text: 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).
  @cite_54: Using ideas from automata theory we design a new efficient (deterministic) identity test for the noncommutative polynomial identity testing problem (first introduced and studied in [RS05, BW05]). More precisely, given as input a noncommutative circuit C x1, ldrldrldr , xn computing a polynomial in F x1, ldrldrldr , xn of degree d with at most t monomials, where the variables xi are noncommuting, we give a deterministic polynomial identity test that checks if C equiv 0 and runs in time polynomial in d, n, |C|, and t. The same methods works in a black-box setting: given a noncommuting black-box polynomial f isin F x1, ldrldrldr , xn of degree d with t monomials we can, in fact, reconstruct the entire polynomial f in time polynomial in n, d and t. Indeed, we apply this idea to the reconstruction of black-box noncommuting algebraic branching programs (the ABPs considered by Nisan in [N91] and Raz-Shpilka in [RS05]). Assuming that the black-box model allows us to query the ABP for the output at any given gate then we can reconstruct an (equivalent) ABP in deterministic polynomial time. Finally, we turn to commutative identity testing and explore the complexity of the problem when the coefficients of the input polynomial come from an arbitrary finite commutative ring with unity whose elements are uniformly encoded as strings and the ring operations are given by an oracle. We show that several algorithmic results for polynomial identity testing over fields also hold when the coefficients come from such finite rings.
  @cite_26: 
  @cite_51: An efficient deterministic polynomial time algorithm is developed for the sparse polynomial interpolation problem. The number of evaluations needed by this algorithm is very small. The algorithm also has a simple NC implementation.
summary: In @cite_54 , Arvind, Mukhopadhyay and Srinivasan gave a deterministic black-box algorithm for identity testing of sparse non-commutative polynomials. The algorithm runs in time polynomial in the number of variables, degree and sparsity of the unknown polynomial. This is similar to the running time achieved in the commutative setting for sparse polynomials (see e.g., @cite_51 @cite_26 ) and in particular it is better than our quasi-polynomial time algorithm. On the other hand our algorithm is more general and works for any non-commutative polynomial that is computed by a small ABP.