Block-Sparse Recovery via Convex Optimization
نویسندگان
چکیده
منابع مشابه
Sparse Recovery by Non - Convex Optimization –
In this note, we address the theoretical properties of ∆p, a class of compressed sensing decoders that rely on ℓ p minimization with p ∈ (0, 1) to recover estimates of sparse and compressible signals from incomplete and inaccurate measurements. In particular, we extend the results of Candès, Romberg and Tao [3] and Wojtaszczyk [30] regarding the decoder ∆ 1 , based on ℓ 1 minimization, to ∆p wi...
متن کاملSparse Reinforcement Learning via Convex Optimization
We propose two new algorithms for the sparse reinforcement learning problem based on different formulations. The first algorithm is an off-line method based on the alternating direction method of multipliers for solving a constrained formulation that explicitly controls the projected Bellman residual. The second algorithm is an online stochastic approximation algorithm that employs the regulari...
متن کاملBlind Sensor Calibration in Sparse Recovery Using Convex Optimization
Abstract—We investigate a compressive sensing system in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on a few unknown (but sparse) signals. We extend our earlier study on real positive gains to two generalized cases (signed real-valued gains; complex-valued gains), and show that the recovery of...
متن کاملSparse Recovery by Non-convex Optimization -- Instance Optimality
In this note, we address the theoretical properties of ∆p, a class of compressed sensing decoders that rely on lp minimization with 0 < p < 1 to recover estimates of sparse and compressible signals from incomplete and inaccurate measurements. In particular, we extend the results of Candès, Romberg and Tao [4] and Wojtaszczyk [30] regarding the decoder ∆1, based on l 1 minimization, to ∆p with 0...
متن کاملPerformance Analysis of Joint-Sparse Recovery from Multiple Measurements and Prior Information via Convex Optimization
We address the problem of compressed sensing with multiple measurement vectors associated with prior information in order to better reconstruct an original sparse matrix signal. l2,1− l2,1 minimization is used to emphasize co-sparsity property and similarity between matrix signal and prior information. We then derive the necessary and sufficient condition of successfully reconstructing the orig...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2012
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2012.2196694