{"title":"Polynomial Representations of Threshold Functions and Algorithmic Applications","authors":"Josh Alman, Timothy M. Chan, Ryan Williams","doi":"10.1109/FOCS.2016.57","DOIUrl":null,"url":null,"abstract":"We design new polynomials for representing threshold functions in three different regimes: probabilistic polynomials of low degree, which need far less randomness than previous constructions, polynomial threshold functions (PTFs) with \"nice\" threshold behavior and degree almost as low as the probabilistic polynomials, and a new notion of probabilistic PTFs where we combine the above techniques to achieve even lower degree with similar \"nice\" threshold behavior. Utilizing these polynomial constructions, we design faster algorithms for a variety of problems: · Offline Hamming Nearest (and Furthest) Neighbors: Given n red and n blue points in d-dimensional Hamming space for d = c log n, we can find an (exact) nearest (or furthest) blue neighbor for every red point in randomized time n<sup>2-1</sup>/O(√clog<sup>2/3</sup> c) or deterministic time n<sup>2-1/O(c log2 c)</sup>. These improve on a randomized n<sup>2-1/O(c log2 c)</sup> bound by Alman and Williams (FOCS'15), and also lead to faster MAX-SAT algorithms for sparse CNFs. · Offline Approximate Nearest (and Furthest) Neighbors: Given n red and n blue points in d-dimensional ℓ<sub>1</sub> or Euclidean space, we can find a (1+ε)-approximate nearest (or furthest) blue neighbor for each red point in randomized time near dn+n<sup>2-Ω(ε1/3/log(1/ε))</sup>. This improves on an algorithm by Valiant (FOCS'12) with randomized time near dn+n<sup>2-Ω(√ε)</sup>, which in turn improves previous methods based on locality-sensitive hashing. · SAT Algorithms and Lower Bounds for Circuits With Linear Threshold Functions: We give a satisfiability algorithm for AC<sup>0</sup>[m] o LTF LTF circuits with a subquadratic number of LTF gates on the bottom layer, and a subexponential number of gates on the other layers, that runs in deterministic 2<sup>n-n</sup><sup>ε</sup> time. This strictly generalizes a SAT algorithm for ACC<sup>0</sup> oLTF circuits of subexponential size by Williams (STOC'14) and also implies new circuit lower bounds for threshold circuits, improving a recent gate lower bound of Kane and Williams (STOC'16). We also give a randomized 2<sup>n-n</sup><sup>ε</sup>-time SAT algorithm for subexponential-size MAJ o AC<sub>0</sub> oLTF o AC<sub>0</sub> oLTF circuits, where the top MAJ gate and middle LTF gates have O(n<sup>6/5-δ</sup>) fan-in.","PeriodicalId":414001,"journal":{"name":"2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"82","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FOCS.2016.57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 82
Abstract
We design new polynomials for representing threshold functions in three different regimes: probabilistic polynomials of low degree, which need far less randomness than previous constructions, polynomial threshold functions (PTFs) with "nice" threshold behavior and degree almost as low as the probabilistic polynomials, and a new notion of probabilistic PTFs where we combine the above techniques to achieve even lower degree with similar "nice" threshold behavior. Utilizing these polynomial constructions, we design faster algorithms for a variety of problems: · Offline Hamming Nearest (and Furthest) Neighbors: Given n red and n blue points in d-dimensional Hamming space for d = c log n, we can find an (exact) nearest (or furthest) blue neighbor for every red point in randomized time n2-1/O(√clog2/3 c) or deterministic time n2-1/O(c log2 c). These improve on a randomized n2-1/O(c log2 c) bound by Alman and Williams (FOCS'15), and also lead to faster MAX-SAT algorithms for sparse CNFs. · Offline Approximate Nearest (and Furthest) Neighbors: Given n red and n blue points in d-dimensional ℓ1 or Euclidean space, we can find a (1+ε)-approximate nearest (or furthest) blue neighbor for each red point in randomized time near dn+n2-Ω(ε1/3/log(1/ε)). This improves on an algorithm by Valiant (FOCS'12) with randomized time near dn+n2-Ω(√ε), which in turn improves previous methods based on locality-sensitive hashing. · SAT Algorithms and Lower Bounds for Circuits With Linear Threshold Functions: We give a satisfiability algorithm for AC0[m] o LTF LTF circuits with a subquadratic number of LTF gates on the bottom layer, and a subexponential number of gates on the other layers, that runs in deterministic 2n-nε time. This strictly generalizes a SAT algorithm for ACC0 oLTF circuits of subexponential size by Williams (STOC'14) and also implies new circuit lower bounds for threshold circuits, improving a recent gate lower bound of Kane and Williams (STOC'16). We also give a randomized 2n-nε-time SAT algorithm for subexponential-size MAJ o AC0 oLTF o AC0 oLTF circuits, where the top MAJ gate and middle LTF gates have O(n6/5-δ) fan-in.