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Prediction from Partial Information and Hindsight, with Application to Circuit Lower Bounds

  • Or MeirEmail author
  • Avi Wigderson
Article
  • 4 Downloads

Abstract

Consider a random sequence of n bits that has entropy at least nk, where \({k\ll n}\) . A commonly used observation is that an average coordinate of this random sequence is close to being uniformly distributed, that is, the coordinate “looks random.” In this work, we prove a stronger result that says, roughly, that the average coordinate looks random to an adversary that is allowed to query \({\approx\frac{n}{k}}\) other coordinates of the sequence, even if the adversary is non-deterministic. This implies corresponding results for decision trees and certificates for Boolean functions.

As an application of this result, we prove a new result on depth-3 circuits, which recovers as a direct corollary the known lower bounds for the parity and majority functions, as well as a lower bound on sensitive functions due to Boppana (Circuits Inf Process Lett 63(5):257–261, 1997). An interesting feature of this proof is that it works in the framework of Karchmer and Wigderson (SIAM J Discrete Math 3(2):255–265, 1990), and, in particular, it is a “top-down” proof (Håstad et al. in Computat Complex 5(2):99–112, 1995). Finally, it yields a new kind of a random restriction lemma for non-product distributions, which may be of independent interest.

Keywords

Certificate complexity Circuit complexity Circuit complexity lower bounds Decision tree complexity Information theoretic Query complexity Sensitivity 

Subject classification

68Q15 

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Notes

Acknowledgements

We would like to thank Oded Goldreich and Benjamin Rossman for valuable discussions and ideas. We would also like to thank Roei Tell for pointing out an error in the introduction of an earlier version of this work. Finally, we thank anonymous referees for comments that improved the presentation of this work and for pointing out connections to the work of Paturi et al. (1999).

Or Meir is partially supported by the Israel Science Foundation (Grant No. 1445/16). Part of this research was done while Or Meir was partially supported by NSF Grant CCF-1412958. Avi Wigderson was partially supported from NSF Grant CCF-1412958.

References

  1. Ajtai, Miklós: \(\Sigma _1^1\)-Formulae on finite structures. Annals of Pure and Applied Logic 24(1), 1–48 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  2. Ajtai, Miklós: Boolean Complexity and Probabilistic Constructions, 140–164. London Mathematical Society Lecture Note Series, Cambridge University Press (1992)zbMATHGoogle Scholar
  3. Miklós Ajtai (1993). Geometric Properties of Sets Defined by Constant Depth Circuits. In Combinatorics, Paul Erdős is eighty. Budapest, Hungary : János Bolyai Mathematical Society. ISBN 9638022736 (set)Google Scholar
  4. Ziv Bar-Yossef, T.S., Jayram, Ravi Kumar, Sivakumar, D.: An information statistics approach to data stream and communication complexity. J. Comput. Syst. Sci. 68(4), 702–732 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  5. Paul Beame (1994). A switching lemma primer. Technical report, Technical Report UW-CSE-95-07-01, Department of Computer Science and Engineering, University of WashingtonGoogle Scholar
  6. Boppana, Ravi B.: The Average Sensitivity of Bounded-Depth Circuits. Inf. Process. Lett. 63(5), 257–261 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  7. Xi Chen, Igor Carboni Oliveira, Rocco A. Servedio & Li-Yang Tan (2016). Near-optimal small-depth lower bounds for small distance connectivity. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18–21, 2016, 612–625Google Scholar
  8. Thomas M. Cover & Joy A. Thomas (1991). Elements of information theory. Wiley-Interscience. ISBN 0-471-06259-6Google Scholar
  9. Irit Dinur & Or Meir (2016). Toward the KRW Composition Conjecture: Cubic Formula Lower Bounds via Communication Complexity. In 31st Conference on Computational Complexity, CCC 2016, May 29 to June 1, 2016, Tokyo, Japan, 3:1–3:51Google Scholar
  10. Duris, Pavol, Galil, Zvi, Schnitger, Georg: Lower Bounds on Communication Complexity. Inf. Comput. 73(1), 1–22 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  11. Edmonds, Jeff, Impagliazzo, Russell, Rudich, Steven, Sgall, Jiri: Communication complexity towards lower bounds on circuit depth. Computational Complexity 10(3), 210–246 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  12. Furst, Merrick L., Saxe, James B., Sipser, Michael: Parity, Circuits, and the Polynomial-Time Hierarchy. Mathematical Systems Theory 17(1), 13–27 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  13. Anat Ganor, Gillat Kol & Ran Raz (2014). Exponential Separation of Information and Communication. In 55th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2014, Philadelphia, PA, USA, October 18–21, 2014, 176–185Google Scholar
  14. Dmitry Gavinsky, Or Meir, Omri Weinstein & Avi Wigderson (2014). Toward better formula lower bounds: an information complexity approach to the KRW composition conjecture. In Symposium on Theory of Computing, STOC 2014, New York, NY, USA, May 31 - June 03, 2014, 213–222Google Scholar
  15. Michelangelo Grigni & Michael Sipser (1991). Monotone Separation of Logspace from NC. In Structure in Complexity Theory Conference, 294–298Google Scholar
  16. Grinberg, Aryeh, Shaltiel, Ronen, Viola, Emanuele: Indistinguishability by adaptive procedures with advice, and lower bounds on hardness amplification proofs. Electronic Colloquium on Computational Complexity (ECCC) 25, 61 (2018)Google Scholar
  17. Johan Håstad (1986). Almost Optimal Lower Bounds for Small Depth Circuits. In STOC, 6–20Google Scholar
  18. Håstad, Johan, Jukna, Stasys, Pudlák, Pavel: Top-Down Lower Bounds for Depth-Three Circuits. Computational Complexity 5(2), 99–112 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  19. Hossein Jowhari, Mert Săglam & Gábor Tardos (2011). Tight bounds for Lp samplers, finding duplicates in streams, and related problems. In Proceedings of the 30th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2011, June 12–16, 2011, Athens, Greece, 49–58Google Scholar
  20. Mauricio Karchmer, Eyal Kushilevitz & Noam Nisan (1995a). Fractional Covers and Communication Complexity. SIAM J. Discrete Math. 8(1), 76–92Google Scholar
  21. Mauricio Karchmer, Ran Raz & Avi Wigderson (1995b). Super-Logarithmic Depth Lower Bounds Via the Direct Sum in Communication Complexity. Computational Complexity 5(3/4), 191–204Google Scholar
  22. Mauricio Karchmer & Avi Wigderson: Monotone Circuits for Connectivity Require Super-Logarithmic Depth. SIAM J. Discrete Math. 3(2), 255–265 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  23. Khrapchenko, V.M.: A method of obtaining lower bounds for the complexity of \(\pi \)-schemes. Mathematical Notes Academy of Sciences USSR 10, 474–479 (1972)zbMATHGoogle Scholar
  24. Maria M. Klawe, Wolfgang J. Paul, Nicholas Pippenger & Mihalis Yannakakis (1984). On Monotone Formulae with Restricted Depth (Preliminary Version). In Proceedings of the 16th Annual ACM Symposium on Theory of Computing, April 30 - May 2, 1984, Washington, DC, USA, 480–487Google Scholar
  25. Gillat Kol & Ran Raz (2013). Interactive channel capacity. In Symposium on Theory of Computing onference, STOC'13, Palo Alto, CA, USA, June 1–4, 2013, 715–724Google Scholar
  26. Linial, Nathan, Mansour, Yishay, Nisan, Noam: Constant Depth Circuits, Fourier Transform, and Learnability. J. ACM 40(3), 607–620 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  27. Lyle A. Mcgeoch (1986). A Strong Separation betweem \(k\) and \(k-1\) Round Communication Complexity for a Constructive Language. Technical Report CMU-CS-86-157, Carnegie Mellon UniversityGoogle Scholar
  28. Meir, Or: An Efficient Randomized Protocol for every Karchmer-Wigderson Relation with Three Rounds. Electronic Colloquium on Computational Complexity (ECCC) 24, 129 (2017)Google Scholar
  29. Noam Nisan & Avi Wigderson: Rounds in Communication Complexity Revisited. SIAM J. Comput. 22(1), 211–219 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  30. Noam Nisan & David Zuckerman: Randomness is Linear in Space. J. Comput. Syst. Sci. 52(1), 43–52 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  31. Papadimitriou, Christos H., Sipser, Michael: Communication Complexity. J. Comput. Syst. Sci. 28(2), 260–269 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  32. Paturi, Ramamohan: Pavel Pudlák & Francis Zane (1999). Satisfiability Coding Lemma. Chicago J. Theor. Comput, Sci (1999)Google Scholar
  33. Toniann Pitassi, Benjamin Rossman, Rocco A. Servedio & Li-Yang Tan (2016). Poly-logarithmic Frege depth lower bounds via an expander switching lemma. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18–21, 2016, 644–657Google Scholar
  34. Raz, Ran: A Parallel Repetition Theorem. SIAM J. Comput. 27(3), 763–803 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  35. Ran Raz & Avi Wigderson (1989). Probabilistic Communication Complexity of Boolean Relations (Extended Abstract). In FOCS, 562–567Google Scholar
  36. Ran Raz & Avi Wigderson: Monotone Circuits for Matching Require Linear Depth. J. ACM 39(3), 736–744 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  37. A. A. Razborov (1992a). On Submodular Complexity Measures. In Poceedings of the London Mathematical Society Symposium on Boolean Function Complexity, 76–83. Cambridge University Press, New York, NY, USA. ISBN 0-521-40826-1Google Scholar
  38. Razborov, Alexander A.: Applications of matrix methods to the theory of lower bounds in computational complexity. Combinatorica 10(1), 81–93 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  39. Alexander A. Razborov (1992b). On the Distributional Complexity of Disjointness. Theor. Comput. Sci. 106(2), 385–390Google Scholar
  40. Smal, Alexander V., Talebanfard, Navid: Prediction from partial information and hindsight, an alternative proof. Inf. Process. Lett. 136, 102–104 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  41. Emanuele Viola (2018). AC0 unpredictability. Electronic Colloquium on Computational Complexity (ECCC) 209Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HaifaHaifaIsrael
  2. 2.Institute for Advanced StudyPrincetonUSA

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