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Quadratic Lower Bounds on Matrix Rigidity

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Book cover Theory and Applications of Models of Computation (TAMC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3959))

Abstract

The rigidity of a matrix A with respect to the rank bound r is the minimum number of entries of A that must be changed to reduce the rank of A to or below r. It is a major unsolved problem (Valiant, 1977) to construct “explicit” families of n × n matrices of rigidity n 1 + δ for r=εn, where ε and δ are positive constants. In fact, no superlinear lower bounds are known for explicit families of matrices for rank bound r=Ω(n).

In this paper we give the first optimal, Ω(n 2), lower bound on the rigidity of two “somewhat explicit” families of matrices with respect to the rank bound r=cn, where c is an absolute positive constant. The entries of these matrix families are (i) square roots of n 2 distinct primes and (ii) primitive roots of unity of prime orders for the first n 2 primes. Our proofs use an algebraic dimension concept introduced by Shoup and Smolensky (1997) and a generalization of that concept.

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Lokam, S.V. (2006). Quadratic Lower Bounds on Matrix Rigidity. In: Cai, JY., Cooper, S.B., Li, A. (eds) Theory and Applications of Models of Computation. TAMC 2006. Lecture Notes in Computer Science, vol 3959. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11750321_28

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  • DOI: https://doi.org/10.1007/11750321_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34021-8

  • Online ISBN: 978-3-540-34022-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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