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1826212374025076736
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MIT
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© Richard Ryan Williams. This paper provides both positive and negative results for counting solutions to systems of polynomial equations over a finite field. The general idea is to try to reduce the problem to counting solutions to a single polynomial, where the task is easier. In both cases, simple methods are utilized that we expect will have wider applicability (far beyond algebra). First, we give an efficient deterministic reduction from approximate counting for a system of (arbitrary) polynomial equations to approximate counting for one equation, over any finite field. We apply this reduction to give a deterministic poly(n, s, log p)/ϵ2 -time algorithm for approximately counting the fraction of solutions to a system of s quadratic n-variate polynomials over Fp (the finite field of prime order p) to within an additive ϵ factor, for any prime p. Note that uniform random sampling would already require Ω(s/ϵ2) time, so our algorithm behaves as a full derandomization of uniform sampling. The approximate-counting algorithm yields efficient approximate counting for other well-known problems, such as 2-SAT, NAE-3SAT, and 3-Coloring. As a corollary, there is a deterministic algorithm (with analogous running time) for producing solutions to such systems which have at least ϵpn solutions. Second, we consider the difficulty of exactly counting solutions to a single polynomial of constant degree, over a finite field. (Note that finding a solution in this case is easy.) It has been known for over 20 years that this counting problem is already NP-hard for degree-three polynomials over F2; however, all known reductions increased the number of variables by a considerable amount. We give a subexponential-time reduction from counting solutions to k-CNF formulas to counting solutions to a degree-kO(k) polynomial (over any finite field of O(1) order) which exactly preserves the number of variables. As a corollary, the Strong Exponential Time Hypothesis (even its weak counting variant #SETH) implies that counting solutions to constant-degree polynomials (even over F2) requires essentially 2n time. Similar results hold for counting orthogonal pairs of vectors over Fp.
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2024-09-23T15:20:21Z
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Article
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mit-1721.1/137334
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Massachusetts Institute of Technology
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English
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2024-09-23T15:20:21Z
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2021
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dspace
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mit-1721.1/1373342022-04-01T17:47:12Z Counting solutions to polynomial systems via reductions © Richard Ryan Williams. This paper provides both positive and negative results for counting solutions to systems of polynomial equations over a finite field. The general idea is to try to reduce the problem to counting solutions to a single polynomial, where the task is easier. In both cases, simple methods are utilized that we expect will have wider applicability (far beyond algebra). First, we give an efficient deterministic reduction from approximate counting for a system of (arbitrary) polynomial equations to approximate counting for one equation, over any finite field. We apply this reduction to give a deterministic poly(n, s, log p)/ϵ2 -time algorithm for approximately counting the fraction of solutions to a system of s quadratic n-variate polynomials over Fp (the finite field of prime order p) to within an additive ϵ factor, for any prime p. Note that uniform random sampling would already require Ω(s/ϵ2) time, so our algorithm behaves as a full derandomization of uniform sampling. The approximate-counting algorithm yields efficient approximate counting for other well-known problems, such as 2-SAT, NAE-3SAT, and 3-Coloring. As a corollary, there is a deterministic algorithm (with analogous running time) for producing solutions to such systems which have at least ϵpn solutions. Second, we consider the difficulty of exactly counting solutions to a single polynomial of constant degree, over a finite field. (Note that finding a solution in this case is easy.) It has been known for over 20 years that this counting problem is already NP-hard for degree-three polynomials over F2; however, all known reductions increased the number of variables by a considerable amount. We give a subexponential-time reduction from counting solutions to k-CNF formulas to counting solutions to a degree-kO(k) polynomial (over any finite field of O(1) order) which exactly preserves the number of variables. As a corollary, the Strong Exponential Time Hypothesis (even its weak counting variant #SETH) implies that counting solutions to constant-degree polynomials (even over F2) requires essentially 2n time. Similar results hold for counting orthogonal pairs of vectors over Fp. 2021-11-04T14:50:15Z 2021-11-04T14:50:15Z 2018-01 2021-03-30T13:46:59Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137334 2018. "Counting solutions to polynomial systems via reductions." OpenAccess Series in Informatics, 61. en 10.4230/OASIcs.SOSA.2018.6 OpenAccess Series in Informatics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf DROPS
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| spellingShingle |
Counting solutions to polynomial systems via reductions
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| title |
Counting solutions to polynomial systems via reductions
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| title_full |
Counting solutions to polynomial systems via reductions
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| title_fullStr |
Counting solutions to polynomial systems via reductions
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| title_full_unstemmed |
Counting solutions to polynomial systems via reductions
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| title_short |
Counting solutions to polynomial systems via reductions
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| title_sort |
counting solutions to polynomial systems via reductions
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| url |
https://hdl.handle.net/1721.1/137334
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