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February  2015, 9(1): 27-53. doi: 10.3934/ipi.2015.9.27

A scalable algorithm for MAP estimators in Bayesian inverse problems with Besov priors

1. 

Department of Aerospace Engineering and Engineering Mechanics, Institute for Computational Engineering & Sciences, The University of Texas at Austin, Austin, TX 78712

2. 

Institute for Computational Engineering & Sciences, Jackson School of Geosciences, and Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712

Received  November 2012 Revised  September 2013 Published  January 2015

We present a scalable solver for approximating the maximum a posteriori (MAP) point of Bayesian inverse problems with Besov priors based on wavelet expansions with random coefficients. It is a subspace trust region interior reflective Newton conjugate gradient method for bound constrained optimization problems. The method combines the rapid locally-quadratic convergence rate properties of Newton's method, the effectiveness of trust region globalization for treating ill-conditioned problems, and the Eisenstat--Walker idea of preventing oversolving. We demonstrate the scalability of the proposed method on two inverse problems: a deconvolution problem and a coefficient inverse problem governed by elliptic partial differential equations. The numerical results show that the number of Newton iterations is independent of the number of wavelet coefficients $n$ and the computation time scales linearly in $n$. It will be numerically shown, under our implementations, that the proposed solver is two times faster than the split Bregman approach, and it is an order of magnitude less expensive than the interior path following primal-dual method. Our results also confirm the fact that the Besov $\mathbb{B}_{11}^1$ prior is sparsity promoting, discretization-invariant, and edge-preserving for both imaging and inverse problems governed by partial differential equations.
Citation: Tan Bui-Thanh, Omar Ghattas. A scalable algorithm for MAP estimators in Bayesian inverse problems with Besov priors. Inverse Problems & Imaging, 2015, 9 (1) : 27-53. doi: 10.3934/ipi.2015.9.27
References:
[1]

M. Benzi, G. H. Golub and J. Liesen, Numerical solution of saddle point problems,, Acta Numerica, 14 (2005), 1.  doi: 10.1017/S0962492904000212.  Google Scholar

[2]

L. Bergamaschi, J. Gondzio and G. Zilli, Preconditioning indefinite systems in interior point methods for optimization,, Computational Optimization and Applications, 28 (2004), 149.  doi: 10.1023/B:COAP.0000026882.34332.1b.  Google Scholar

[3]

M. A. Branch, T. F. Coleman and Y. Li, A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems,, SIAM Journal on Scientific Computing, 21 (1999), 1.  doi: 10.1137/S1064827595289108.  Google Scholar

[4]

T. Bui-Thanh, Model-Constrained Optimization Methods for Reduction of Parameterized Large-Scale Systems,, PhD thesis, (2007).   Google Scholar

[5]

T. Bui-Thanh and O. Ghattas, Analysis of the Hessian for inverse scattering problems. Part I: Inverse shape scattering of acoustic waves,, Inverse Problems, 28 (2012).  doi: 10.1088/0266-5611/28/5/055001.  Google Scholar

[6]

________, Analysis of the Hessian for inverse scattering problems. Part II: Inverse medium scattering of acoustic waves,, Inverse Problems, 28 (2012).   Google Scholar

[7]

________, Analysis of the Hessian for inverse scattering problems. Part III: Inverse medium scattering of electromagnetic waves., Submitted to Inverse Problems, (2012).   Google Scholar

[8]

________, A scaled stochastic Newton algorithm for Markov chain Monte Carlo simulations,, Submitted to SIAM Journal of Uncertainty Quantification, (2012).   Google Scholar

[9]

T. Bui-Thanh, K. Willcox and O. Ghattas, Model reduction for large-scale systems with high-dimensional parametric input space,, SIAM Journal on Scientific Computing, 30 (2008), 3270.  doi: 10.1137/070694855.  Google Scholar

[10]

T. F. Coleman and Y. Li, An interior trust region approach for nonlinear minimization subject to bounds,, SIAM Journal on Optimization, 6 (1996), 418.  doi: 10.1137/0806023.  Google Scholar

[11]

S. Comelli, A Novel Class of Priors for Edge-Preserving Methods in Bayesian Inversion,, master's thesis, (2011).   Google Scholar

[12]

M. Dashti, S. Harris and A. Stuart, Besov priors for Bayesian inverse problems,, Inverse Problems and Imaging, 6 (2012), 183.  doi: 10.3934/ipi.2012.6.183.  Google Scholar

[13]

I. Daubechies, Ten Lectures on Wavelets,, CBMS-NSF Regional Conference Series in Applied Mathematics, (1992).  doi: 10.1137/1.9781611970104.  Google Scholar

[14]

I. Daubechies, M. Defrise and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,, Communications on Pure and Applied Mathematics, 57 (2004), 1413.  doi: 10.1002/cpa.20042.  Google Scholar

[15]

J. E. Dennis and L. N. Vicente, Trust-region interior-point algorithms for minimization methods with simple bounds,, in Applied Mathematics and Parallel Computing, (1996), 97.   Google Scholar

[16]

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 Journal of Selected Topics in Signal Processing, 1 (2007), 586.  doi: 10.1109/JSTSP.2007.910281.  Google Scholar

[17]

J. N. Franklin, Well-posed stochastic extensions of ill-posed linear problems,, Journal of Mathematical Analysis and Applications, 31 (1970), 682.  doi: 10.1016/0022-247X(70)90017-X.  Google Scholar

[18]

T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems,, SIAM Journal on Imaging Sciences, 2 (2009), 323.  doi: 10.1137/080725891.  Google Scholar

[19]

A. Grasmair, M. Haltmeier and O. Scherzer, Sparse regularization with $l^q$ penalty term,, Inverse Problems, 24 (2008).  doi: 10.1088/0266-5611/24/5/055020.  Google Scholar

[20]

K. Hamalainen, A. Kallonen, V. Kolehmainen, M. Lassas, K. Niinimaki and S. Siltanen, Sparse tomography,, SIAM J. Sci. Comput., 35 (2013).  doi: 10.1137/120876277.  Google Scholar

[21]

M. Heinkenschloss, M. Ulbrich and S. Ulbrich, Superlinear and quadratic convergence of affine-scaling interior-point Newton methods for problems with simple bounds without strict complementarity assumption,, Mathematical Programming, 86 (1999), 615.  doi: 10.1007/s101070050107.  Google Scholar

[22]

C. Kanzow and A. Klug, On affine-scaling interior-point Newton methods for nonlinear minimization with bound constraints,, Computational Optimization and Applications, 35 (2006), 177.  doi: 10.1007/s10589-006-6514-5.  Google Scholar

[23]

C. T. Kelley, Iterative Methods for Optimization,, SIAM, (1999).  doi: 10.1137/1.9781611970920.  Google Scholar

[24]

S.-J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, An interior-point method for large-scale $l_1$-regularized least squares,, IEEE Journal of Selected Topics in Signal Processing, 1 (2007), 606.   Google Scholar

[25]

V. Kolehmainen, M. Lassas, K. Niinimaki and S. Siltanen, Sparsity-promoting Bayesian inversion,, Inverse Problems, 28 (2012).  doi: 10.1088/0266-5611/28/2/025005.  Google Scholar

[26]

S. Lasanen, Discretizations of generalized random variables with applications to inverse problems,, Ann. Acad. Sci. Fenn. Math. Diss., 2002 (2002).   Google Scholar

[27]

M. Lassas, E. Saksman and S. Siltanen, Discretization invariant Bayesian inversion and Besov space priors,, Inverse Problems and Imaging, 3 (2009), 87.  doi: 10.3934/ipi.2009.3.87.  Google Scholar

[28]

M. S. Lehtinen, L. Päivärinta and E. Somersalo, Linear inverse problems for generalized random variables,, Inverse Problems, 5 (1989), 599.  doi: 10.1088/0266-5611/5/4/011.  Google Scholar

[29]

C.-J. Lin and J. J. Moré, Newton's method for large bound-constrained optimization problems,, SIAM Journal on Optimization, 9 (1999), 1100.  doi: 10.1137/S1052623498345075.  Google Scholar

[30]

D. A. Lorenz and D. Trede, Optimal convergence rates for Tikhonov regularization in Besov scales,, Inverse Problems, 24 (2008).  doi: 10.1088/0266-5611/24/5/055010.  Google Scholar

[31]

M. Lustig, D. Donoho and J. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging,, Journal of Magnetic Resonance Imaging, 58 (2007), 1182.  doi: 10.1002/mrm.21391.  Google Scholar

[32]

S. Mehrotra, On the implementation of a primal-dual interior point method,, SIAM Journal on Optimization, 2 (1992), 575.  doi: 10.1137/0802028.  Google Scholar

[33]

P. Piiroinen, Statistical Measurements, Experiments, and Applications,, PhD thesis, (2005).   Google Scholar

[34]

D. F. Shanno and R. J. Vanderbei, An interior-point method for nonconvex nonlinear programming,, Computational Optimization and Applications, 13 (1999), 231.  doi: 10.1023/A:1008677427361.  Google Scholar

[35]

A. M. Stuart, Inverse problems: A Bayesian perspective,, Acta Numerica, 19 (2010), 451.  doi: 10.1017/S0962492910000061.  Google Scholar

[36]

H. Triebel, Theory of Function Spaces III,, vol. 100, (2006).   Google Scholar

[37]

J. Trzasko, A. Manduca and E. Borisch, Sparse MRI reconstruction via multiscale L0-continuation,, in Proceedings of the 14th IEEE/SP Workshop o Satistical Signal Processing, (2007), 176.  doi: 10.1109/SSP.2007.4301242.  Google Scholar

[38]

B. Vexler, Adaptive finite element methods for parameter identification problems,, Contributions in Mathematical and Computational Sciences, 4 (2013), 31.  doi: 10.1007/978-3-642-30367-8_2.  Google Scholar

[39]

S. J. Wright, Primal-Dual Interior-Point Methods,, SIAM, (1997).  doi: 10.1137/1.9781611971453.  Google Scholar

[40]

C. Zhu, R. H. Byrd, P. Lu and J. Nocedal, L-bfgs-b - fortran subroutines for large-scale bound constrained optimization,, ACM Transactions on Mathematical Software, 23 (1997), 550.  doi: 10.1145/279232.279236.  Google Scholar

show all references

References:
[1]

M. Benzi, G. H. Golub and J. Liesen, Numerical solution of saddle point problems,, Acta Numerica, 14 (2005), 1.  doi: 10.1017/S0962492904000212.  Google Scholar

[2]

L. Bergamaschi, J. Gondzio and G. Zilli, Preconditioning indefinite systems in interior point methods for optimization,, Computational Optimization and Applications, 28 (2004), 149.  doi: 10.1023/B:COAP.0000026882.34332.1b.  Google Scholar

[3]

M. A. Branch, T. F. Coleman and Y. Li, A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems,, SIAM Journal on Scientific Computing, 21 (1999), 1.  doi: 10.1137/S1064827595289108.  Google Scholar

[4]

T. Bui-Thanh, Model-Constrained Optimization Methods for Reduction of Parameterized Large-Scale Systems,, PhD thesis, (2007).   Google Scholar

[5]

T. Bui-Thanh and O. Ghattas, Analysis of the Hessian for inverse scattering problems. Part I: Inverse shape scattering of acoustic waves,, Inverse Problems, 28 (2012).  doi: 10.1088/0266-5611/28/5/055001.  Google Scholar

[6]

________, Analysis of the Hessian for inverse scattering problems. Part II: Inverse medium scattering of acoustic waves,, Inverse Problems, 28 (2012).   Google Scholar

[7]

________, Analysis of the Hessian for inverse scattering problems. Part III: Inverse medium scattering of electromagnetic waves., Submitted to Inverse Problems, (2012).   Google Scholar

[8]

________, A scaled stochastic Newton algorithm for Markov chain Monte Carlo simulations,, Submitted to SIAM Journal of Uncertainty Quantification, (2012).   Google Scholar

[9]

T. Bui-Thanh, K. Willcox and O. Ghattas, Model reduction for large-scale systems with high-dimensional parametric input space,, SIAM Journal on Scientific Computing, 30 (2008), 3270.  doi: 10.1137/070694855.  Google Scholar

[10]

T. F. Coleman and Y. Li, An interior trust region approach for nonlinear minimization subject to bounds,, SIAM Journal on Optimization, 6 (1996), 418.  doi: 10.1137/0806023.  Google Scholar

[11]

S. Comelli, A Novel Class of Priors for Edge-Preserving Methods in Bayesian Inversion,, master's thesis, (2011).   Google Scholar

[12]

M. Dashti, S. Harris and A. Stuart, Besov priors for Bayesian inverse problems,, Inverse Problems and Imaging, 6 (2012), 183.  doi: 10.3934/ipi.2012.6.183.  Google Scholar

[13]

I. Daubechies, Ten Lectures on Wavelets,, CBMS-NSF Regional Conference Series in Applied Mathematics, (1992).  doi: 10.1137/1.9781611970104.  Google Scholar

[14]

I. Daubechies, M. Defrise and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,, Communications on Pure and Applied Mathematics, 57 (2004), 1413.  doi: 10.1002/cpa.20042.  Google Scholar

[15]

J. E. Dennis and L. N. Vicente, Trust-region interior-point algorithms for minimization methods with simple bounds,, in Applied Mathematics and Parallel Computing, (1996), 97.   Google Scholar

[16]

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 Journal of Selected Topics in Signal Processing, 1 (2007), 586.  doi: 10.1109/JSTSP.2007.910281.  Google Scholar

[17]

J. N. Franklin, Well-posed stochastic extensions of ill-posed linear problems,, Journal of Mathematical Analysis and Applications, 31 (1970), 682.  doi: 10.1016/0022-247X(70)90017-X.  Google Scholar

[18]

T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems,, SIAM Journal on Imaging Sciences, 2 (2009), 323.  doi: 10.1137/080725891.  Google Scholar

[19]

A. Grasmair, M. Haltmeier and O. Scherzer, Sparse regularization with $l^q$ penalty term,, Inverse Problems, 24 (2008).  doi: 10.1088/0266-5611/24/5/055020.  Google Scholar

[20]

K. Hamalainen, A. Kallonen, V. Kolehmainen, M. Lassas, K. Niinimaki and S. Siltanen, Sparse tomography,, SIAM J. Sci. Comput., 35 (2013).  doi: 10.1137/120876277.  Google Scholar

[21]

M. Heinkenschloss, M. Ulbrich and S. Ulbrich, Superlinear and quadratic convergence of affine-scaling interior-point Newton methods for problems with simple bounds without strict complementarity assumption,, Mathematical Programming, 86 (1999), 615.  doi: 10.1007/s101070050107.  Google Scholar

[22]

C. Kanzow and A. Klug, On affine-scaling interior-point Newton methods for nonlinear minimization with bound constraints,, Computational Optimization and Applications, 35 (2006), 177.  doi: 10.1007/s10589-006-6514-5.  Google Scholar

[23]

C. T. Kelley, Iterative Methods for Optimization,, SIAM, (1999).  doi: 10.1137/1.9781611970920.  Google Scholar

[24]

S.-J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, An interior-point method for large-scale $l_1$-regularized least squares,, IEEE Journal of Selected Topics in Signal Processing, 1 (2007), 606.   Google Scholar

[25]

V. Kolehmainen, M. Lassas, K. Niinimaki and S. Siltanen, Sparsity-promoting Bayesian inversion,, Inverse Problems, 28 (2012).  doi: 10.1088/0266-5611/28/2/025005.  Google Scholar

[26]

S. Lasanen, Discretizations of generalized random variables with applications to inverse problems,, Ann. Acad. Sci. Fenn. Math. Diss., 2002 (2002).   Google Scholar

[27]

M. Lassas, E. Saksman and S. Siltanen, Discretization invariant Bayesian inversion and Besov space priors,, Inverse Problems and Imaging, 3 (2009), 87.  doi: 10.3934/ipi.2009.3.87.  Google Scholar

[28]

M. S. Lehtinen, L. Päivärinta and E. Somersalo, Linear inverse problems for generalized random variables,, Inverse Problems, 5 (1989), 599.  doi: 10.1088/0266-5611/5/4/011.  Google Scholar

[29]

C.-J. Lin and J. J. Moré, Newton's method for large bound-constrained optimization problems,, SIAM Journal on Optimization, 9 (1999), 1100.  doi: 10.1137/S1052623498345075.  Google Scholar

[30]

D. A. Lorenz and D. Trede, Optimal convergence rates for Tikhonov regularization in Besov scales,, Inverse Problems, 24 (2008).  doi: 10.1088/0266-5611/24/5/055010.  Google Scholar

[31]

M. Lustig, D. Donoho and J. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging,, Journal of Magnetic Resonance Imaging, 58 (2007), 1182.  doi: 10.1002/mrm.21391.  Google Scholar

[32]

S. Mehrotra, On the implementation of a primal-dual interior point method,, SIAM Journal on Optimization, 2 (1992), 575.  doi: 10.1137/0802028.  Google Scholar

[33]

P. Piiroinen, Statistical Measurements, Experiments, and Applications,, PhD thesis, (2005).   Google Scholar

[34]

D. F. Shanno and R. J. Vanderbei, An interior-point method for nonconvex nonlinear programming,, Computational Optimization and Applications, 13 (1999), 231.  doi: 10.1023/A:1008677427361.  Google Scholar

[35]

A. M. Stuart, Inverse problems: A Bayesian perspective,, Acta Numerica, 19 (2010), 451.  doi: 10.1017/S0962492910000061.  Google Scholar

[36]

H. Triebel, Theory of Function Spaces III,, vol. 100, (2006).   Google Scholar

[37]

J. Trzasko, A. Manduca and E. Borisch, Sparse MRI reconstruction via multiscale L0-continuation,, in Proceedings of the 14th IEEE/SP Workshop o Satistical Signal Processing, (2007), 176.  doi: 10.1109/SSP.2007.4301242.  Google Scholar

[38]

B. Vexler, Adaptive finite element methods for parameter identification problems,, Contributions in Mathematical and Computational Sciences, 4 (2013), 31.  doi: 10.1007/978-3-642-30367-8_2.  Google Scholar

[39]

S. J. Wright, Primal-Dual Interior-Point Methods,, SIAM, (1997).  doi: 10.1137/1.9781611971453.  Google Scholar

[40]

C. Zhu, R. H. Byrd, P. Lu and J. Nocedal, L-bfgs-b - fortran subroutines for large-scale bound constrained optimization,, ACM Transactions on Mathematical Software, 23 (1997), 550.  doi: 10.1145/279232.279236.  Google Scholar

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