压缩感知介绍

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压缩感知介绍压缩感知介绍压缩感知介绍压缩感知介绍
Lower Bound on Probability of Sensing Error via Fano's Inequality Donoho's message passing algorithm for compressed sensing E Table of Figures Figure 1: Fig. 2 of shannon /1/ Figure 2: A chart from /157 7 Figure 3: A chart from /157 48 Figure 4: An example of linear objective function ≤. Note that the optimal point is achieved at the perimeter of the feasible set Figure 5. The Concept of Placing Barriers At the boundary of the Feasible Set Figure 6. 4 log-barrier function l01 Figure 7: Variation of the penalized objective function as lambda is changing from 4 to O.......106 Figure 8. Relation map ofalgorithms From Donoho and Tsaig /18 l08 Figure 9: The graph 18 Figure 10: Gilbert-Varshamov Compressed Sensing Bounds for Sensing Matrices over GF(q) and The Singleton bound Figure 11: Simulation results of a C(=1200, d,=3, de=6)code with different field lEs 135 138 Figure 12: Hyperplane..... l41 igure 13: Geometrical illustration of a subgradient of a conver function f. Note that the space is n+I dimensional 142 Figure 14: Illustration of subgradients 143 Figure 15: Geometrical illustration of a subgradient of a conver function f. Note that the space is n+I dimensional Figure 16. The trajectory ofl(x, A) with n is varied from 0. 4 to 4 149 Figure 17: Illustration of the duality gap 149 Chapter I COURSE INFORMATION D. Donoho and x. Huo, Uncertainty Principles and Ideal atomic Decomposition, IEEE Trans on Info M. Elad and A. Bruckstein,"a generalized pairs of bases, IEEE Trans approac Emmanuel Candes and Terence Tao, Near optimal encoding strategies"(IEEE Trans. on Information Jacob Mattingley and Stephen Boyd, Real Michael Zibulevsky and Michael Elad, L1 Processing ns 1948 paper D. Baron, M. F. Duarte, and M. B. Wakin, "Distributed Compressive Sensing, Dror

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