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Approximating shortest lattice vectors is not harder than approximating closet lattice vectors
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Improved algorithms for integer programming and related lattice problems
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Hardness of Approximating the Shortest Vector Problem in High Lp Norms
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Generalized Compact Knapsacks, Cyclic Lattices, and Efficient One-Way Functions
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Limits on the Hardness of Lattice Problems in lp Norms
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Foundations of security analysis and design VI
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List Decoding Tensor Products and Interleaved Codes
SIAM Journal on Computing
Parallel shortest lattice vector enumeration on graphics cards
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Public key identification based on the equivalence of quadratic forms
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Sampling methods for shortest vectors, closest vectors and successive minima
ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
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Let p 1 be any fixed real. We show that assuming NP ⊈ RP, there is no polynomial time algorithm that approximates the Shortest Vector Problem (SVP) in ℓp norm within a constant factor. Under the stronger assumption NP ⊈ RTIME(2poly(log n)), we show that there is no polynomial-time algorithm with approximation ratio 2(log n)1/2−ε where n is the dimension of the lattice and ε 0 is an arbitrarily small constant.We first give a new (randomized) reduction from Closest Vector Problem (CVP) to SVP that achieves some constant factor hardness. The reduction is based on BCH Codes. Its advantage is that the SVP instances produced by the reduction behave well under the augmented tensor product, a new variant of tensor product that we introduce. This enables us to boost the hardness factor to 2(log n)1/2-ε.