Approximate CVP in time 20.802 n - now in any norm!
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[1] Alexander Golovnev,et al. On the Quantitative Hardness of CVP , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).
[2] Daniele Micciancio,et al. Faster exponential time algorithms for the shortest vector problem , 2010, SODA '10.
[3] Ravi Kumar,et al. A sieve algorithm for the shortest lattice vector problem , 2001, STOC '01.
[4] Divesh Aggarwal,et al. Improved Algorithms for the Shortest Vector Problem and the Closest Vector Problem in the Infinity Norm , 2018, ISAAC.
[5] Miklós Ajtai,et al. The shortest vector problem in L2 is NP-hard for randomized reductions (extended abstract) , 1998, STOC '98.
[6] Daniel Dadush,et al. Lattice Sparsification and the Approximate Closest Vector Problem , 2013, SODA.
[7] V. Milman,et al. Asymptotic Geometric Analysis, Part I , 2015 .
[8] Oded Regev,et al. Tensor-based hardness of the shortest vector problem to within almost polynomial factors , 2007, STOC '07.
[9] Santosh S. Vempala,et al. Enumerative Lattice Algorithms in any Norm Via M-ellipsoid Coverings , 2010, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.
[10] Xiaoyun Wang,et al. Finding Shortest Lattice Vectors in the Presence of Gaps , 2015, CT-RSA.
[11] Dorit Aharonov,et al. Lattice problems in NP ∩ coNP , 2005, JACM.
[12] László Lovász,et al. Factoring polynomials with rational coefficients , 1982 .
[13] Divesh Aggarwal,et al. Fine-grained hardness of CVP(P) - Everything that we can prove (and nothing else) , 2019, SODA.
[14] Daniel Dadush,et al. Near-optimal deterministic algorithms for volume computation via M-ellipsoids , 2012, Proceedings of the National Academy of Sciences.
[15] Friedrich Eisenbrand,et al. Approximate CVPp in Time 20.802 n , 2020, ESA.
[16] J. G. Pierce,et al. Geometric Algorithms and Combinatorial Optimization , 2016 .
[17] C. P. Schnorr,et al. A Hierarchy of Polynomial Time Lattice Basis Reduction Algorithms , 1987, Theor. Comput. Sci..
[18] Andrew Odlyzko,et al. The Rise and Fall of Knapsack Cryptosystems , 1998 .
[19] Oded Goldreich,et al. On the Limits of Nonapproximability of Lattice Problems , 2000, J. Comput. Syst. Sci..
[20] Ravi Kannan,et al. Minkowski's Convex Body Theorem and Integer Programming , 1987, Math. Oper. Res..
[21] Friedrich Eisenbrand,et al. Covering cubes and the closest vector problem , 2011, SoCG '11.
[22] Oded Regev,et al. On lattices, learning with errors, random linear codes, and cryptography , 2005, STOC '05.
[23] Márton Naszódi,et al. Covering convex bodies and the Closest Vector Problem , 2019, ArXiv.
[24] Chris Peikert,et al. Limits on the Hardness of Lattice Problems in ℓp Norms , 2008, Twenty-Second Annual IEEE Conference on Computational Complexity (CCC'07).
[25] F. John. Extremum Problems with Inequalities as Subsidiary Conditions , 2014 .
[26] M'arton Nasz'odi. On some covering problems in geometry , 2014, 1404.1691.
[27] Serge Vaudenay,et al. Faster Sieving Algorithm for Approximate SVP with Constant Approximation Factors , 2019, IACR Cryptol. ePrint Arch..
[28] Damien Stehlé,et al. Solving the Shortest Lattice Vector Problem in Time 22.465n , 2009, IACR Cryptol. ePrint Arch..
[29] Sanjeev Arora. Probabilistic checking of proofs and hardness of approximation problems , 1995 .
[30] Subhash Khot,et al. Hardness of approximating the shortest vector problem in lattices , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.
[31] Divesh Aggarwal,et al. (Gap/S)ETH hardness of SVP , 2017, STOC.
[32] Guy Kindler,et al. Approximating CVP to Within Almost-Polynomial Factors is NP-Hard , 2003, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).
[33] Divesh Aggarwal,et al. Dimension-Preserving Reductions Between SVP and CVP in Different p-Norms , 2021, SODA.
[34] Priyanka Mukhopadhyay,et al. Faster provable sieving algorithms for the Shortest Vector Problem and the Closest Vector Problem on lattices in 𝓁p norm , 2019, Algorithms.
[35] Noah Stephens-Davidowitz,et al. Discrete Gaussian Sampling Reduces to CVP and SVP , 2015, SODA.
[36] Daniel Dadush. A O(1/ε 2) n -Time Sieving Algorithm for Approximate Integer Programming , 2012, LATIN.
[37] Miklós Ajtai,et al. Generating hard instances of lattice problems (extended abstract) , 1996, STOC '96.
[38] Jean-Pierre Seifert,et al. Approximating Shortest Lattice Vectors is Not Harder Than Approximating Closest Lattice Vectors , 1999, Electron. Colloquium Comput. Complex..
[39] Divesh Aggarwal,et al. Just Take the Average! An Embarrassingly Simple $2^n$-Time Algorithm for SVP (and CVP) , 2017, SOSA.
[40] Ravi Kumar,et al. Sampling short lattice vectors and the closest lattice vector problem , 2002, Proceedings 17th IEEE Annual Conference on Computational Complexity.
[41] Hendrik W. Lenstra,et al. Integer Programming with a Fixed Number of Variables , 1983, Math. Oper. Res..
[42] Gilles Pisier,et al. A new approach to several results of V. Milman. , 1989 .
[43] Vitali Milman,et al. Isomorphic symmetrization and geometric inequalities , 1988 .
[44] Daniele Micciancio,et al. The shortest vector in a lattice is hard to approximate to within some constant , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).
[45] G. Pisier. Sur les espaces de Banach $K$-convexes , 1980 .
[46] Daniel Dadush,et al. Solving the Closest Vector Problem in 2^n Time -- The Discrete Gaussian Strikes Again! , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.
[47] László Babai,et al. On Lovász’ lattice reduction and the nearest lattice point problem , 1986, Comb..
[48] Daniel Dadush,et al. Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract , 2014, STOC.
[49] Johannes Blömer,et al. Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima , 2007, ICALP.
[50] Craig Gentry,et al. Fully homomorphic encryption using ideal lattices , 2009, STOC '09.
[51] Oded Regev,et al. Lattice problems and norm embeddings , 2006, STOC '06.
[52] Noah Stephens-Davidowitz,et al. Kissing Numbers and Transference Theorems from Generalized Tail Bounds , 2018, SIAM J. Discret. Math..
[53] Daniele Micciancio,et al. A Deterministic Single Exponential Time Algorithm for Most Lattice Problems based on Voronoi Cell Computations ( Extended Abstract ) , 2009 .