Approximately Certifying the Restricted Isometry Property is Hard

A matrix is said to possess the restricted isometry property (RIP), if it acts as an approximate isometry when restricted to sparse vectors. Previous work has shown it to be NP-hard to determine whether a matrix possess this property, but only in a narrow range of parameters. In this paper, we show that it is NP-hard to make this determination for any accuracy parameter, even when we restrict ourselves to instances which are either RIP or far from being RIP. This result implies that it is NP-hard to approximate the range of parameters for which a matrix possesses the RIP with accuracy better than some constant. Ours is the first work to prove such a claim without any additional assumptions.

[1]  Anru Zhang,et al.  Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices , 2013, IEEE Transactions on Information Theory.

[2]  Ronald A. DeVore,et al.  Deterministic constructions of compressed sensing matrices , 2007, J. Complex..

[3]  Stephen J. Dilworth,et al.  Explicit constructions of RIP matrices and related problems , 2010, ArXiv.

[4]  Jared Tanner,et al.  Explorer Compressed Sensing : How Sharp Is the Restricted Isometry Property ? , 2011 .

[5]  Mihalis Yannakakis,et al.  Optimization, approximation, and complexity classes , 1991, STOC '88.

[6]  Oded Regev,et al.  The Restricted Isometry Property of Subsampled Fourier Matrices , 2015, SODA.

[7]  Pascal Koiran,et al.  Hidden Cliques and the Certification of the Restricted Isometry Property , 2012, IEEE Transactions on Information Theory.

[8]  Emmanuel J. Cand The Restricted Isometry Property and Its Implications for Compressed Sensing , 2008 .

[9]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[10]  Gene H. Golub,et al.  Matrix computations , 1983 .

[11]  Marc E. Pfetsch,et al.  The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing , 2012, IEEE Transactions on Information Theory.

[12]  Thomas J. Schaefer,et al.  The complexity of satisfiability problems , 1978, STOC.

[13]  E. Candès The restricted isometry property and its implications for compressed sensing , 2008 .

[14]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[15]  Yaniv Plan,et al.  Average-case hardness of RIP certification , 2016, NIPS.

[16]  Sanjeev Khanna,et al.  The Optimization Complexity of Constraint Satisfaction Problems , 1996, Electron. Colloquium Comput. Complex..

[17]  Dustin G. Mixon,et al.  Certifying the Restricted Isometry Property is Hard , 2012, IEEE Transactions on Information Theory.

[18]  Carsten Lund,et al.  Proof verification and hardness of approximation problems , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.

[19]  Dustin G. Mixon,et al.  The Road to Deterministic Matrices with the Restricted Isometry Property , 2012, Journal of Fourier Analysis and Applications.

[20]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[21]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[22]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[23]  J. Schur Bemerkungen zur Theorie der beschränkten Bilinearformen mit unendlich vielen Veränderlichen. , 1911 .

[24]  Yi Wu,et al.  Computational Complexity of Certifying Restricted Isometry Property , 2014, APPROX-RANDOM.

[25]  Anru Zhang,et al.  Sharp RIP bound for sparse signal and low-rank matrix recovery , 2013 .