Download A Mathematical Introduction to Compressive Sensing by Simon Foucart PDF

By Simon Foucart

At the intersection of arithmetic, engineering, and desktop technological know-how sits the thriving box of compressive sensing. according to the basis that facts acquisition and compression will be played at the same time, compressive sensing reveals purposes in imaging, sign processing, and lots of different domain names. within the parts of utilized arithmetic, electric engineering, and theoretical desktop technology, an explosion of study job has already the theoretical effects that highlighted the potency of the elemental rules. The stylish principles at the back of those ideas also are of self reliant curiosity to natural mathematicians.

A Mathematical creation to Compressive Sensing supplies a close account of the center idea upon which the sector is construct. With in basic terms reasonable necessities, it's a superb textbook for graduate classes in arithmetic, engineering, and computing device technology. It additionally serves as a competent source for practitioners and researchers in those disciplines who are looking to collect a cautious realizing of the topic. A Mathematical advent to Compressive Sensing makes use of a mathematical standpoint to give the middle of the idea underlying compressive sensing.

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The system (a1 , . . , aN ) is often called a dictionary. Note that this system may be linearly dependent (redundant) since we allow N > m. Redundancy may be desired when linearly independence is too restrictive. For instance, in time–frequency analysis, bases of time–frequency shifts elements are only possible if the generator has poor time–frequency concentration— this is the Balian–Low theorem. Unions of several bases are also of interest. In N such situations, a representation y = j=1 xj aj is not unique.

When it is present, one may use the quadratically constrained 1 -minimization minimize z 1 subject to Az − y 2 ≤ η, yet this requires an estimation of the noise level η (other algorithms do not require an estimation of η, but they require an estimation of the sparsity level s instead). 15) valid under RIP. 2) can also be performed in the presence of measurement error using Gaussian measurement matrices. 15) even without knowledge of the noise level η. Subgaussian random matrices are of limited practical use, because specific applications may impose a structure on the measurement matrix that totally random matrices lack.

In the absence of the operator Hs , this is well known in the area of inverse problems as Landweber iterations. Applying Hs ensures sparsity of xn at each iteration. We will finally present the hard thresholding pursuit algorithm which combines iterative hard thresholding with an orthogonal projection step. Chapter 4 is devoted to the analysis of basis pursuit ( 1 -minimization). First, we derive conditions for the exact recovery of sparse vectors. The null space property of order s is a necessary and sufficient condition (on the matrix A) for the success of exact recovery of all s-sparse vectors x from y = Ax via 1 -minimization.

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