Polynomial time deterministic identity testing algorithm for Σ [3] ΠΣΠ [2] circuits via Edelstein–Kelly type theorem for quadratic polynomials

Author(s):  
Shir Peleg ◽  
Amir Shpilka
2019 ◽  
Vol 116 (17) ◽  
pp. 8107-8118
Author(s):  
Manindra Agrawal ◽  
Sumanta Ghosh ◽  
Nitin Saxena

We show that for the blackbox polynomial identity testing (PIT) problem it suffices to study circuits that depend only on the first extremely few variables. One needs only to consider size-s degree-s circuits that depend on the firstlog○c svariables (where c is a constant and composes a logarithm with itself c times). Thus, the hitting-set generator (hsg) manifests a bootstrapping behavior—a partial hsg against very few variables can be efficiently grown to a complete hsg. A Boolean analog, or a pseudorandom generator property of this type, is unheard of. Our idea is to use the partial hsg and its annihilator polynomial to efficiently bootstrap the hsg exponentially w.r.t. variables. This is repeated c times in an efficient way. Pushing the envelope further we show that (i) a quadratic-time blackbox PIT for 6,913-variate degree-s size-s polynomials will lead to a “near”-complete derandomization of PIT and (ii) a blackbox PIT for n-variate degree-s size-s circuits insnδtime, forδ<1/2, will lead to a near-complete derandomization of PIT (in contrast,sntime is trivial). Our second idea is to study depth-4 circuits that depend on constantly many variables. We show that a polynomial-time computable,O(s1.49)-degree hsg for trivariate depth-4 circuits bootstraps to a quasipolynomial time hsg for general polydegree circuits and implies a lower bound that is a bit stronger than that of Kabanets and Impagliazzo [Kabanets V, Impagliazzo R (2003)Proceedings of the Thirty-Fifth Annual ACM Symposium on Theory of Computing STOC ’03].


2019 ◽  
Author(s):  
S Peter ◽  
BNK Abdelmalek ◽  
C Demuth ◽  
B Meier ◽  
E Wolfram
Keyword(s):  

2018 ◽  
Vol 60 (2) ◽  
pp. 360-375
Author(s):  
A. V. Vasil'ev ◽  
D. V. Churikov

Author(s):  
Е.А. Померанцева ◽  
А.А. Исаев ◽  
А.П. Есакова ◽  
И.В. Поволоцкая ◽  
Е.В. Денисенкова ◽  
...  

Согласно рекомендациям Американской академии педиатрии при постановке диагноза аутизм, следует направить семью на консультацию генетика и генетическое обследование. Однако оптимальный подход к алгоритму генетического обследования при выявлении расстройства аутистического спектра еще предстоит разработать. В рамках исследования было проведено сравнение выявляемости генетических факторов аутизма различными молекулярно-генетическими тестами. According to American Academy of Pediatrics recent guidelines, each family with a child diagnosed with autistic spectrum disorder should be reffered to a medical geneticist and offered genetic tests. However, an optimal genetic testing algorithm has yet to be developed. This study was conducted to compare abilities of different molecular-genetic methods to detect genetic factors of autistic spectrum disorders.


10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.


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