Parameterized Intractability of Even Set and Shortest Vector Problem

The $k$-Even Set problem is a parameterized variant of the Minimum Distance Problem of linear codes over $\mathbb F_2$, which can be stated as follows: given a generator matrix $\mathbf A$ and an integer $k$, determine whether the code generated by $\mathbf A$ has distance at most $k$, or in other words, whether there is a nonzero vector $\mathbf{x}$ such that $\mathbf A \mathbf{x}$ has at most $k$ nonzero coordinates. The question of whether $k$-Even Set is fixed parameter tractable (FPT) parameterized by the distance $k$ has been repeatedly raised in literature; in fact, it is one of the few remaining open questions from the seminal book of Downey and Fellows (1999). In this work, we show that $k$-Even Set is W[1]-hard under randomized reductions. We also consider the parameterized $k$-Shortest Vector Problem (SVP), in which we are given a lattice whose basis vectors are integral and an integer $k$, and the goal is to determine whether the norm of the shortest vector (in the $\ell_p$ norm for some fixed $p$) is at most $k$. Similar to $k$-Even Set, understanding the complexity of this problem is also a long-standing open question in the field of Parameterized Complexity. We show that, for any $p > 1$, $k$-SVP is W[1]-hard to approximate (under randomized reductions) to some constant factor.

[1]  Irit Dinur,et al.  Mildly exponential reduction from gap 3SAT to polynomial-gap label-cover , 2016, Electron. Colloquium Comput. Complex..

[2]  Qi Cheng,et al.  A Deterministic Reduction for the Gap Minimum Distance Problem , 2012, IEEE Trans. Inf. Theory.

[3]  Daniele Micciancio The Shortest Vector in a Lattice is Hard to Approximate to within Some Constant , 2000, SIAM J. Comput..

[4]  Subhash Khot,et al.  A Simple Deterministic Reduction for the Gap Minimum Distance of Code Problem , 2014, IEEE Transactions on Information Theory.

[5]  Dwijendra K. Ray-Chaudhuri,et al.  Binary mixture flow with free energy lattice Boltzmann methods , 2022, arXiv.org.

[6]  Jacques Stern,et al.  The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations , 1997, J. Comput. Syst. Sci..

[7]  Oded Regev,et al.  Lattice problems and norm embeddings , 2006, STOC '06.

[8]  Subhash Khot,et al.  Hardness of approximating the shortest vector problem in lattices , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[9]  Divesh Aggarwal,et al.  (Gap/S)ETH hardness of SVP , 2017, STOC.

[10]  Guy Kindler,et al.  Approximating CVP to Within Almost-Polynomial Factors is NP-Hard , 1998, Electron. Colloquium Comput. Complex..

[11]  Cynthia Dwork,et al.  A public-key cryptosystem with worst-case/average-case equivalence , 1997, STOC '97.

[12]  Alexander Golovnev,et al.  On the Quantitative Hardness of CVP , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).

[13]  Oded Regev,et al.  Tensor-based hardness of the shortest vector problem to within almost polynomial factors , 2007, STOC '07.

[14]  László Lovász,et al.  Factoring polynomials with rational coefficients , 1982 .

[15]  Jin-Yi Cai,et al.  Approximating the SVP to within a Factor (1+1/dimxi) Is NP-Hard under Randomized Reductions , 1999, J. Comput. Syst. Sci..

[16]  Miklós Ajtai,et al.  The shortest vector problem in L2 is NP-hard for randomized reductions (extended abstract) , 1998, STOC '98.

[17]  Alexander Vardy,et al.  The Parametrized Complexity of Some Fundamental Problems in Coding Theory , 1999, SIAM J. Comput..

[18]  Pasin Manurangsi,et al.  On the parameterized complexity of approximating dominating set , 2017, Electron. Colloquium Comput. Complex..

[19]  Alexander Vardy,et al.  Algorithmic complexity in coding theory and the minimum distance problem , 1997, STOC '97.

[20]  Daniele Micciancio,et al.  Inapproximability of the Shortest Vector Problem: Toward a Deterministic Reduction , 2012, Theory Comput..

[21]  Alexander Vardy,et al.  The intractability of computing the minimum distance of a code , 1997, IEEE Trans. Inf. Theory.

[22]  Oded Regev,et al.  The Learning with Errors Problem (Invited Survey) , 2010, 2010 IEEE 25th Annual Conference on Computational Complexity.

[23]  Madhu Sudan,et al.  Hardness of approximating the minimum distance of a linear code , 1999, IEEE Trans. Inf. Theory.

[24]  Daniele Micciancio,et al.  Locally Dense Codes , 2014, 2014 IEEE 29th Conference on Computational Complexity (CCC).

[25]  Oded Regev,et al.  On lattices, learning with errors, random linear codes, and cryptography , 2005, STOC '05.

[26]  Jacques Stern,et al.  Approximating the Number of Error Locations within a Constant Ratio is NP-complete , 1993, AAECC.

[27]  Pasin Manurangsi Tight Running Time Lower Bounds for Strong Inapproximability of Maximum k-Coverage, Unique Set Cover and Related Problems (via t-Wise Agreement Testing Theorem) , 2020, SODA.

[28]  Erik D. Demaine,et al.  07281 Open Problems -- Structure Theory and FPT Algorithmcs for Graphs, Digraphs and Hypergraphs , 2007, Structure Theory and FPT Algorithmics for Graphs, Digraphs and Hypergraphs.

[29]  Irit Dinur,et al.  Approximating SVPinfinity to within almost-polynomial factors is NP-hard , 1998, Theor. Comput. Sci..

[30]  Elwyn R. Berlekamp,et al.  On the inherent intractability of certain coding problems (Corresp.) , 1978, IEEE Trans. Inf. Theory.

[31]  Shafi Goldwasser,et al.  Complexity of lattice problems , 2002 .

[32]  Fedor V. Fomin,et al.  Randomization in Parameterized Complexity (Dagstuhl Seminar 17041) , 2017, Dagstuhl Reports.

[33]  Daniele Micciancio,et al.  The hardness of the closest vector problem with preprocessing , 2001, IEEE Trans. Inf. Theory.

[34]  Bingkai Lin,et al.  The Parameterized Complexity of the k-Biclique Problem , 2018, J. ACM.

[35]  Oded Regev,et al.  Lattice-Based Cryptography , 2006, CRYPTO.

[36]  Miklós Ajtai,et al.  Generating hard instances of lattice problems (extended abstract) , 1996, STOC '96.

[37]  Jean-Pierre Seifert,et al.  Approximating Shortest Lattice Vectors is Not Harder Than Approximating Closest Lattice Vectors , 1999, Electron. Colloquium Comput. Complex..

[38]  Oded Goldreich,et al.  On Promise Problems: A Survey , 2006, Essays in Memory of Shimon Even.

[39]  Hendrik W. Lenstra,et al.  Integer Programming with a Fixed Number of Variables , 1983, Math. Oper. Res..

[40]  Ameet Gadekar,et al.  On the hardness of learning sparse parities , 2015, Electron. Colloquium Comput. Complex..

[41]  Petr A. Golovach,et al.  Parameterized complexity of generalized domination problems , 2012, Discret. Appl. Math..

[42]  Dániel Marx,et al.  Data Reduction and Problem Kernels (Dagstuhl Seminar 12241) , 2012, Dagstuhl Reports.