Titlebook: Algorithmic Aspects in Information and Management; 13th International C Ding-Zhu Du,Lian Li,Jialin Zhang Conference proceedings 2019 Spring

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Approximating Closest Vector Problem in , Norm Revisited, the study for approximating Closest Vector Problem. We give one proof that approximating the Closest Vector Problem over . norm (.) within any constant factor is NP-hard. The result is obtained by the gap-preserving reduction from Min Total Label Cover problem in . norm to .. This proof is simpler than known proofs.

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Introduction to Dense Optical Flowacility has zero or at least . customers. The .-gathering problem asks to find an .-gathering that minimizes the maximum distance between a customer and its facility. In this paper we study the .-gathering problem when the customers and the facilities are on a line, and each customer location is unc

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W.A. Baan,Y. Hagiwara,H.J. Langevelderward re-calculation of . would require . arithmetic operations, where . and . depends on the strategy of computing . appearing in ., using the fastest square matrix multiplication algorithm by Fran?ois Le Gall (ISSAC’14). In this paper, we assume that . is a . matrix and that . is known while no ot

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