this richness condition. Hence, we achieve an explicit polynomial time construction of a hitting set for read-once branching programs of width 3 with the acceptance probability greater than 12 13 by using the result due to (1992). We prove that the pseudorandom generator introduced by (1994) with proper choice of parameters fools group products of a given finite group G. The seed length is O((|G|O(1) + log 1 )log n), where n is the length of the word and  is the allowed error. The result implies that the pseudorandom generator with seed length O((2O(w log w) + log 1 )log n) fools read-once permutation branching programs of width w. As an application of the pseudorandom generator one obtains small-bias spaces for products over all finite groups Meka and Zuckerman (2009). The is the following problem: a coin is given, which lands on head with probability either @math or @math . We are given the outcome of @math independent tosses of this coin, and the goal is to guess which way the coin is biased, and to answer correctly with probability @math . When our computational model is unrestricted, the majority function is optimal, and succeeds when @math for a large enough constant @math . The coin problem is open and interesting in models that cannot compute the majority function. In this paper we study the coin problem in the model of . We prove that in order to succeed in this model, @math must be at least @math . For constant @math this is tight by considering the recursive tribes function, and for other values of @math this is nearly tight by considering other read-once AND-OR trees. We generalize this to a , where instead of independent tosses of a coin we are given independent tosses of one of two @math -sided dice. We prove that if the distributions are too close and the mass of each side of the dice is not too small, then the dice cannot be distinguished by small-width read-once branching programs. We suggest one application for this kind of theorems: we prove that Nisan's Generator fools width- @math read-once branching programs, using seed length @math . For @math , this seed length is @math . The coin theorem and its relatives might have other connections to PRGs. This application is related to the independent, but chronologically-earlier, work of Braver man, Rao, Raz and Yehudayoff BRRY . We study the natural question of constructing pseudorandom generators (PRGs) for low-degree polynomial threshold functions (PTFs). We give a PRG with seed-length log n eO(d) fooling degree d PTFs with error at most e. Previously, no nontrivial constructions were known even for quadratic threshold functions and constant error e. For the class of degree 1 threshold functions or halfspaces, we construct PRGs with much better dependence on the error parameter e and obtain the following results. A PRG with seed length O(log n log(1 e)) for error e  1 poly(n). A PRG with seed length O(log n) for e  1 poly(log n). Previously, only PRGs with seed length O(log n log2(1 e) e2) were known for halfspaces. We also obtain PRGs with similar seed lengths for fooling halfspaces over the @math dimensional unit sphere. The main theme of our constructions and analysis is the use of invariance principles to construct pseudorandom generators. We also introduce the notion of monotone read-once branching programs, which is key to improving the dependence on the error rate e for halfspaces. These techniques may be of independent interest. Recently Bogdanov and Viola (FOCS 2007) and Lovett (ECCC-07) constructed pseudorandom generators that fool degree k polynomials over F2 for an arbitrary constant k. We show that such generators can also be used to fool branching programs of width 2 and polynomial length that read k bits of inputs at a time. This model generalizes polynomials of degree k over F2 and includes some other interesting classes of functions, for instance k-DNF. The constructions of Bogdanov and Viola and Lovett consist of adding a constant number of independent copies of a generator that fools linear functions (an -biased set). It is natural to ask, in light of our first result, whether such generators can fool branching programs of width larger than 2. Our second result is a lower bound showing In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.