Sparse polynomial interpolation based on diversification

We consider the problem of interpolating a sparse multivariate polynomial over a finite field, represented with a black box. Building on the algorithm of Ben-Or and Tiwari (1988) for interpolating polynomials over fields with characteristic zero, we develop a new Monte Carlo algorithm over finite fi...

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Vydané v:Science China. Mathematics Ročník 65; číslo 6; s. 1147 - 1162
Hlavný autor: Huang, Qiao-Long
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Beijing Science China Press 01.06.2022
Springer Nature B.V
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ISSN:1674-7283, 1869-1862
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Shrnutí:We consider the problem of interpolating a sparse multivariate polynomial over a finite field, represented with a black box. Building on the algorithm of Ben-Or and Tiwari (1988) for interpolating polynomials over fields with characteristic zero, we develop a new Monte Carlo algorithm over finite fields by doing additional probes. To interpolate a polynomial f ∈ F q [ x 1 , … , x n ] with a partial degree bound D and a term bound T , our new algorithm costs O ∼ ( n T log 2 q + n T D log q ) bit operations and uses 2( n + 1) T probes to the black box. If q ⩾ O ( nT 2 D ), it has constant success rate to return the correct polynomial. Compared with the previous algorithms over general finite field, our algorithm has better complexity in the parameters n, T and D , and is the first one to achieve the complexity of fractional power about D , while keeping linear in n and T . A key technique is a randomization which makes all the coefficients of the unknown polynomial distinguishable, producing a diverse polynomial. This approach, called diversification, was proposed by Giesbrecht and Roche (2011). Our algorithm interpolates each variable independently by using O ( T ) probes, and then uses the diversification to correlate terms in different images. At last, we get the exponents by solving the discrete logarithms and obtain the coefficients by solving a linear system. We have implemented our algorithm in Maple. Experimental results show that our algorithm can be applied to the sparse polynomials with large degree within a few minutes. We also analyze the success rate of the algorithm.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1674-7283
1869-1862
DOI:10.1007/s11425-020-1791-5