(i) {Î± - Convex Optimization

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Super-resolution Stanford seminar 08-28
Computation time CS H = Fourier, Φ = Dirac m/n !=Dirac, = H=RST, 20%, m/n=25%, sparsity sparsity=5% = 5% CS H = Hadamard, Φ = Dirac m/n = 20%, sparsity = 5% !=Dirac, H=Hadamard, m/n=25%, sparsity=5% 10 4 log 2 (m) 10 3 BP−DR LARS LP−Interior Point StOMP 10 1 10 2 log 2 (m) BP−DR LARS LP−Interior Point StOMP Computation time (s) 10 0 Computation time (s) 10 2 10 1 10 0 10 −1 10 −1 6 7 8 9 10 11 12 6 7 8 9 10 11 12 13 14 Stanford seminar 08-29
Page 1 and 2: Optimization problems in compressed
Page 3 and 4: Compressed/ive Sensing Stanford sem
Page 5 and 6: Compressed/ive Sensing Common wisdo
Page 7 and 8: Compressed/ive Sensing Common wisdo
Page 9 and 10: Compressed/ive Sensing (cont’d) C
Page 11 and 12: Compressed/ive Sensing (cont’d) C
Page 13 and 14: Convex analysis and operator splitt
Page 15 and 16: Class of problems in CS (cont’d)
Page 17 and 18: Class of problems in CS (cont’d)
Page 19 and 20: Characterization Theorem 1 (i) Exis
Page 21 and 22: Operator splitting schemes Idea: re
Page 23 and 24: Proximity operators Some properties
Page 25 and 26: Example of proximity operator Stanf
Page 27 and 28: Compressed sensing optimization pro
Page 29 and 30: Characterizing Problem (P τ ) Stan
Page 31 and 32: Proximity operators of Ψ Conclusio
Page 33 and 34: DR to solve Problem (P σ ) Theorem
Page 35 and 36: DR to solve Problem (Peq) Theorem 1
Page 37 and 38: Pros and cons (P σ ) and (P eq ) h
Page 39 and 40: CS reconstruction (1) H = Fourier,
Page 41 and 42: Inpainting and CS H = Dirac, Φ = C
Page 43: Inpainting and CS H = Dirac, Φ = C
Page 47 and 48: Ongoing and future work Beyond the

(i) {Î± - Convex Optimization

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