ECCC-Report TR13-132https://eccc.weizmann.ac.il/report/2013/132Comments and Revisions published for TR13-132en-usMon, 23 Sep 2013 08:28:28 +0300
Paper TR13-132
| Pseudorandomness for Multilinear Read-Once Algebraic Branching Programs, in any Order |
Michael Forbes,
Ramprasad Saptharishi,
Amir Shpilka
https://eccc.weizmann.ac.il/report/2013/132We give deterministic black-box polynomial identity testing algorithms for multilinear read-once oblivious algebraic branching programs (ROABPs), in n^(lg^2 n) time. Further, our algorithm is oblivious to the order of the variables. This is the first sub-exponential time algorithm for this model. Furthermore, our result has no known analogue in the model of read-once oblivious boolean branching programs with unknown order, as despite recent work there is no known pseudorandom generator for this model with sub-polynomial seed-length (for unbounded-width branching programs).
This result extends and generalizes the result of Forbes and Shpilka that obtained a n^(lg n)-time algorithm when given the order. We also extend and strengthen the work of Agrawal, Saha and Saxena that gave a black-box algorithm running in time exp((lg n)^d) for set-multilinear formulas of depth d. We note that the model of multilinear ROABPs contains the model of set-multilinear algebraic branching programs, which itself contains the model of set-multilinear formulas of arbitrary depth. We obtain our results by recasting, and improving upon, the ideas of Agrawal, Saha and Saxena. We phrase the ideas in terms of rank condensers and Wronskians, and show that our results improve upon the classical multivariate Wronskian, which may be of independent interest.
In addition, we give the first n^(lglg n) black-box polynomial identity testing algorithm for the so called model of diagonal circuits. This model, introduced by Saxena has recently found applications in the work of Mulmuley, as well as in the work of Gupta, Kamath, Kayal, Saptharishi. Previously, Agrawal, Saha and Saxena, Forbes and Shpilka, and Forbes and Shpilka had given n^(lg n)-time algorithms for this class. More generally, our result holds for any model computing polynomials whose partial derivatives (of all orders) span a low dimensional linear space.
Mon, 23 Sep 2013 08:28:28 +0300https://eccc.weizmann.ac.il/report/2013/132