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Updated: Jan 26, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Bootstrapping variables in algebraic circuits.

Manindra Agrawal1, Sumanta Ghosh1, Nitin Saxena1

  • 1Department of Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India manindra@cse.iitk.ac.in sumghosh@cse.iitk.ac.in nitin@cse.iitk.ac.in.

Proceedings of the National Academy of Sciences of the United States of America
|April 13, 2019
PubMed
Summary

We show that polynomial identity testing (PIT) suffices to study circuits with few variables. A hitting-set generator (HSG) can be efficiently grown, bootstrapping to a complete HSG for improved PIT derandomization.

Keywords:
depth-4derandomizationhitting-setidentity testinglower bound

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Area of Science:

  • Theoretical Computer Science
  • Computational Complexity Theory
  • Algebraic Complexity Theory

Background:

  • The polynomial identity testing (PIT) problem is a fundamental challenge in theoretical computer science.
  • Efficiently derandomizing PIT is a major open problem with implications for circuit complexity.
  • Existing methods often require significant computational resources for large circuit sizes and variable counts.

Purpose of the Study:

  • To reduce the number of variables required for blackbox polynomial identity testing (PIT).
  • To demonstrate a "bootstrapping" behavior for hitting-set generators (HSGs).
  • To explore the implications of PIT for specific circuit depths and variable counts.

Main Methods:

  • Analyzing circuits dependent on a limited number of initial variables.
  • Utilizing the "bootstrapping" property of hitting-set generators (HSGs).
  • Investigating depth-4 circuits with a constant number of variables.

Main Results:

  • It suffices to study size-s, degree-s circuits depending on a constant number of variables for blackbox PIT.
  • A partial HSG can be efficiently grown into a complete HSG, exhibiting bootstrapping behavior.
  • Efficient blackbox PIT for specific circuit types leads to near-complete derandomization of PIT.

Conclusions:

  • The study introduces a novel approach to PIT by focusing on a reduced variable set.
  • The demonstrated bootstrapping behavior of HSGs offers a new pathway for derandomization.
  • The findings suggest stronger lower bounds and advance the understanding of circuit complexity.