Abstract. Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery algorithm called CoSaMP that delivers the same guarantees as the best optimization-based approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage. It is likely to be extremely efficient for practical problems because it requires only matrix–vector multiplies with the sampling matrix. For compressible signals, the running time is just O(N log 2 N), where N is the length of the signal. 1.

Abstract. There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach starts with a geometric constraint on the measurement matrix Φ and then uses linear programming to decode information about x from Φx. The combinatorial approach constructs Φ and a combinatorial decoding algorithm to match. We present a unified approach to these two classes of sparse signal recovery algorithms. The unifying elements are the adjacency matrices of high-quality unbalanced expanders. We generalize the notion of Restricted Isometry Property (RIP), crucial to compressed sensing results for signal recovery, from the Euclidean norm to the ℓp norm for p ≈ 1, and then show that unbalanced expanders are essentially equivalent to RIP-p matrices. From known deterministic constructions for such matrices, we obtain new deterministic measurement matrix constructions and algorithms for signal recovery which, compared to previous deterministic algorithms, are superior in either the number of measurements or in noise tolerance. 1.

Abstract—Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length- signal containing large coefficients, our CS-BP decoding algorithm uses ( log ()) measurements and ( log 2 ()) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models. Index Terms—Bayesian inference, belief propagation, compressive sensing, fast algorithms, sparse matrices. I.

Compressed sensing is an emerging field that enables to reconstruct sparse or compressible signals from a small number of linear projections. We describe a specific measurement scheme using an LDPC-like measurement matrix, which is a real-valued analogue to LDPC techniques over a finite alphabet. We then describe the reconstruction details for mixture Gaussian signals. The technique can be extended to additional compressible signal models. 1

Over the recent years, a new approach for obtaining a succinct approximate representation of ndimensional

Abstract — Sudocodes are a new scheme for lossless compressive sampling and reconstruction of sparse signals. Consider a sparse signal x ∈ R N containing only K ≪ N non-zero values. Sudo-encoding computes the codeword y ∈ R M via the linear matrix-vector multiplication y = Φx, with K < M ≪ N. We propose a non-adaptive construction of a sparse Φ comprising only the values 0 and 1; hence the computation of y involves only sums of subsets of the elements of x. An accompanying sudodecoding strategy efficiently recovers x given y. Sudocodes require only M = O(K log(N)) measurements for exact reconstruction with worst-case computational complexity O(K log(K) log(N)). Sudocodes can be used as erasure codes for real-valued data and have potential applications in peer-to-peer networks and distributed data storage systems. They are also easily extended to signals that are sparse in arbitrary bases. I.

Abstract. We can recover approximately a sparse signal with limited noise, i.e, a vector of length d with at least d − m zeros or near-zeros, using little more than m log(d) nonadaptive linear measurements rather than the d measurements needed to recover an arbitrary signal of length d. Several research communities are interested in techniques for measuring and recovering such signals and a variety of approaches have been proposed. We focus on two important properties of such algorithms. • Uniformity. A single measurement matrix should work simultaneously for all signals. • Computational Efficiency. The time to recover such an m-sparse signal should be close to the obvious lower bound, m log(d/m). To date, algorithms for signal recovery that provide a uniform measurement matrix with approximately the optimal number of measurements, such as first proposed by Donoho and his collaborators, and, separately, by Candès and Tao, are based on linear programming and require time poly(d) instead of m polylog(d). On the other hand, fast decoding algorithms to date from the Theoretical Computer Science and Database communities fail with probability at least 1 / poly(d), whereas we need failure probability no more than around 1/d m to achieve a uniform failure guarantee. This paper develops a new method for recovering m-sparse signals that is simultaneously uniform

We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a high-dimensional vector x from its lower-dimensional sketch Ax. A popular way of performing this recovery is by finding x # such that Ax = Ax # , and �x # �1 is minimal. It is known that this approach “works” if A is a random dense matrix, chosen from a proper distribution. In this paper, we investigate this procedure for the case where A is binary and very sparse. We show that, both in theory and in practice, sparse matrices are essentially as “good” as the dense ones. At the same time, sparse binary matrices provide additional benefits, such as reduced encoding and decoding time.

We study the problem of estimating the best B term Fourier representation for a given frequency-sparse signal (i.e., vector) A of length N≫B. More explicitly, we investigate how to deterministically identify B of the largest magnitude frequencies of Â, and estimate their coefficients, in polynomial(B, log N) time. Randomized sub-linear time algorithms which have a small (controllable) probability of failure for each processed signal exist for solving this problem. However, for failure intolerant applications such as those involving mission-critical hardware designed to process many signals over a long lifetime, deterministic algorithms with no probability of failure are highly desirable. In this paper we build on the deterministic Compressed Sensing results of Cormode and Muthukrishnan (CM) [26, 6, 7] in order to develop the first known deterministic sub-linear time sparse Fourier Transform algorithm suitable for failure intolerant applications. Furthermore, in the process of developing our new Fourier algorithm, we present a simplified deterministic Compressed Sensing algorithm which improves on CM’s algebraic compressibility results while simultaneously maintaining their results concerning exponential decay. 1

In this note, we prove that matrices whose entries are all 0 or 1 cannot achieve good performance with respect to the Restricted Isometry Property (RIP). Most currently known deterministic constructions of matrices satisfying the RIP fall into this category, and hence these constructions suffer inherent limitations. In particular, we show that DeVore’s construction of matrices satisfying the RIP is close to optimal once we add the constraint that all entries of the matrix are 0 or 1. 1