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doi: 10.3934/dcdsb.2021054

A learning-enhanced projection method for solving convex feasibility problems

Janosch Rieger ,

School of Mathematics, Monash University, 9 Rainforest Walk, Victoria 3800 Australia

* Corresponding author: Janosch Rieger

Received  June 2020 Revised  November 2020 Early access  February 2021

We propose a generalization of the method of cyclic projections, which uses the lengths of projection steps carried out in the past to learn about the geometry of the problem and decides on this basis which projections to carry out in the future. We prove the convergence of this algorithm and illustrate its behavior in a first numerical study.

Keywords: Convex feasibility problems, method of alternating projections, Kaczmarz method, algebraic reconstruction technique, acceleration by learning.
Mathematics Subject Classification: Primary: 65K05, 52B55, 37B20; Secondary: 90C59.
Citation: Janosch Rieger. A learning-enhanced projection method for solving convex feasibility problems. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2021054
References:
[1]

F. Arroyo, E. Arroyo, X. Li and J. Zhu, The convergence of the block cyclic projection with an overrelaxation parameter for compressed sensing based tomography, J. Comput. Appl. Math., 280 (2015), 59-67.  doi: 10.1016/j.cam.2014.11.036.  Google Scholar

[2]

Z.-Z. Bai and W.-T. Wu, On greedy randomized {K}aczmarz method for solving large sparse linear systems, SIAM J. Sci. Comput., 40 (2018), A592–A606. doi: 10.1137/17M1137747.  Google Scholar

[3]

H. H. Bauschke and J. M. Borwein, On projection algorithms for solving convex feasibility problems, SIAM Rev., 38 (1996), 367-426.  doi: 10.1137/S0036144593251710.  Google Scholar

[4]

H. H. Bauschke and P. L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces., CMS Books in Mathematics/Ouvrages de Mathématiques de la SMC. Springer, New York, 2011. doi: 10.1007/978-1-4419-9467-7.  Google Scholar

[5]

H. H. Bauschke, F. Deutsch, H. Hundal and S.-H. Park, Accelerating the convergence of the method of alternating projections, Trans. Amer. Math. Soc., 355 (2003), 3433-3461.  doi: 10.1090/S0002-9947-03-03136-2.  Google Scholar

[6]

J. M. Borwein, G. Li and L. Yao, Analysis of the convergence rate for the cyclic projection algorithm applied to basic semialgebraic convex sets, SIAM J. Optim., 24 (2014), 498-527.  doi: 10.1137/130919052.  Google Scholar

[7]

L. M. Brègman, Finding the common point of convex sets by the method of successive projection, Dokl. Akad. Nauk SSSR, 162 (1965), 487-490.   Google Scholar

[8]

Y. Censor, Row-action methods for huge and sparse systems and their applications, SIAM Rev., 23 (1981), 444-466.  doi: 10.1137/1023097.  Google Scholar

[9]

F. Deutsch, Best Approximation in Inner Product Spaces, Volume 7 of CMS Books in Mathematics/Ouvrages de Mathématiques de la SMC., Springer-Verlag, New York, 2001. doi: 10.1007/978-1-4684-9298-9.  Google Scholar

[10]

T. Elfving, P. C. Hansen and T. Nikazad, Convergence analysis for column-action methods in image reconstruction, Numer. Algorithms, 74 (2017), 905-924.  doi: 10.1007/s11075-016-0176-x.  Google Scholar

[11]

L. Elsner, I. Koltracht and M. Neumann, Convergence of sequential and asynchronous nonlinear paracontractions, Numer. Math., 62 (1992), 305-319.  doi: 10.1007/BF01396232.  Google Scholar

[12]

C. Franchetti and W. Light, On the von {N}eumann alternating algorithm in {H}ilbert space, J. Math. Anal. Appl., 114 (1986), 305-314.  doi: 10.1016/0022-247X(86)90085-5.  Google Scholar

[13]

W. B. Gearhart and M. Koshy, Acceleration schemes for the method of alternating projections, J. Comput. Appl. Math., 26 (1989), 235-249.  doi: 10.1016/0377-0427(89)90296-3.  Google Scholar

[14]

G. S. M. Jansen, M. Keunecke, M. Düvel, C. Möller, D. Schmitt, W. Bennecke, F. J. S. Kappert, D. Steil, D. R. Luke, S. Steil and S. Mathias, Efficient orbital imaging based on ultrafast momentum microscopy and sparsity-driven phase retrieval, New Journal of Physics, 22 (2020), 063012. doi: 10.1088/1367-2630/ab8aae.  Google Scholar

[15]

M. Jiang and G. Wang, Convergence studies on iterative algorithms for image reconstruction, IEEE Trans. Med. Imaging, 22 (2003), 569-579.  doi: 10.1109/TMI.2003.812253.  Google Scholar

[16]

S. Kaczmarz, Angenäherte auflösung von systemen linearer gleichungen., Bulletin International de l'Académie Polonaise des Sciences et des Lettres. Classe des Sciences Mathématiques et Naturelles. Série A, Sciences Mathématiques, 35 1937,355–357. Google Scholar

[17]

T. Strohmer and R. Vershynin, A randomized {K}aczmarz algorithm with exponential convergence, J. Fourier Anal. Appl., 15 (2009), 262-278.  doi: 10.1007/s00041-008-9030-4.  Google Scholar

[18]

M. K. Tam, Gearhart-Koshy acceleration for affine subspaces, Operations Research Letters, 49 (2021), 157-163.  doi: 10.1016/j.orl.2020.12.007.  Google Scholar

[19]

N. H. Thao, D. R. Luke, O. Soloviev and M. Verhaegen, Phase retrieval with sparse phase constraint, SIAM J. Mathematics of Data Science, 2 (2020), 246-263.  doi: 10.1137/19M1266800.  Google Scholar

show all references

References:
[1]

F. Arroyo, E. Arroyo, X. Li and J. Zhu, The convergence of the block cyclic projection with an overrelaxation parameter for compressed sensing based tomography, J. Comput. Appl. Math., 280 (2015), 59-67.  doi: 10.1016/j.cam.2014.11.036.  Google Scholar

[2]

Z.-Z. Bai and W.-T. Wu, On greedy randomized {K}aczmarz method for solving large sparse linear systems, SIAM J. Sci. Comput., 40 (2018), A592–A606. doi: 10.1137/17M1137747.  Google Scholar

[3]

H. H. Bauschke and J. M. Borwein, On projection algorithms for solving convex feasibility problems, SIAM Rev., 38 (1996), 367-426.  doi: 10.1137/S0036144593251710.  Google Scholar

[4]

H. H. Bauschke and P. L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces., CMS Books in Mathematics/Ouvrages de Mathématiques de la SMC. Springer, New York, 2011. doi: 10.1007/978-1-4419-9467-7.  Google Scholar

[5]

H. H. Bauschke, F. Deutsch, H. Hundal and S.-H. Park, Accelerating the convergence of the method of alternating projections, Trans. Amer. Math. Soc., 355 (2003), 3433-3461.  doi: 10.1090/S0002-9947-03-03136-2.  Google Scholar

[6]

J. M. Borwein, G. Li and L. Yao, Analysis of the convergence rate for the cyclic projection algorithm applied to basic semialgebraic convex sets, SIAM J. Optim., 24 (2014), 498-527.  doi: 10.1137/130919052.  Google Scholar

[7]

L. M. Brègman, Finding the common point of convex sets by the method of successive projection, Dokl. Akad. Nauk SSSR, 162 (1965), 487-490.   Google Scholar

[8]

Y. Censor, Row-action methods for huge and sparse systems and their applications, SIAM Rev., 23 (1981), 444-466.  doi: 10.1137/1023097.  Google Scholar

[9]

F. Deutsch, Best Approximation in Inner Product Spaces, Volume 7 of CMS Books in Mathematics/Ouvrages de Mathématiques de la SMC., Springer-Verlag, New York, 2001. doi: 10.1007/978-1-4684-9298-9.  Google Scholar

[10]

T. Elfving, P. C. Hansen and T. Nikazad, Convergence analysis for column-action methods in image reconstruction, Numer. Algorithms, 74 (2017), 905-924.  doi: 10.1007/s11075-016-0176-x.  Google Scholar

[11]

L. Elsner, I. Koltracht and M. Neumann, Convergence of sequential and asynchronous nonlinear paracontractions, Numer. Math., 62 (1992), 305-319.  doi: 10.1007/BF01396232.  Google Scholar

[12]

C. Franchetti and W. Light, On the von {N}eumann alternating algorithm in {H}ilbert space, J. Math. Anal. Appl., 114 (1986), 305-314.  doi: 10.1016/0022-247X(86)90085-5.  Google Scholar

[13]

W. B. Gearhart and M. Koshy, Acceleration schemes for the method of alternating projections, J. Comput. Appl. Math., 26 (1989), 235-249.  doi: 10.1016/0377-0427(89)90296-3.  Google Scholar

[14]

G. S. M. Jansen, M. Keunecke, M. Düvel, C. Möller, D. Schmitt, W. Bennecke, F. J. S. Kappert, D. Steil, D. R. Luke, S. Steil and S. Mathias, Efficient orbital imaging based on ultrafast momentum microscopy and sparsity-driven phase retrieval, New Journal of Physics, 22 (2020), 063012. doi: 10.1088/1367-2630/ab8aae.  Google Scholar

[15]

M. Jiang and G. Wang, Convergence studies on iterative algorithms for image reconstruction, IEEE Trans. Med. Imaging, 22 (2003), 569-579.  doi: 10.1109/TMI.2003.812253.  Google Scholar

[16]

S. Kaczmarz, Angenäherte auflösung von systemen linearer gleichungen., Bulletin International de l'Académie Polonaise des Sciences et des Lettres. Classe des Sciences Mathématiques et Naturelles. Série A, Sciences Mathématiques, 35 1937,355–357. Google Scholar

[17]

T. Strohmer and R. Vershynin, A randomized {K}aczmarz algorithm with exponential convergence, J. Fourier Anal. Appl., 15 (2009), 262-278.  doi: 10.1007/s00041-008-9030-4.  Google Scholar

[18]

M. K. Tam, Gearhart-Koshy acceleration for affine subspaces, Operations Research Letters, 49 (2021), 157-163.  doi: 10.1016/j.orl.2020.12.007.  Google Scholar

[19]

N. H. Thao, D. R. Luke, O. Soloviev and M. Verhaegen, Phase retrieval with sparse phase constraint, SIAM J. Mathematics of Data Science, 2 (2020), 246-263.  doi: 10.1137/19M1266800.  Google Scholar

Figure 1.  Methods MCP, MRP and PAM applied to toy problem. Top row: Iterates red, subspaces blue. Bottom row: Frequencies (yellow = high, blue = low) of transitions from set $ C_m $ to set $ C_n $
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Figure 2.  Trajectories and frequencies of PAM as in Example 4(ⅰ)
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Figure 3.  Trajectories and frequencies of PAM as in Example 4(ⅱ)
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Figure 4.  Trajectories and frequencies of PAM as in Example 5
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Figure 5.  Error plots of methods applied to toy model with varying parameters. Solid black line MCP, dashed black line MRP, solid red line PAM $ \omega = N/4 $, dashed red line PAM $ \omega = N/2 $, dash-dotted red line PAM $ \omega = N $. More details given in Example 6
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