American Institute of Mathematical Sciences

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August  2016, 10(3): 689-709. doi: 10.3934/ipi.2016017

An efficient projection method for nonlinear inverse problems with sparsity constraints

Deren Han 1, , Zehui Jia 1, , Yongzhong Song 1, and  David Z. W. Wang 2,

1. 

School of Mathematical Sciences, Key Laboratory for NSLSCS of Jiangsu Province, Nanjing Normal University, Nanjing 210023, China, China, China
2. 

School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore

Received  July 2015 Revised  March 2016 Published  August 2016

In this paper, we propose a modification of the accelerated projective steepest descent method for solving nonlinear inverse problems with an $\ell_1$ constraint on the variable, which was recently proposed by Teschke and Borries (2010 Inverse Problems 26 025007). In their method, there are some parameters need to be estimated, which is a difficult task for many applications. We overcome this difficulty by introducing a self-adaptive strategy in choosing the parameters. Theoretically, the convergence of their algorithm was guaranteed under the assumption that the underlying mapping $F$ is twice Fréchet differentiable together with some other conditions, while we can prove weak and strong convergence of the proposed algorithm under the condition that $F$ is Fréchet differentiable, which is a relatively weak condition. We also report some preliminary computational results and compare our algorithm with that of Teschke and Borries, which indicate that our method is efficient.
Keywords: weak and strong convergence., sparsity, self-adaptive, nonlinear inverse problems, Projection method.
Mathematics Subject Classification: Primary: 90C33, 94A08, 97M5.
Citation: Deren Han, Zehui Jia, Yongzhong Song, David Z. W. Wang. An efficient projection method for nonlinear inverse problems with sparsity constraints. Inverse Problems & Imaging, 2016, 10 (3) : 689-709. doi: 10.3934/ipi.2016017
References:
[1]

J. F. Bonnans and A. Shapiro, Perturbation Analysis of Optimization Problems, Springer-Verlag, New York, 2000. doi: 10.1007/978-1-4612-1394-9.  Google Scholar

[2]

K. Bredies, D. A. Lorenz and P. Maass, A generalized conditional gradient method and its connection to an iterative shrinkage method, Comput. Optim. Appl., 42 (2009), 173-193. doi: 10.1007/s10589-007-9083-3.  Google Scholar

[3]

X. J. Cai, G. Y. Gu and B. S. He, A proximal point algorithm revisit on the alternating direction method of multipliers, Sci. China Math., 56 (2013), 2179-2186. doi: 10.1007/s11425-013-4683-0.  Google Scholar

[4]

I. Daubechies, M. Defrise and C. DeMol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Commun. Pure Appl. Math., 57 (2004), 1413-1457. doi: 10.1002/cpa.20042.  Google Scholar

[5]

I. Daubechies, M. Fornasier and I. Loris, Accelerated projected gradient methods for linear inverse problems with sparsity constraints, J. Fourier Anal. Appl., 14 (2008), 764-792. doi: 10.1007/s00041-008-9039-8.  Google Scholar

[6]

I. Daubechies, G. Teschke and L. Vese, Iteratively solving linear inverse problems with general convex constraints, Inverse Problems Imag., 1 (2007), 29-46. doi: 10.3934/ipi.2007.1.29.  Google Scholar

[7]

I. Daubechies, G. Teschke and L. Vese, On some iterative concepts for image restoration, Adv. Imag. Electron Phys., 150 (2008), 1-51. doi: 10.1016/S1076-5670(07)00001-8.  Google Scholar

[8]

Y. C. Eldar, T. G. Dvorkind and E. Matusiak, Nonlinear and non-ideal sampling: Theory and methods, IEEE Trans. Signal Process., 56 (2008), 5874-5890. doi: 10.1109/TSP.2008.929872.  Google Scholar

[9]

H. W. Engl, M. Hanke and A. Neubauer, Regularization of Inverse Problems, Springer, Netherlands, 1996. doi: 10.1007/978-94-009-1740-8.  Google Scholar

[10]

F. Facchinei and J. S. Pang, Finite-Dimensional Variational Inequalities and Complementarity Problems, Volumes I and II, Springer-Verlag, Berlin, 2003.  Google Scholar

[11]

M. Fornasier, Domain decomposition methods for linear inverse problems with sparsity constraints, Inverse Problems, 23 (2007), 2505-2526. doi: 10.1088/0266-5611/23/6/014.  Google Scholar

[12]

M. A. T. Figueiredo, R. D. Nowak and S. J. Wright, Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE J. Sel. Top. Signal Process., 1 (2007), 586-597. doi: 10.1109/JSTSP.2007.910281.  Google Scholar

[13]

M. Fornasier and H. Rauhut, Recovery algorithms for vector valued data with joint sparsity constraint, SIAM J. Numer. Anal., 46 (2008), 577-613. doi: 10.1137/0606668909.  Google Scholar

[14]

Z. L. Ge, G. Qian and D. R. Han, Global convergence of an inexact operator splitting method for monotone variational inequalities, J. Ind. Man. Optim., 7 (2011), 1013-1026. doi: 10.3934/jimo.2011.7.1013.  Google Scholar

[15]

B. S. He, H. Yang, Q. Meng and D. R. Han, Modified Goldstein-Levitin-Polyak projection method for asymmetric strong monotone variational inequality, J. Optim. Theory Appl., 112 (2002), 129-143. doi: 10.1023/A:1013048729944.  Google Scholar

[16]

T. Hein and K. S. Kazimierski, Accelerated Landweber iteration in Banach spaces, Inverse Problems, 26 (2010), 055002 (17pp). doi: 10.1088/0266-5611/26/5/055002.  Google Scholar

[17]

B. Kaltenbacher, F. Schöpfer and T. Schuster, Iterative methods for nonlinear ill-posed problems in Banach spaces: convergence and applications to parameter identification problems, Inverse Problems, 25 (2009), 065003 (19pp). doi: 10.1088/0266-5611/25/6/065003.  Google Scholar

[18]

R. Ramlau and G. Teschke, Tikhonov replacement functionals for iteratively solving nonlinear operator equations, Inverse Problems, 21 (2005), 1571-1592. doi: 10.1088/0266-5611/21/5/005.  Google Scholar

[19]

R. Ramlau and G. Teschke, A Tikhonov-based projection iteration for nonlinear Ill-posed problems with sparsity constraints, Numer. Math., 104 (2006), 177-203. doi: 10.1007/s00211-006-0016-3.  Google Scholar

[20]

G. Teschke, Multi-frame representations in linear inverse problems with mixed multi-constraints, Appl. Comput. Harmon. Anal., 22 (2007), 43-60. doi: 10.1016/j.acha.2006.05.003.  Google Scholar

[21]

G. Teschke and C. Borries, Accelerated projected steepest descent method for nonlinear inverse problems with sparsity constraints, Inverse Problems, 26 (2010), 025007 (23pp). doi: 10.1088/0266-5611/26/2/025007.  Google Scholar

[22]

E. van den Berg and M. P. Friedlander, Probing the Pareto frontier for basic pursuit solutions, SIAM J. Sci. Comput., 31 (2008), 890-912. doi: 10.1137/080714488.  Google Scholar

[23]

E. Zeidler, Nonlinear Functional Analysis and Its Applications III, Springer, New York, 1985. doi: 10.1007/978-1-4612-5020-3.  Google Scholar

[24]

E. Zeidler, Nonlinear Functional Analysis and Its Applications I, Springer, New York, 1986. doi: 10.1007/978-1-4612-4838-5.  Google Scholar

[25]

W. X. Zhang, D. R. Han and Z. B. Li, A self-adaptive projection method for solving the multiple-sets split feasibility problem, Inverse Problems, 25 (2009), 115001 (16pp). doi: 10.1088/0266-5611/25/11/115001.  Google Scholar

[26]

T. Zhu and Z. Yu, A simple proof for some important properties of the projection mapping, Mathematical Inequalities and Applications, 7 (2004), 453-456. doi: 10.7153/mia-07-45.  Google Scholar

show all references

References:
[1]

J. F. Bonnans and A. Shapiro, Perturbation Analysis of Optimization Problems, Springer-Verlag, New York, 2000. doi: 10.1007/978-1-4612-1394-9.  Google Scholar

[2]

K. Bredies, D. A. Lorenz and P. Maass, A generalized conditional gradient method and its connection to an iterative shrinkage method, Comput. Optim. Appl., 42 (2009), 173-193. doi: 10.1007/s10589-007-9083-3.  Google Scholar

[3]

X. J. Cai, G. Y. Gu and B. S. He, A proximal point algorithm revisit on the alternating direction method of multipliers, Sci. China Math., 56 (2013), 2179-2186. doi: 10.1007/s11425-013-4683-0.  Google Scholar

[4]

I. Daubechies, M. Defrise and C. DeMol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Commun. Pure Appl. Math., 57 (2004), 1413-1457. doi: 10.1002/cpa.20042.  Google Scholar

[5]

I. Daubechies, M. Fornasier and I. Loris, Accelerated projected gradient methods for linear inverse problems with sparsity constraints, J. Fourier Anal. Appl., 14 (2008), 764-792. doi: 10.1007/s00041-008-9039-8.  Google Scholar

[6]

I. Daubechies, G. Teschke and L. Vese, Iteratively solving linear inverse problems with general convex constraints, Inverse Problems Imag., 1 (2007), 29-46. doi: 10.3934/ipi.2007.1.29.  Google Scholar

[7]

I. Daubechies, G. Teschke and L. Vese, On some iterative concepts for image restoration, Adv. Imag. Electron Phys., 150 (2008), 1-51. doi: 10.1016/S1076-5670(07)00001-8.  Google Scholar

[8]

Y. C. Eldar, T. G. Dvorkind and E. Matusiak, Nonlinear and non-ideal sampling: Theory and methods, IEEE Trans. Signal Process., 56 (2008), 5874-5890. doi: 10.1109/TSP.2008.929872.  Google Scholar

[9]

H. W. Engl, M. Hanke and A. Neubauer, Regularization of Inverse Problems, Springer, Netherlands, 1996. doi: 10.1007/978-94-009-1740-8.  Google Scholar

[10]

F. Facchinei and J. S. Pang, Finite-Dimensional Variational Inequalities and Complementarity Problems, Volumes I and II, Springer-Verlag, Berlin, 2003.  Google Scholar

[11]

M. Fornasier, Domain decomposition methods for linear inverse problems with sparsity constraints, Inverse Problems, 23 (2007), 2505-2526. doi: 10.1088/0266-5611/23/6/014.  Google Scholar

[12]

M. A. T. Figueiredo, R. D. Nowak and S. J. Wright, Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE J. Sel. Top. Signal Process., 1 (2007), 586-597. doi: 10.1109/JSTSP.2007.910281.  Google Scholar

[13]

M. Fornasier and H. Rauhut, Recovery algorithms for vector valued data with joint sparsity constraint, SIAM J. Numer. Anal., 46 (2008), 577-613. doi: 10.1137/0606668909.  Google Scholar

[14]

Z. L. Ge, G. Qian and D. R. Han, Global convergence of an inexact operator splitting method for monotone variational inequalities, J. Ind. Man. Optim., 7 (2011), 1013-1026. doi: 10.3934/jimo.2011.7.1013.  Google Scholar

[15]

B. S. He, H. Yang, Q. Meng and D. R. Han, Modified Goldstein-Levitin-Polyak projection method for asymmetric strong monotone variational inequality, J. Optim. Theory Appl., 112 (2002), 129-143. doi: 10.1023/A:1013048729944.  Google Scholar

[16]

T. Hein and K. S. Kazimierski, Accelerated Landweber iteration in Banach spaces, Inverse Problems, 26 (2010), 055002 (17pp). doi: 10.1088/0266-5611/26/5/055002.  Google Scholar

[17]

B. Kaltenbacher, F. Schöpfer and T. Schuster, Iterative methods for nonlinear ill-posed problems in Banach spaces: convergence and applications to parameter identification problems, Inverse Problems, 25 (2009), 065003 (19pp). doi: 10.1088/0266-5611/25/6/065003.  Google Scholar

[18]

R. Ramlau and G. Teschke, Tikhonov replacement functionals for iteratively solving nonlinear operator equations, Inverse Problems, 21 (2005), 1571-1592. doi: 10.1088/0266-5611/21/5/005.  Google Scholar

[19]

R. Ramlau and G. Teschke, A Tikhonov-based projection iteration for nonlinear Ill-posed problems with sparsity constraints, Numer. Math., 104 (2006), 177-203. doi: 10.1007/s00211-006-0016-3.  Google Scholar

[20]

G. Teschke, Multi-frame representations in linear inverse problems with mixed multi-constraints, Appl. Comput. Harmon. Anal., 22 (2007), 43-60. doi: 10.1016/j.acha.2006.05.003.  Google Scholar

[21]

G. Teschke and C. Borries, Accelerated projected steepest descent method for nonlinear inverse problems with sparsity constraints, Inverse Problems, 26 (2010), 025007 (23pp). doi: 10.1088/0266-5611/26/2/025007.  Google Scholar

[22]

E. van den Berg and M. P. Friedlander, Probing the Pareto frontier for basic pursuit solutions, SIAM J. Sci. Comput., 31 (2008), 890-912. doi: 10.1137/080714488.  Google Scholar

[23]

E. Zeidler, Nonlinear Functional Analysis and Its Applications III, Springer, New York, 1985. doi: 10.1007/978-1-4612-5020-3.  Google Scholar

[24]

E. Zeidler, Nonlinear Functional Analysis and Its Applications I, Springer, New York, 1986. doi: 10.1007/978-1-4612-4838-5.  Google Scholar

[25]

W. X. Zhang, D. R. Han and Z. B. Li, A self-adaptive projection method for solving the multiple-sets split feasibility problem, Inverse Problems, 25 (2009), 115001 (16pp). doi: 10.1088/0266-5611/25/11/115001.  Google Scholar

[26]

T. Zhu and Z. Yu, A simple proof for some important properties of the projection mapping, Mathematical Inequalities and Applications, 7 (2004), 453-456. doi: 10.7153/mia-07-45.  Google Scholar

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