Skip to main content

Part of the book series: Lecture Notes in Mathematics ((LNM,volume 2057))

  • 2369 Accesses

Abstract

In Chap. 2 we introduce several classes of operators: nonexpansive, quasi-nonexpansive, strictly quasi-nonexpansive, strongly quasi-nonexpansive, cutters, firmly nonexpansive, relaxed firmly nonexpansive and strongly nonexpansive. We present general properties of these operators, prove the closedness of these classes under some algebraic operations and present the properties of the subsets of fixed points of operators from these classes. The importance of these operators follows from the fact that they define algorithms for solving convex optimization problems. In one iteration of the algorithm an appropriate operator (called an algorithmic operator) defines an actualization of the current approximation of a solution of the convex optimization problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S. Agmon, The relaxation method for linear inequalities. Can. J. Math. 6, 382–392 (1954)

    Google Scholar 

  2. R. Aharoni, A. Berman, Y. Censor, An interior point algorithm for the convex feasibility problem. Adv. Appl. Math. 4, 479–489 (1983)

    Google Scholar 

  3. R. Aharoni, Y. Censor, Block-iterative projection methods for parallel computation of solutions to convex feasibility problems. Lin. Algebra Appl. 120, 165–175 (1989)

    Google Scholar 

  4. D. Alevras, M.W. Padberg, Linear Optimization and Extensions. Problems and Solutions (Springer, Berlin, 2001)

    Google Scholar 

  5. A. Aleyner, S. Reich, Block-iterative algorithms for solving convex feasibility problems in Hilbert and in Banach spaces. J. Math. Anal. Appl. 343, 427–435 (2008)

    Google Scholar 

  6. A. Aleyner, S. Reich, Random products of quasi-nonexpansive mappings in Hilbert space. J. Convex Anal. 16, 633–640 (2009)

    Google Scholar 

  7. M. Altman, On the approximate solution of linear algebraic equations. Bulletin de l’Académie Polonaise des Sciences Cl. III 3 , 365–370 (1957)

    Google Scholar 

  8. I. Amemiya, T. Ando, Convergence of random products of contractions in Hilbert space. Acta Sci. Math. (Szeged) 26, 239–244 (1965)

    Google Scholar 

  9. R. Ansorge, Connections between the Cimmino-method and the Kaczmarz-method for solution of singular and regular systems of equations. Computing 33, 367–375 (1984)

    Google Scholar 

  10. G. Appleby, D.C. Smolarski, A linear acceleration row action method for projecting onto subspaces. Electron. Trans. Numer. Anal. 20, 253–275 (2005)

    Google Scholar 

  11. N. Aronszajn, Theory of reproducing kernels. Trans. Am. Math. Soc. 68, 337–404 (1950)

    Google Scholar 

  12. A. Auslender, Optimisation, méthodes numériques (Masson, Paris, 1976)

    Google Scholar 

  13. V.N. Babenko, Convergence of the Kaczmarz projection algorithm. Zh. Vychisl. Mat. Mat. Fiz. 24, 1571–1573 (1984) (in Russian)

    Google Scholar 

  14. J.B. Baillon, R.E. Bruck, S. Reich, On the asymptotic behavior of nonexpansive mappings and semigroups in Banach spaces. Houston J. Math. 4, 1–9 (1978)

    Google Scholar 

  15. S. Banach, Sur les opérations dans les ensembles abstraits et leur application aux équations intégrals. Fundamenta Mathematicae 3, 133–181 (1922)

    Google Scholar 

  16. H.H. Bauschke, A norm convergence result on random products of relaxed projections in Hilbert space. Trans. Am. Math. Soc. 347, 1365–1373 (1995)

    Google Scholar 

  17. H.H. Bauschke, Projection Algorithms and Monotone Operators, Ph.D. Thesis, Department of Mathematics, Simon Fraser, University, Burnaby, BC, Canada, 1996

    Google Scholar 

  18. H.H. Bauschke, The approximation of fixed points of compositions of nonexpansive mapping in Hilbert space. J. Math. Anal. Appl. 202, 150–159 (1996)

    Google Scholar 

  19. H.H. Bauschke, The composition of the projections onto closed convex sets in Hilbert space is asymptotically regular. Proc. Am. Math. Soc. 131, 141–146 (2002)

    Google Scholar 

  20. H.H. Bauschke, J. Borwein, On the convergence of von Neumann’s alternating projection algorithm for two sets. Set-Valued Anal. 1, 185–212 (1993)

    Google Scholar 

  21. H.H. Bauschke, J. Borwein, Dykstra’s alternating projection algorithm for two sets. J. Approx. Theor. 79, 418–443 (1994)

    Google Scholar 

  22. H.H. Bauschke, J. Borwein, On projection algorithms for solving convex feasibility problems. SIAM Rev. 38, 367–426 (1996)

    Google Scholar 

  23. H.H. Bauschke, J.M. Borwein, A.S. Lewis, The method of cyclic projections for closed convex sets in Hilbert space. Contemp. Math. 204, 1–38 (1997)

    Google Scholar 

  24. H.H. Bauschke, P.L. Combettes, A weak-to-strong convergence principle for Fejér-monotone methods in Hilbert spaces. Math. Oper. Res. 26, 248–264 (2001)

    Google Scholar 

  25. H.H. Bauschke, P.L. Combettes, S.G. Kruk, Extrapolation algorithm for affine-convex feasibility problems. Numer. Algorithms 41, 239–274 (2006)

    Google Scholar 

  26. H.H. Bauschke, P.L. Combettes, D.R. Luke, Phase retrieval, error reduction algorithm, and Fienup variants: A view from convex optimization. J. Opt. Soc. Am. A 19, 1334–1345 (2002)

    Google Scholar 

  27. H.H. Bauschke, P.L. Combettes, D.R. Luke, Hybrid projection-reflection method for phase retrieval. J. Opt. Soc. Am. A 20, 1025–1034 (2003)

    Google Scholar 

  28. H.H. Bauschke, P.L. Combettes, D.R. Luke, Finding best approximation pairs relative to two closed convex sets in Hilbert spaces. J. Approx. Theor. 127, 178–192 (2004)

    Google Scholar 

  29. H.H. Bauschke, P.L. Combettes, D.R. Luke, A strongly convergent reflection method for finding the projection onto the intersection of two closed convex sets in a Hilbert space. J. Approx. Theor. 141, 63–69 (2006)

    Google Scholar 

  30. H.H. Bauschke, F. Deutsch, H. Hundal, S-H. Park, Accelerating the convergence of the method of alternating projections. Trans. Am. Math. Soc. 355, 3433–3461 (2003)

    Google Scholar 

  31. H.H. Bauschke, S.G. Kruk, Reflection-projection method for convex feasibility problems with an obtuse cone. J. Optim. Theor. Appl. 120, 503–531 (2004)

    Google Scholar 

  32. H.H. Bauschke, E. Matoušková, S. Reich, Projection and proximal point methods: convergence results and counterexamples. Nonlinear Anal. 56, 715–738 (2004)

    Google Scholar 

  33. M.H. Bazaraa, H.D. Sherali, C.M. Shetty, Nonlinear Programming, Theory and Algorithms, 3rd edn. (Wiley, Hoboken, 2006)

    Google Scholar 

  34. M. Benzi, C.D. Meyer, A direct projection method for sparse linear systems. SIAM J. Sci. Comput. 16, 1159–1176 (1995)

    Google Scholar 

  35. A. Berman, R.J. Plemmons, Nonnegative Matrices in the Mathematical Sciences (Academic, New York, 1979)

    Google Scholar 

  36. V. Berinde, Iterative Approximation of Fixed Points (Springer, Berlin, 2007)

    Google Scholar 

  37. M. Bertero, P. Boccacci, Introduction to Inverse Problems in Imaging (Institute of Physics Publishing, Bristol, 1998)

    Google Scholar 

  38. D.P. Bertsekas, Nonlinear Programming (Athena Scientific, Belmont, 1995)

    Google Scholar 

  39. D. Blatt, A.O. Hero, Energy-based sensor network source localization via projection onto convex sets (POCS). IEEE Trans. Signal Process. 54, 3614–3619 (2006)

    Google Scholar 

  40. J.M. Borwein, A. Lewis, Convex Analysis and Nonlinear Optimization, Theory and Examples (Springer, New York, 2000)

    Google Scholar 

  41. R. Bramley, A. Sameh, Row projection methods for large nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 13, 168–193 (1992)

    Google Scholar 

  42. L.M. Bregman, Finding the common point of convex sets by the method of successive projection (in Russian). Dokl. Akad. Nauk SSSR 162, 487–490 (1965); English translation in: Soviet Math. Dokl. 6, 688–692 (1965)

    Google Scholar 

  43. L.E.J. Brouwer, Über Abbildung von Mannigfaltigkeiten. Math. Ann. 71, 97–115 (1912)

    Google Scholar 

  44. F.E. Browder, Fixed-point theorems for noncompact mappings in Hilbert space. Proc. Nat. Acad. Sci. USA 53, 1272–1276 (1965)

    Google Scholar 

  45. F.E. Browder, Nonexpansive nonlinear operators in a Banach space. Proc. Nat. Acad. Sci. USA 54, 1041–1044 (1965)

    Google Scholar 

  46. F.E. Browder, Convergence of approximants to fixed points of nonexpansive nonlinear mappings in Banach spaces. Arch. Rational Mech. Anal. 24, 82–90 (1967)

    Google Scholar 

  47. F.E. Browder, Convergence theorems for sequences of nonlinear operators in Banach spaces. Math. Zeitschr. 100, 201–225 (1967)

    Google Scholar 

  48. F.E. Browder, W.V. Petryshyn, The solution by iteration of nonlinear functional equations in Banach spaces. Bull. Am. Math. Soc. 72, 571–575 (1966)

    Google Scholar 

  49. R.E. Bruck, Nonexpansive projections on subsets of Banach spaces. Pac. J. Math. 47, 341–355 (1973)

    Google Scholar 

  50. R.E. Bruck, Random products of contractions in metric and Banach spaces. J. Math. Anal. Appl. 88, 319–332 (1982)

    Google Scholar 

  51. R.E. Bruck, S. Reich, Nonexpansive projections and resolvents of accretive operators in Banach spaces. Houston J. Math. 3, 459–470 (1977)

    Google Scholar 

  52. R.S. Burachik, J.O. Lopes, B.F. Svaiter, An outer approximation method for the variational inequality problem. SIAM J. Contr. Optim. 43, 2071–2088 (2005)

    Google Scholar 

  53. D. Butnariu, Y. Censor, On the behavior of a block-iterative projection method for solving convex feasibility problems. Int. J. Comp. Math. 34, 79–94 (1990)

    Google Scholar 

  54. D. Butnariu, Y. Censor, P. Gurfil, E. Hadar, On the behavior of subgradient projections methods for convex feasibility problems in Euclidean spaces. SIAM J. Opt. 19, 786–807 (2008)

    Google Scholar 

  55. C. Byrne, Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl. 18, 441–453 (2002)

    Google Scholar 

  56. C. Byrne, A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl. 20, 103–120 (2004)

    Google Scholar 

  57. C.L. Byrne, Applied Iterative Methods (AK Peters, Wellesley, 2008)

    Google Scholar 

  58. C.L. Byrne, Bounds on the largest singular value of a matrix and the convergence of simultaneous and block-iterative algorithms for sparse linear systems. Int. Trans. Oper. Res. 16, 465–479 (2009)

    Google Scholar 

  59. T.D. Capricelli, P.L. Combettes, Parallel block-iterative reconstruction algorithms for binary tomography. Electron. Notes Discr. Math. 20, 263–280 (2005)

    Google Scholar 

  60. G. Casssiani, G. Böhm, A. Vesnaver, R. Nicolich, A geostatistical framework for incorporating seismic tomography auxiliary data into hydraulic conductivity estimation. J. Hydrol. 206, 58–74 (1998)

    Google Scholar 

  61. J. Cea, Optimisation: théorie et algorithmes (Dunod, Paris, 1971); Polish translation: Optymalizacja: Teoria i algorytmy (PWN, Warszawa, 1976)

    Google Scholar 

  62. A. Cegielski, Relaxation Methods in Convex Optimization Problems (in Polish). Monographs, vol. 67, Institute of Mathematics, Higher College of Engineering, Zielona Góra, 1993

    Google Scholar 

  63. A. Cegielski, in Projection Onto an Acute Cone and Convex Feasibility Problems, ed. by J. Henry i J.-P. Yvon. Lecture Notes in Control and Inform. Sci., vol. 197 (Springer, London, 1994), pp. 187–194

    Google Scholar 

  64. A. Cegielski, A method of projection onto an acute cone with level control in convex minimization. Math. Program. 85, 469–490 (1999)

    Google Scholar 

  65. A. Cegielski, Obtuse cones and Gram matrices with nonnegative inverse. Lin. Algebra Appl. 335, 167–181 (2001)

    Google Scholar 

  66. A. Cegielski, A generalization of the Opial’s theorem. Contr. Cybern. 36, 601–610 (2007)

    Google Scholar 

  67. A. Cegielski, Convergence of the projected surrogate constraints method for the linear split feasibility problems. J. Convex Anal. 14, 169–183 (2007)

    Google Scholar 

  68. A. Cegielski, Projection methods for the linear split feasibility problems. Optimization 57, 491–504 (2008)

    Google Scholar 

  69. A. Cegielski, Generalized relaxations of nonexpansive operators and convex feasibility problems. Contemp. Math. 513, 111–123 (2010)

    Google Scholar 

  70. A. Cegielski, Y. Censor, in Opial-Type Theorems and the Common Fixed Point Problem, ed. by H.H. Bauschke, R.S. Burachik, P.L. Combettes, V. Elser, D.R. Luke, H. Wolkowicz. Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer Optimization and Its Applications, vol. 49 (Springer, New York, 2011), pp. 155–183

    Google Scholar 

  71. A. Cegielski, Y. Censor, Extrapolation and local acceleration of an iterative process for common fixed point problems. J. Math. Anal. Appl. 394, 809–818 (2012)

    Google Scholar 

  72. A. Cegielski, R. Dylewski, Selection strategies in projection methods for convex minimization problems. Discuss. Math. Differ. Incl. Contr. Optim. 22, 97–123 (2002)

    Google Scholar 

  73. A. Cegielski, R. Dylewski, Residual selection in a projection method for convex minimization problems. Optimization 52, 211–220 (2003)

    Google Scholar 

  74. A. Cegielski, R. Dylewski, Variable target value relaxed alternating projection method. Comput. Optim. Appl. 47, 455–476 (2010)

    Google Scholar 

  75. A. Cegielski, A. Suchocka, Incomplete alternating projection method for large inconsistent linear systems. Lin. Algebra Appl. 428, 1313–1324 (2008)

    Google Scholar 

  76. A. Cegielski, A. Suchocka, Relaxed alternating projection methods. SIAM J. Optim. 19, 1093–1106 (2008)

    Google Scholar 

  77. A. Cegielski, R. Zalas, Methods for variational inequality problem over the intersection of fixed point sets of quasi-nonexpansive operators (2012) Numer. Funct. Anal. Optim. (in print)

    Google Scholar 

  78. Y. Censor, Row-action methods for huge and sparse systems and their applications. SIAM Rev. 23, 444–466 (1981)

    Google Scholar 

  79. Y. Censor, Iterative methods for convex feasibility problems. Ann. Discrete Math. 20, 83–91 (1984)

    Google Scholar 

  80. Y. Censor, An automatic relaxation method for solving interval linear inequalities. J. Math. Anal. Appl. 106, 19–25 (1985)

    Google Scholar 

  81. Y. Censor, Parallel application of block-iterative methods in medical imaging and radiation therapy. Math. Program. 42, 307–325 (1988)

    Google Scholar 

  82. Y. Censor, Binary steering in discrete tomography reconstruction with sequential and simultaneous iterative algorithms. Lin. Algebra Appl. 339, 111–124 (2001)

    Google Scholar 

  83. Y. Censor, M.D. Altschuler, W.D. Powlis, On the use of Cimmino’s simultaneous projections method for computing a solution of the inverse problem in radiation therapy treatment planning. Inverse Probl. 4, 607–623 (1988)

    Google Scholar 

  84. Y. Censor, A. Ben-Israel, Y. Xiao, J.M. Galvin, On linear infeasibility arising in intensity-modulated radiation therapy inverse planning. Lin. Algebra Appl. 428, 1406–1420 (2008)

    Google Scholar 

  85. Y. Censor, T. Bortfeld, B. Martin, A. Trofimov, A unified approach for inversion problems in intensity-modulated radiation therapy. Phys. Med. Biol. 51, 2353–2365 (2006)

    Google Scholar 

  86. Y. Censor, P.P.B. Eggermont, D. Gordon, Strong underrelaxation in Kaczmarz’s method for inconsistent systems. Numer. Math. 41, 83–92 (1983)

    Google Scholar 

  87. Y. Censor, T. Elfving, New methods for linear inequalities. Lin. Algebra Appl. 42, 199–211 (1982)

    Google Scholar 

  88. Y. Censor, T. Elfving, A multiprojection algorithm using Bregman projections in a product space. Numer. Algorithm 8, 221–239 (1994)

    Google Scholar 

  89. Y. Censor, T. Elfving, Block-iterative algorithms with diagonal scaled oblique projections for the linear feasibility problems. SIAM J. Matrix Anal. Appl. 24, 40–58 (2002)

    Google Scholar 

  90. Y. Censor, T. Elfving, Iterative algorithms with seminorm-induced oblique projections. Abstr. Appl. Anal. 8, 387–406 (2003)

    Google Scholar 

  91. Y. Censor, T. Elfving, G.T. Herman, in Averaging Strings of Sequential Iterations for Convex Feasibility Problems, ed. by D. Butnariu, Y. Censor, S. Reich. Inherently Parallel Algorithms in Feasibility and Optimization and their Applications (Elsevier, Amsterdam, 2001), pp. 101–113

    Google Scholar 

  92. Y. Censor, T. Elfving, G.T. Herman, T. Nikazad, On diagonally relaxed orthogonal projection methods. SIAM J. Sci. Comput. 30, 473–504 (2008)

    Google Scholar 

  93. Y. Censor, T. Elfving, N. Kopf, T. Bortfeld, The multiple-sets split feasibility problem and its applications for inverse problems. Inverse Probl. 21, 2071–2084 (2005)

    Google Scholar 

  94. Y. Censor, A. Gibali, Projections onto super-half-spaces for monotone variational inequality problems in finite-dimensional spaces. J. Nonlinear Convex Anal. 9, 461–475 (2008)

    Google Scholar 

  95. Y. Censor, A. Gibali, S. Reich, The subgradient extragradient method for solving variational inequalities in Hilbert space. J. Optim. Theory Appl. 148, 318–335 (2011)

    Google Scholar 

  96. Y. Censor, A. Gibali, S. Reich, Extensions of Korpelevich’s extragradient method for the variational inequality problem in Euclidean space. Optimization 61, 1119–1132 (2012)

    Google Scholar 

  97. Y. Censor, A. Gibali, S. Reich, Strong convergence of subgradient extragradient methods for the variational inequality problem in Hilbert space. Optim. Meth. Software 26, 827–845 (2011)

    Google Scholar 

  98. Y. Censor, D. Gordon, R. Gordon, Component averaging: An efficient iterative parallel algorithm for large and sparse unstructured problems. Parallel Comput. 27, 777–808 (2001)

    Google Scholar 

  99. Y. Censor, D. Gordon, R. Gordon, BICAV: A block-iterative, parallel algorithm for sparse systems with pixel-related weighting. IEEE Trans. Med. Imag. 20, 1050–1060 (2001)

    Google Scholar 

  100. Y. Censor, G.T. Herman, On some optimization techniques in image reconstruction from projections. Appl. Numer. Math. 3, 365–391 (1987)

    Google Scholar 

  101. Y. Censor, A.N. Iusem, S.A. Zenios, An interior point method with Bregman functions for the variational inequality problem with paramonotone operators. Math. Program. 81, 373–400 (1998)

    Google Scholar 

  102. Y. Censor, A. Lent, Cyclic subgradient projections. Math. Program. 24, 233–235 (1982)

    Google Scholar 

  103. Y. Censor, A. Motova, A. Segal, Perturbed projections and subgradient projections for the multiple-sets split feasibility problem. J. Math. Anal. Appl. 327, 1244–1256 (2007)

    Google Scholar 

  104. Y. Censor, A. Segal, The split common fixed point problem for directed operators. J. Convex Anal. 16, 587–600 (2009)

    Google Scholar 

  105. Y. Censor, A. Segal, On the string averaging method for sparse common fixed point problems. Int. Trans. Oper. Res. 16, 481–494 (2009)

    Google Scholar 

  106. Y. Censor, A. Segal, Sparse string-averaging and split common fixed points. Contemp. Math. 513, 125–142 (2010)

    Google Scholar 

  107. Y. Censor, E. Tom, Convergence of string-averaging projection schemes for inconsistent convex feasibility problems. Optim. Meth. Software 18, 543–554 (2003)

    Google Scholar 

  108. Y. Censor, S.A. Zenios, Parallel Optimization, Theory, Algorithms and Applications (Oxford University Press, New York, 1997)

    Google Scholar 

  109. A.E. Çetin, H. Özaktaş, H.M. Ozaktas, Resolution enhancement of low resolution wavefields with POCS algorithm. Electron. Lett. 9, 1808–1810 (2003)

    Google Scholar 

  110. W. Chen, D. Craft, T.M. Madden, K. Zhang, H.M. Kooy, G.T. Herman, A fast optimization algorithm for multicriteria intensity modulated proton therapy planning. Med. Phys. 7, 4938–4945 (2010)

    Google Scholar 

  111. W. Chen, G.T. Herman, Efficient controls for finitely convergent sequential algorithms. ACM Trans. Math. Software 37, 1–23 (2010)

    Google Scholar 

  112. W. Cheney, A.A. Goldstein, Proximity maps for convex sets. Proc. Am. Math Soc. 10, 448–450 (1959)

    Google Scholar 

  113. C.E. Chidume, Quasi-nonexpansive mappings and uniform asymptotic regularity. Kobe J. Math. 3, 29–35 (1986)

    Google Scholar 

  114. Ch. Chidume, Geometric Properties of Banach Spaces and Nonlinear Iterations (Springer, London, 2009)

    Google Scholar 

  115. H. Choi, R.G. Baraniuk, Multiple wavelet basis image denoising using Besov ball projections. IEEE Signal Process. Lett. 11, 717–720 (2004)

    Google Scholar 

  116. G. Cimmino, Calcolo approssimato per le soluzioni dei sistemi di equazioni lineari. La Ricerca Scientifica, II 9, 326–333 (1938)

    Google Scholar 

  117. P.L. Combettes, Inconsistent signal feasibility problems: Least-square solutions in a product space. IEEE Trans. Signal Process. 42, 2955–2966 (1994)

    Google Scholar 

  118. P.L. Combettes, in The Convex Feasibility Problem in Image Recovery, ed. by P. Hawkes. Advances in Imaging and Electron Physics, vol. 95 (Academic, New York, 1996), pp. 155–270

    Google Scholar 

  119. P.L. Combettes, Hilbertian convex feasibility problem: Convergence of projection methods. Appl. Math. Optim. 35, 311–330 (1997)

    Google Scholar 

  120. P.L. Combettes, Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections. IEEE Trans. Image Process. 6, 493–506 (1997)

    Google Scholar 

  121. P.L. Combettes, in Quasi-Fejérian Analysis of Some Optimization Algorithm, ed. by D. Butnariu, Y. Censor, S. Reich. Inherently Parallel Algorithms in Feasibility and Optimization and their Applications (Elsevier, Amsterdam, 2001), pp. 115–152

    Google Scholar 

  122. P.L. Combettes, Solving monotone inclusions via compositions of nonexpansive averaged operators. Optimization 53, 475–504 (2004)

    Google Scholar 

  123. P.L. Combettes, P. Bondon, Hard-constrained inconsistent signal feasibility problems. IEEE Trans. Signal Process. 47, 2460–2468 (1999)

    Google Scholar 

  124. P.L. Combettes, S.A. Hirstoaga, Equilibrium programming in Hilbert spaces. J. Nonlinear Convex Anal. 6, 117–136 (2005)

    Google Scholar 

  125. P.L. Combettes, H. Puh, Iterations of parallel convex projections in Hilbert spaces. Numer. Funct. Anal. Optim. 15, 225–243 (1994)

    Google Scholar 

  126. G. Crombez, A geometrical look at iterative methods for operators with fixed points. Numer. Funct. Anal. Optim. 26, 157–175 (2005)

    Google Scholar 

  127. G. Crombez, A hierarchical presentation of operators with fixed points on Hilbert spaces. Numer. Funct. Anal. Optim. 27, 259–277 (2006)

    Google Scholar 

  128. Y.-H. Dai, Fast algorithms for projection on an ellipsoid. SIAM J. Optim. 16, 986–1006 (2006)

    Google Scholar 

  129. A. Dax, A note of the convergence of linear stationary iterative process. Lin. Algebra Appl. 129, 131–142 (1990)

    Google Scholar 

  130. A. Dax, Linear search acceleration of iterative methods. Lin. Algebra Appl. 130, 43–63 (1990)

    Google Scholar 

  131. A. Dax, On hybrid acceleration of a linear stationary iterative process. Lin. Algebra Appl. 130, 99–110 (1990)

    Google Scholar 

  132. A. Dax, The convergence of linear stationary iterative processes for solving singular unstructured systems of linear equations. SIAM Rev. 32, 611–635 (1990)

    Google Scholar 

  133. L. Debnath, P. Mikusiński, Hilbert Spaces with Applications, 2nd edn. (Academic, San Diego, 1999)

    Google Scholar 

  134. A.R. De Pierro, A.N. Iusem, A simultaneous projections method for linear inequalities. Lin. Algebra Appl. 64, 243–253 (1985)

    Google Scholar 

  135. A.R. De Pierro, A.N. Iusem, A parallel projection method of finding a common point of a family of convex sets. Pesquisa Operacional 5, 1–20 (1985)

    Google Scholar 

  136. A.R. De Pierro, A.N. Iusem, A finitely convergent “row-action” method for the convex feasibility problem. Appl. Math. Optim. 17, 225–235 (1988)

    Google Scholar 

  137. A.R. De Pierro, A.N. Iusem, On the asymptotic behavior of some alternate smoothing series expansion iterative methods. Lin. Algebra Appl. 130, 3–24 (1990)

    Google Scholar 

  138. F. Deutsch, in Applications of von Neumann’s Alternating Projections Algorithm, ed. by P. Kenderov. Mathematical Methods in Operations Research (Sophia, Bulgaria, 1983), pp. 44–51

    Google Scholar 

  139. F. Deutsch, in The Method of Alternating Orthogonal Projections, ed. by S.P. Singh. Approximation Theory, Spline Functions and Applications (Kluwer Academic, The Netherlands, 1992), pp. 105–121

    Google Scholar 

  140. F. Deutsch, Best Approximation in Inner Product Spaces (Springer, New York, 2001)

    Google Scholar 

  141. F. Deutsch, in Accelerating the Convergence of the Method of Alternating Projections via a Line Search: A Brief Survey, ed. by D. Butnariu, Y. Censor, S. Reich. Inherently Parallel Algorithms in Feasibility and Optimization and their Application, Studies in Computational Mathematics, vol. 8 (Elsevier Science, Amsterdam, 2001), pp. 203–217

    Google Scholar 

  142. F. Deutsch, H. Hundal, The rate of convergence for the cyclic projections algorithm, I. Angles between convex sets. J. Approx. Theor. 142, 36–55 (2006)

    Google Scholar 

  143. F. Deutsch, H. Hundal, The rate of convergence for the cyclic projections algorithm, II. Norms of nonlinear operators. J. Approx. Theor. 142, 56–82 (2006)

    Google Scholar 

  144. F. Deutsch, I. Yamada, Minimizing certain convex functions over the intersection of the fixed point sets of nonexpansive mappings. Numer. Funct. Anal. Optim. 19, 33–56 (1998)

    Google Scholar 

  145. J.B. Diaz, F.T. Metcalf, On the set of subsequential limit points of successive approximations. Trans. Am. Math. Soc. 135, 459–485 (1969)

    Google Scholar 

  146. L.T. Dos Santos, A parallel subgradient projections method for the convex feasibility problem. J. Comput. Appl. Math. 18, 307–320 (1987)

    Google Scholar 

  147. W.G. Dotson Jr., On the Mann iterative process. Trans. Am. Math. Soc. 149, 65–73 (1970)

    Google Scholar 

  148. W.G. Dotson, Fixed points of quasi-nonexpansive mappings. J. Austral. Math. Soc. 13, 167–170 (1972)

    Google Scholar 

  149. J. Douglas, H.H. Rachford, On the numerical solution of heat conduction problems in two or three space variables. Trans. Am. Math. Soc. 82, 421–439 (1956)

    Google Scholar 

  150. R. Dudek, Iterative method for solving the linear feasibility problem. J. Optim Theor. Appl. 132, 401–410 (2007)

    Google Scholar 

  151. J. Dye, M.A. Khamsi, S. Reich, Random products of contractions in Banach spaces. Trans. Am. Math. Soc. 325, 87–99 (1991)

    Google Scholar 

  152. J.M. Dye, S. Reich, On the unrestricted iteration of projections in Hilbert space. J. Math. Anal. Appl. 156, 101–119 (1991)

    Google Scholar 

  153. J. Dye, S. Reich, Unrestricted iterations of nonexpansive mappings in Hilbert space. Nonlinear Anal. 18, 199–207 (1992)

    Google Scholar 

  154. R. Dylewski, Selection of Linearizations in Projection Methods for Convex Optimization Problems (in Polish), Ph.D. thesis, University of Zielona Góra, Institute of Mathematics, 2003

    Google Scholar 

  155. R. Dylewski, Projection method with residual selection for linear feasibility problems. Discuss. Math. Differ. Incl. Contr. Optim. 27, 43–50 (2007)

    Google Scholar 

  156. M.G. Eberle, M.C. Maciel, Finding the closest Toeplitz matrix. Computat. Appl. Math. 22, 1–18 (2003)

    Google Scholar 

  157. J. Eckstein, D.P. Bertsekas, On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Program. 55, 293–318 (1992)

    Google Scholar 

  158. I. Ekeland, R. Témam, Convex Analysis and Variational Problems (North-Holland, Amsterdam, 1976)

    Google Scholar 

  159. T. Elfving, A projection method for semidefinite linear systems and its applications. Lin. Algebra Appl. 391, 57–73 (2004)

    Google Scholar 

  160. T. Elfving, T. Nikazad, Stopping rules for Landweber-type iteration. Inverse Probl. 23, 1417–1432 (2007)

    Google Scholar 

  161. V. Elser, I. Rankenburg, P. Thibault, Searching with iterated maps. Proc. Natl. Acad. Sci. USA 104, 418–423 (2007)

    Google Scholar 

  162. L. Elsner, I. Koltracht, P. Lancaster, Convergence properties of ART and SOR algorithms. Numer. Math. 59, 91–106 (1991)

    Google Scholar 

  163. L. Elsner, I. Koltracht, M. Neumann, On the convergence of asynchronous paracontractions with application to tomographic reconstruction from incomplete data. Lin. Algebra Appl. 130, 65–82 (1990)

    Google Scholar 

  164. L. Elsner, I. Koltracht, M. Neumann, Convergence of sequential and asynchronous nonlinear paracontractions. Numer. Math. 62, 305–319 (1992)

    Google Scholar 

  165. F. Facchinei, J.-S. Pang, Finite-Dimensional Variational Inequalities and Complementarity Problems, Volume I, Volume II (Springer, New York, 2003)

    Google Scholar 

  166. M. Fiedler, V. Pták, On matrices with non-positive off-diagonal elements and positive principal minors. Czech. Math. J. 12, 382–400 (1962)

    Google Scholar 

  167. S. Fitzpatrick, R.R. Phelps, Differentiability of the metric projection in Hilbert space. Trans. Am. Math. Soc. 270, 483–501 (1982)

    Google Scholar 

  168. S.D. Flåm, J. Zowe, Relaxed outer projections, weighted averages and convex feasibility. BIT 30, 289–300 (1990)

    Google Scholar 

  169. R. Fletcher, Practical Methods of Optimization (Wiley, Chichester, 1987)

    Google Scholar 

  170. K. Friedricks, On certain inequalities and characteristic value problems for analytic functions and for functions of two variables. Trans. Am. Math. Soc. 41, 321–364 (1937)

    Google Scholar 

  171. M. Fukushima, A relaxed projection method for variational inequalities. Math. Program. 35, 58–70 (1986)

    Google Scholar 

  172. E.M. Gafni, D.P. Bertsekas, Two metric projection methods for constrained optimization. SIAM J. Contr. Optim. 22, 936–964 (1984)

    Google Scholar 

  173. A. Galántai, Projectors and Projection Methods (Kluwer Academic, Boston, 2004)

    Google Scholar 

  174. A. Galántai, On the rate of convergence of the alternating projection method in finite dimensional spaces. J. Math. Anal. Appl. 310, 30–44 (2005)

    Google Scholar 

  175. U. García-Palomares, Parallel projected aggregation methods for solving the convex feasibility problem. SIAM J. Optim. 3, 882–900 (1993)

    Google Scholar 

  176. U. García-Palomares, A superlinearly convergent projection algorithm for solving the convex inequality problem. Oper. Res. Lett. 22, 97–103 (1998)

    Google Scholar 

  177. W.B. Gearhart, M. Koshy, Acceleration schemes for the method of alternating projections. J. Comput. Appl. Math. 26, 235–249 (1989)

    Google Scholar 

  178. C. Geiger, Ch. Kanzow, Numerische Verfahren zur Lösung unrestingierter Optimierungsaufgaben (Springer, Berlin, 1999)

    Google Scholar 

  179. C. Geiger, Ch. Kanzow, Theorie und Numerik restringierter Optimierungsaufgaben (Springer, Berlin, 2002)

    Google Scholar 

  180. J.R. Giles, Convex Analysis with Application in Differentiation of Convex Functions (Pitman Advanced Publishing Program, Boston, 1982)

    Google Scholar 

  181. P.E. Gill, W. Murray, M.H. Wright, Numerical Linear Algebra and Optimization (Addison-Wesley, Redwood City, 1991)

    Google Scholar 

  182. W. Glunt, T.L. Hayden, R. Reams, The nearest ‘doubly stochastic’ matrix to a real matrix with the same first moment. Numer. Lin. Algebra Appl. 5, 475–482 (1998)

    Google Scholar 

  183. K. Goebel, Concise Course on Fixed Points Theorems (Yokohama Publishing, Yokohama, 2002); Polish translation: Twierdzenia o punktach stałych (Wydawnictwo UMCS, Lublin, 2005)

    Google Scholar 

  184. K. Goebel, W.A. Kirk, Topics in Metric Fixed Point Theory (Cambridge University Press, Cambridge, 1990); Polish translation: Zagadnienia metrycznej teorii punktów stałych (Wydawnictwo UMCS, Lublin, 1999)

    Google Scholar 

  185. K. Goebel, S. Reich, Uniform Convexity, Hyperbolic Geometry, and Nonexpansive Mappings (Marcel Dekker, New York, 1984)

    Google Scholar 

  186. J.L. Goffin, The relaxation method for solving systems of linear inequalities. Math. Oper. Res. 5, 388–414 (1980)

    Google Scholar 

  187. J.L. Goffin, On the finite convergence of the relaxation method for solving systems of inequalities. Operations Research Center, Report ORC 71–36, University of California, Berkeley, 1971

    Google Scholar 

  188. D. Göhde, Zum Prinzip der kontraktiven Abbildung. Math. Nachr. 30, 251–258 (1965)

    Google Scholar 

  189. R. Gordon, R. Bender, G.T. Herman, Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J. Theoret. Biol. 29, 471–481 (1970)

    Google Scholar 

  190. D. Gordon, R. Gordon, Component-averaged row projections: a robust, block-parallel scheme for sparse linear systems. SIAM J. Sci. Comput. 27, 1092–1117 (2005)

    Google Scholar 

  191. A. Granas, J. Dugundji, Fixed Point Theory (Springer, New York, 2003)

    Google Scholar 

  192. K.M. Grigoriadis, A.E. Frazho, R.E. Skelton, Application of alternating convex projection methods for computation of positive Toeplitz matrices. IEEE Trans. Signal Process. 42, 1873–1875 (1994)

    Google Scholar 

  193. K.M. Grigoriadis, R.E. Skelton, Low-order control design for LMI problems using alternating projection methods. Automatica 32, 1117–1125 (1996)

    Google Scholar 

  194. K.M. Grigoriadis, R.E. Skelton, Alternating convex projection methods for discrete-time covariance control design. J. Optim. Theor. Appl. 88, 399–432 (1996)

    Google Scholar 

  195. J. Gu, H. Stark, Y. Yang, Wide-band smart antenna design using vector space projection methods. IEEE Trans. Antenn. Propag. 52, 3228–3236 (2004)

    Google Scholar 

  196. L.G. Gurin, B.T. Polyak, E.V. Raik, The method of projection for finding the common point in convex sets. Zh. Vychisl. Mat. Mat. Fiz. 7, 1211–1228 (1967) (in Russian); English translation in: USSR Comput. Math. Phys. 7, 1–24 (1967)

    Google Scholar 

  197. R. Haller, R. Szwarc, Kaczmarz algorithm in Hilbert space. Studia Math. 169, 123–132 (2005)

    Google Scholar 

  198. I. Halperin, The product of projection operators. Acta Sci. Math. (Szeged) 23, 96–99 (1962)

    Google Scholar 

  199. H.W. Hamacher, K.-H. Küfer, Inverse radiation therapy planning – a multiple objective optimization approach. Discrete Appl. Math. 118, 145–161 (2002)

    Google Scholar 

  200. S.-P. Han, A successive projection method. Math. Program. (Ser. A) 40, 1–14 (1988)

    Google Scholar 

  201. M. Hanke, W. Niethammer, On the acceleration of Kaczmarz’s method for inconsistent linear systems. Lin. Algebra Appl. 130, 83–98 (1990)

    Google Scholar 

  202. Y. Haugazeau, Sur les inéquations variationnelles et la minimisation de fonctionnelles convexes (Thèse, Université de Paris, Paris, 1968)

    Google Scholar 

  203. H. He, S. Liu, H. Zhou, An explicit method for finding common solutions of variational inequalities and systems of equilibrium problems and fixed points of an infinite family of nonexpansive mappings. Nonlinear Anal. 72, 3124–3135 (2010)

    Google Scholar 

  204. G.T. Herman, A relaxation method for reconstructing objects from noisy X-rays. Math. Program. 8, 1–19 (1975)

    Google Scholar 

  205. G.T. Herman, Fundamentals of Computerized Tomography: Image Reconstruction from Projections, 2nd edn. (Springer, London, 2009)

    Google Scholar 

  206. G.T. Herman, W. Chen, A fast algorithm for solving a linear feasibility problem with application to intensity-modulated radiation therapy. Lin. Algebra Appl. 428, 1207–1217 (2008)

    Google Scholar 

  207. N.J. Higham, Computing a nearest symmetric positive semidefinite matrix. Lin. Algebra Appl. 103, 103–118 (1988)

    Google Scholar 

  208. N.J. Higham, Computing the nearest correlation matrix – a problem from finance. IMA J. Numer. Anal. 22, 329–343 (2002)

    Google Scholar 

  209. J.-B. Hiriart-Urruty, C. Lemaréchal, Convex Analysis and Minimization Algorithms, Vol I, Vol II (Springer, Berlin, 1993)

    Google Scholar 

  210. J.-B. Hiriart-Urruty, C. Lemaréchal, Fundamentals of Convex Analysis (Springer, Berlin, 2001)

    Google Scholar 

  211. S.A. Hirstoaga, Iterative selection methods for common fixed point problems. J. Math. Anal. Appl. 324, 1020–1035 (2006)

    Google Scholar 

  212. J. Höffner, P. Decker, E.L. Schmidt, W. Herbig, J. Rittler, P. Weiß, Development of a fast optimization preview in radiation treatment planning. Strahlentherapie und Onkologie 172, 384–394 (1996)

    Google Scholar 

  213. H.S. Hundal, An alternating projection that does not converge in norm. Nonlinear Anal. 57, 35–61 (2004)

    Google Scholar 

  214. J.K. Hunter, B. Nachtergaele, Applied Analysis (World Scientific, Singapore, 2000)

    Google Scholar 

  215. A.N. Iusem, A.R. De Pierro, Convergence results for an accelerated nonlinear Cimmino algorithm. Numer. Math. 49, 367–378 (1986)

    Google Scholar 

  216. A.N. Iusem, A.R. De Pierro, A simultaneous iterative method for computing projections on polyhedra. SIAM J. Contr. Optim. 25, 231–243 (1987)

    Google Scholar 

  217. A.N. Iusem, A.R. De Pierro, On the convergence properties of Hildreth’s quadratic programming algorithm. Math. Program. (Ser. A) 47, 37–51 (1990)

    Google Scholar 

  218. A.N. Iusem, B.F. Svaiter, A row-action method for convex programming. Math. Program. 64, 149–171 (1994)

    Google Scholar 

  219. B.K. Jennison, J.P. Allebach, D.W. Sweeney, Iterative approaches to computer-generated holography. Opt. Eng. 28, 629–637 (1989)

    Google Scholar 

  220. M. Jiang, G. Wang, Development of iterative algorithms for image reconstruction. J. X-Ray Sci. Tech. 10, 77–86 (2002)

    Google Scholar 

  221. M. Jiang, G. Wang, Convergence studies on iterative algorithms for image reconstruction. IEEE Trans. Med. Imag. 22, 569–579 (2003)

    Google Scholar 

  222. B. Johansson, T. Elfving, V. Kozlov, Y. Censor, P.-E. Forssén, G. Granlund, The application of an oblique-projected Landweber method to a model of supervised learning. Math. Comput. Model. 43, 892–909 (2006)

    Google Scholar 

  223. S. Kaczmarz, Angenäherte Auflösung von Systemen linearer Gleichungen. Bulletin International de l’Académie Polonaise des Sciences et des Lettres A35, 355–357 (1937); English translation: S. Kaczmarz, Approximate solution of systems of linear equations. Int. J. Contr. 57, 1269–1271 (1993)

    Google Scholar 

  224. A.C. Kak, M. Slaney, Principles of Computerized Tomographic Imaging (IEEE, New York, 1988)

    Google Scholar 

  225. I.G. Kazantsev, S. Schmidt, H.F. Poulsen, A discrete spherical x-ray transform of orientation distribution functions using bounding cubes. Inverse Probl. 25, 105009 (2009)

    Google Scholar 

  226. D. Kinderlehrer, G. Stampacchia, An Introduction to Variational Inequalities and Their Applications (Academic, New York, 1980)

    Google Scholar 

  227. W.A. Kirk, A fixed point theorem for mappings which do not increase distances. Am. Math. Mon. 72, 1004–1006 (1965)

    Google Scholar 

  228. Yu.N. Kiseliov, Algorithms of projection of a point onto an ellipsoid. Lithuanian Math. J. 34, 141–159 (1994)

    Google Scholar 

  229. K.C. Kiwiel, Block-iterative surrogate projection methods for convex feasibility problems. Lin. Algebra Appl. 215, 225–259 (1995)

    Google Scholar 

  230. K.C. Kiwiel, The efficiency of subgradient projection methods for convex optimization. I. General level methods. SIAM J. Contr. Optim. 34, 660–676 (1996)

    Google Scholar 

  231. K.C. Kiwiel, The efficiency of subgradient projection methods for convex optimization, II. Implementations and extensions. SIAM J. Contr. Optim. 34, 677–697 (1996)

    Google Scholar 

  232. K.C. Kiwiel, Monotone Gram matrices and deepest surrogate inequalities in accelerated relaxation methods for convex feasibility problems. Lin. Algebra Appl. 252, 27–33 (1997)

    Google Scholar 

  233. K.C. Kiwiel, B. Łopuch, Surrogate projection methods for finding fixed points of firmly nonexpansive mappings. SIAM J. Opt. 7, 1084–1102 (1997)

    Google Scholar 

  234. A. Kiełbasiński, H. Schwetlick, Numerical Linear Algebra (in German) (Verlag Harri Deutsch, Thun, 1988); Polish translation: Numeryczna algebra liniowa (WNT, Warszawa, 1992)

    Google Scholar 

  235. E. Kopecká, S. Reich, A note on the von Neumann alternating projections algorithm. J. Nonlinear Convex Anal. 5, 379–386 (2004)

    Google Scholar 

  236. E. Kopecká, S. Reich, Another note on the von Neumann alternating projections algorithm. J. Nonlinear Convex Anal. 11, 455–460 (2010)

    Google Scholar 

  237. G.M. Korpelevich, The extragradient method for finding saddle points and other problems. Ekonomika i Matematicheskie Metody 12, 747–756 (1976)

    Google Scholar 

  238. M.A. Krasnosel’skiĭ, Two remarks on the method of successive approximations (in Russian). Uspehi Mat. Nauk 10, 123–127 (1955)

    Google Scholar 

  239. S. Kwapień, J. Mycielski, On the Kaczmarz algorithm of approximation in infinite-dimensional spaces. Studia Math. 148, 5–86 (2001)

    Google Scholar 

  240. L. Landweber, An iteration formula for Fredholm integral equations of the first kind. Am. J. Math. 73, 615–624 (1951)

    Google Scholar 

  241. S. Lee, P.S. Cho, R.J. Marks, S.Oh, Conformal radiotherapy computation by the method of alternating projections onto convex sets. Phys. Med. Biol. 42, 1065–1086 (1997)

    Google Scholar 

  242. S.-H. Lee, K.-R. Kwon, Mesh watermarking based projection onto two convex sets. Multimedia Syst. 13, 323–330 (2008)

    Google Scholar 

  243. A. Lent, in A Convergent Algorithm for Maximum Entropy Image Restoration with a Medical X-ray Application, ed. by R. Shaw. Image Analysis and Evaluation (SPSE, Washington DC), pp. 249–257

    Google Scholar 

  244. A. Lent, Y. Censor, Extensions of Hildreth’s row-action method for quadratic programming. SIAM J. Contr. Optim. 18, 444–454 (1980)

    Google Scholar 

  245. A.W.-C. Liew, H. Yan, N.-F. Law, POCS-based blocking artifacts suppression using a smoothness constraint set with explicit region modeling. IEEE Trans. Circ. Syst. Video Tech. 15, 795–800 (2005)

    Google Scholar 

  246. P.L. Lions, B. Mercier, Splitting algorithms for the sum of two nonlinear operators. SIAM J. Numer. Anal. 16, 964–979 (1979)

    Google Scholar 

  247. C. Liu, An acceleration scheme for row projection methods. J. Comput. Appl. Math. 57, 363–391 (1995)

    Google Scholar 

  248. Y.M. Lu, M. Karzand, M. Vetterli, Demosaicking by alternating projections: theory and fast one-step implementation. IEEE Trans. Image Process. 19, 2085–2098 (2010)

    Google Scholar 

  249. P.-E. Maingé, Inertial iterative process for fixed points of certain quasi-nonexpansive mappings. Set-Valued Anal. 15, 67–79 (2007)

    Google Scholar 

  250. P.-E. Maingé, Extension of the hybrid steepest descent method to a class of variational inequalities and fixed point problems with nonself-mappings. Numer. Funct. Anal. Optim. 29, 820–834 (2008)

    Google Scholar 

  251. P.-E. Maingé, New approach to solving a system of variational inequalities and hierarchical problems. J. Optim. Theor. Appl. 138, 459–477 (2008)

    Google Scholar 

  252. W.R. Mann, Mean value methods in iteration. Proc. Am. Math. Soc. 4, 506–510 (1953)

    Google Scholar 

  253. Şt. Măruşter, The solution by iteration of nonlinear equations in Hilbert spaces. Proc. Am. Math. Soc. 63, 69–73 (1977)

    Google Scholar 

  254. Şt. Măruşter, Quasi-nonexpansivity and the convex feasibility problem. An. Ştiinţ. Univ. Al. I. Cuza Iaşi Inform. (N.S.) 15, 47–56 (2005)

    Google Scholar 

  255. Şt. Măruşter, C. Popîrlan, On the Mann-type iteration and the convex feasibility problem. J. Comput. Appl. Math. 212, 390–396 (2008)

    Google Scholar 

  256. Şt. Măruşter, C. Popîrlan, On the regularity condition in a convex feasibility problem. Nonlinear Anal. 70, 1923–1928 (2009)

    Google Scholar 

  257. E. Masad, S. Reich, A note on the multiple-set split convex feasibility problem in Hilbert space. J. Nonlinear Convex Anal. 8, 367–371 (2007)

    Google Scholar 

  258. E. Matoušková, S. Reich, The Hundal example revisited. J. Nonlinear Convex Anal. 4, 411–427 (2003)

    Google Scholar 

  259. S.F. McCormick, The methods of Kaczmarz and row orthogonalization for solving linear equations and least squares problems in Hilbert space. Indiana Univ. Math. J. 26, 1137–1150 (1977)

    Google Scholar 

  260. Yu.I. Merzlyakov, On a relaxation method of solving systems of linear inequalities (in Russian). Zh. Vychisl. Mat. Mat. Fiz. 2, 482–487 (1962)

    Google Scholar 

  261. D. Michalski, Y. Xiao, Y. Censor, J.M. Galvin, The dose-volume constraint satisfaction problem for inverse treatment planning with field segments. Phys. Med. Biol. 49, 601–616 (2004)

    Google Scholar 

  262. W. Mlak, Introduction to Hilbert Spaces (in Polish) (PWN, Warsaw, 1982)

    Google Scholar 

  263. W. Mlak, Hilbert Spaces and Operator Theory (Kluwer Academic, Boston, 1991)

    Google Scholar 

  264. J.-J. Moreau, Décomposition orthogonale d’un espace hilbertien selon deux cônes mutuellement polaires. C. R. Acad. Sci. Paris 255, 238–240 (1962)

    Google Scholar 

  265. J. Moreno, B. Datta, M. Raydan, A symmetry preserving alternating projection method for matrix model updating. Mech. Syst. Signal Process. 23, 1784–1791 (2009)

    Google Scholar 

  266. T.S. Motzkin, I.J. Schoenberg, The relaxation method for linear inequalities. Can. J. Math. 6, 393–404 (1954)

    Google Scholar 

  267. J. Musielak, Introduction to Functional Analysis (in Polish) (PWN, Warszawa, 1989)

    Google Scholar 

  268. J. Mycielski, S. Świerczkowski, Uniform approximation with linear combinations of reproducing kernels. Studia Math. 121, 105–114 (1996)

    Google Scholar 

  269. N. Nadezhkina, W. Takahashi, Strong convergence theorem by a hybrid method for nonexpansive mappings and Lipschitz-continuous monotone mappings. SIAM J. Optim. 16, 1230–1241 (2006)

    Google Scholar 

  270. F. Natterer, The Mathematics of Computerized Tomography (Wiley, Chichester, 1986)

    Google Scholar 

  271. J. von Neumann, in Functional Operators – Vol. II. The Geometry of Orthogonal Spaces. Annals of Mathematics Studies, vol. 22 (Princeton University Press, Princeton, 1950) (Reprint of mimeographed lecture notes first distributed in 1933)

    Google Scholar 

  272. O. Nevanlinna, S. Reich, Strong convergence of contraction semigroups and of iterative methods for accretive operators in Banach spaces. Israel J. Math. 32, 44–58 (1979)

    Google Scholar 

  273. N. Ogura, I. Yamada, Non-strictly convex minimization over the fixed point set of an asymptotically shrinking nonexpansive mapping. Numer. Funct. Anal. Optim. 23, 113–137 (2002)

    Google Scholar 

  274. N. Ogura, I. Yamada, Nonstrictly convex minimization over the bounded fixed point set of a nonexpansive mapping. Numer. Funct. Anal. Optim. 24, 129–135 (2003)

    Google Scholar 

  275. S. Oh, R.J. Marks, L.E. Atlas, Kernel synthesis for generalized time-frequency distributions using the method of alternating projections onto convex sets. IEEE Trans. Signal Process. 42, 1653–1661 (1994)

    Google Scholar 

  276. J.G. O’Hara, P. Pillay, H.-K. Xu, Iterative approaches to finding nearest common fixed points of nonexpansive mappings in Hilbert spaces. Nonlinear Anal. 54, 1417–1426 (2003)

    Google Scholar 

  277. S.O. Oko, Surrogate methods for linear inequalities. J. Optim. Theor. Appl. 72, 247–268 (1992)

    Google Scholar 

  278. Z. Opial, Nonexpansive and Monotone Mappings in Banach Spaces. Lecture Notes 67-1, Center for Dynamical Systems, Brown University, Providence, RI, 1967

    Google Scholar 

  279. Z. Opial, Weak convergence of the sequence of successive approximations for nonexpansive mappings. Bull. Am. Math. Soc. 73, 591–597 (1967)

    Google Scholar 

  280. S.C. Park, M.K. Park, M.G. Kang, Super-resolution image reconstruction: A technical overview. IEEE Signal Process. Mag. 20, 21–36 (2003)

    Google Scholar 

  281. J. Park, D.C. Park, R.J. Marks, M. El-Sharkawi, Recovery of image blocks using the method of alternating projections. IEEE Trans. Image Process. 14, 461–474 (2005)

    Google Scholar 

  282. S.N. Penfold, R.W. Schulte, Y. Censor, A.B. Rosenfeld, Total variation superiorization schemes in proton computed tomography image reconstruction. Med. Phys. 37, 5887–5895 (2010)

    Google Scholar 

  283. W.V. Petryshyn, T.E. Williamson Jr., Strong and weak convergence of the sequence of successive approximations for quasi-nonexpansive mappings. J. Math. Anal. Appl. 43, 459–497 (1973)

    Google Scholar 

  284. G. Pierra, Decomposition through formalization in a product space. Math. Program. 28, 96–115 (1984)

    Google Scholar 

  285. C. Popa, Least-squared solution of overdetermined inconsistent linear systems using Kaczmarz’s relaxation. Int. J. Comp. Math. 55, 79–89 (1995)

    Google Scholar 

  286. C. Popa, Extensions of block-projections methods with relaxation parameters to inconsistent and rank-deficient least-squares problems. BIT 38, 151–176 (1998)

    Google Scholar 

  287. C. Popa, R. Zdunek, Kaczmarz extended algorithm for tomographic image reconstruction from limited data. Math. Comput. Simulat. 65, 579–598 (2004)

    Google Scholar 

  288. S. Prasad, Generalized array pattern synthesis by the method of alternating orthogonal projections. IEEE Trans. Antenn. Propag. 28, 328–332 (1980)

    Google Scholar 

  289. J.L. Prince, A.S. Willsky, A geometric projection-space reconstruction algorithm. Lin. Algebra Appl. 130, 151–191 (1990)

    Google Scholar 

  290. E. Pustylnik, S. Reich, A.J. Zaslavski, Convergence of infinite products of nonexpansive operators in Hilbert space. J. Nonlinear Convex Anal. 11, 461–474 (2010)

    Google Scholar 

  291. E. Pustylnik, S. Reich, A.J. Zaslavski, Convergence of non-cyclic infinite products of operators. J. Math. Anal. Appl. 380, 759–767 (2011)

    Google Scholar 

  292. B. Qu, N. Xiu, A note on the CQ algorithm for the split feasibility problem. Inverse Probl. 21, 1655–1665 (2005)

    Google Scholar 

  293. B. Qu, N. Xiu, A new halfspace-relaxation projection method for the split feasibility problem. Lin. Algebra Appl. 428, 1218–1229 (2008)

    Google Scholar 

  294. S. Reich, Weak convergence theorems for nonexpansive mappings in Banach spaces. J. Math. Anal. Appl. 67, 274–276 (1979)

    Google Scholar 

  295. S. Reich, A limit theorem for projections. Lin. Multilinear Algebra 13, 281–290 (1983)

    Google Scholar 

  296. S. Reich and I. Shafrir, The asymptotic behavior of firmly nonexpansive mappings. Proc. Am. Math. Soc. 101, 246–250 (1987)

    Google Scholar 

  297. S. Reich, A.J. Zaslavski, Attracting mappings in Banach and hyperbolic spaces. J. Math. Anal. Appl. 253, 250–268 (2001)

    Google Scholar 

  298. R.T. Rockafellar, Convex Analysis (Princeton University Press, Princeton, 1970)

    Google Scholar 

  299. R.T. Rockafellar, Monotone operators and the proximal point algorithm. SIAM J. Contr. Optim. 14, 877–898 (1976)

    Google Scholar 

  300. W. Rudin, Functional Analysis, 2nd edn. (McGraw-Hill, New York, 1991); Polish translation: Analiza funkcjonalna (PWN, Warszawa, 2002)

    Google Scholar 

  301. A.A. Samsonov, E.G. Kholmovski, D.L. Parker, C.R. Johnson, POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging. Magn. Reson. Med. 52, 1397–1406 (2004)

    Google Scholar 

  302. J. Schauder, Der Fixpunktsatz in Funktionalräumen. Studia Math. 2, 171–180 (1930)

    Google Scholar 

  303. D. Schott, A general iterative scheme with applications to convex optimization and related fields. Optimization 22, 885–902 (1991)

    Google Scholar 

  304. H.D. Scolnik, N. Echebest, M.T. Guardarucci, M.C. Vacchino, A class of optimized row projection methods for solving large nonsymmetric linear systems. Appl. Numer. Math. 41, 499–513 (2002)

    Google Scholar 

  305. H.D. Scolnik, N. Echebest, M.T. Guardarucci, M.C. Vacchino, Acceleration scheme for parallel projected aggregation methods for solving large linear systems. Ann. Oper. Res. 117, 95–115 (2002)

    Google Scholar 

  306. H.D. Scolnik, N. Echebest, M.T. Guardarucci, M.C. Vacchino, Incomplete oblique projections for solving large inconsistent linear systems. Math. Program. 111, 273–300 (2008)

    Google Scholar 

  307. A. Segal, Directed Operators for Common Fixed Point Problems and Convex Programming Problems, Ph.D. Thesis, University of Haifa, Haifa, Israel, 2008

    Google Scholar 

  308. H.F. Senter, W.G. Dotson Jr., Approximating fixed points of nonexpansive mappings. Proc. Am. Math. Soc. 44, 375–380 (1974)

    Google Scholar 

  309. A. Serbes, L. Durak, Optimum signal and image recovery by the method of alternating projections in fractional Fourier domains. Comm. Nonlinear Sci. Numer. Simulat. 15, 675–689 (2010)

    Google Scholar 

  310. N.T. Shaked, J. Rosen, Multiple-viewpoint projection holograms synthesized by spatially incoherent correlation with broadband functions. J. Opt. Soc. Am. A 25, 2129–2138 (2008)

    Google Scholar 

  311. G. Sharma, Set theoretic estimation for problems in subtractive color. Color Res. Appl. 25, 333–348 (2000)

    Google Scholar 

  312. K.K. Sharma, S.D. Joshi, Extrapolation of signals using the method of alternating projections in fractional Fourier domains. Signal Image Video Process. 2, 177–182 (2008)

    Google Scholar 

  313. K.T. Smith, D.C. Solman, S.L. Wagner, Practical and mathematical aspects of the problem of reconstructing objects from radiographs. Bull. Am. Math. Soc. 83, 1227–1270 (1977)

    Google Scholar 

  314. R.A. Soni, K.A. Gallivan, W.K. Jenkins, Low-complexity data reusing methods in adaptive filtering. IEEE Trans. Signal Process. 52, 394–405 (2004)

    Google Scholar 

  315. H. Stark, P. Oskoui, High resolution image recovery from image-plane arrays, using convex projections. J. Opt. Soc. Am. A 6, 1715–1726 (1989)

    Google Scholar 

  316. H. Stark, Y. Yang, Vector Space Projections. A Numerical Approach to Signal and Image Processing, Neural Nets and Optics (Wiley, New York, 1998)

    Google Scholar 

  317. J. Stoer, R. Bulirsch, Introduction to Numerical Analysis, 3rd edn. (Springer, New York, 2002)

    Google Scholar 

  318. C. Sudsukh, Strong convergence theorems for fixed point problems, equilibrium problems and applications. Int. J. Math. Anal. (Ruse) 3, 1867–1880 (2009)

    Google Scholar 

  319. S. Świerczkowski, A model of following. J. Math. Anal. Appl. 222, 547–561 (1998)

    Google Scholar 

  320. W. Takahashi, Y. Takeuchi, R. Kubota, Strong convergence theorems by hybrid methods for families of nonexpansive mappings in Hilbert spaces. J. Math. Anal. Appl. 341, 276–286 (2008)

    Google Scholar 

  321. K. Tanabe, Projection method for solving a singular system of linear equations and its applications. Numer. Math. 17, 203–214 (1971)

    Google Scholar 

  322. K. Tanabe, Characterization of linear stationary iterative processes for solving a singular system of linear equations. Numer. Math. 22, 349–359 (1974)

    Google Scholar 

  323. G. Tetzlaff, K. Arnold, A. Raabe, A. Ziemann, Observations of area averaged near-surface wind- and temperature-fields in real terrain using acoustic travel time tomography. Meteorologische Zeitschrift 11, 273–283 (2002)

    Google Scholar 

  324. Ch. Thieke, T. Bortfeld, A. Niemierko, S. Nill, From physical dose constraints to equivalent uniform dose constraints in inverse radiotherapy planning. Med. Phys. 30, 2332–2339 (2003)

    Google Scholar 

  325. J. van Tiel, Convex Analysis, An Introductory Text (Wiley, Chichester, 1984)

    Google Scholar 

  326. M.J. Todd, Some Remarks on the Relaxation Method for Linear Inequalities, Technical Report, vol. 419, Cornell University, Cornell, Ithaca, 1979

    Google Scholar 

  327. Ph.L. Toint, Global convergence of a class of trust region methods for nonconvex minimization in Hilbert space. IMA J. Numer. Anal. 8, 231–252 (1988)

    Google Scholar 

  328. W. Treimer, U. Feye-Treimer, Two dimensional reconstruction of small angle scattering patterns from rocking curves. Physica B 241–243, 1228–1230 (1998)

    Google Scholar 

  329. J.A. Tropp, I.S. Dhillon, R.W. Heath, T. Strohmer, Designing structured tight frames via an alternating projection method. IEEE Trans. Inform. Theor. 51, 188–209 (2005)

    Google Scholar 

  330. M.R. Trummer, SMART – an algorithm for reconstructing pictures from projections. J. Appl. Math. Phys. 34, 746–753 (1983)

    Google Scholar 

  331. P. Tseng, On the convergence of the products of firmly nonexpansive mappings. SIAM J. Optim. 2, 425–434 (1992)

    Google Scholar 

  332. A. Van der Sluis, H.A. Van der Vorst, in Numerical Solution of Large Sparse Linear Algebraic Systems Arising from Tomographic Problems, ed. by G. Nolet. Seismic Tomography (Reidel, Dordrecht, 1987)

    Google Scholar 

  333. V.V. Vasin, A.L. Ageev, Ill-Posed Problems with A Priori Information (VSP, Utrecht, 1995)

    Google Scholar 

  334. S. Webb, Intensity Modulated Radiation Therapy (Institute of Physics Publishing, Bristol, 2001)

    Google Scholar 

  335. S. Webb, The Physics of Conformal Radiotherapy (Institute of Physics Publishing, Bristol, 2001)

    Google Scholar 

  336. R. Webster, Convexity (Oxford University Press, Oxford, 1994)

    Google Scholar 

  337. R. Wegmann, Conformal mapping by the method of alternating projections. Numer. Math. 56, 291–307 (1989)

    Google Scholar 

  338. R. Wittmann, Approximation of fixed points of nonexpansive mappings. Arch. Math. 58, 486–491 (1992)

    Google Scholar 

  339. P. Wolfe, Finding the nearest point in a polytope. Math. Program. 11, 128–149 (1976)

    Google Scholar 

  340. B.J. van Wyk, M.A. van Wyk, A POCS-based graph matching algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1526–1530 (2004)

    Google Scholar 

  341. Y. Xiao, Y. Censor, D. Michalski, J.M. Galvin, The least-intensity feasible solution for aperture-based inverse planning in radiation therapy. Ann. Oper. Res. 119, 183–203 (2003)

    Google Scholar 

  342. H.-K. Xu, Iterative algorithms for nonlinear operators. J. Lond. Math. Soc. 66, 240–256 (2002)

    Google Scholar 

  343. H.-K. Xu, An iterative approach to quadratic optimization. J. Optim. Theor. Appl. 116, 659–678 (2003)

    Google Scholar 

  344. H.-K. Xu, A variable Krasnosel’skiĭ-Mann algorithm and the multiple-set split feasibility problem. Inverse Probl. 22, 2021–2034 (2006)

    Google Scholar 

  345. I. Yamada, in The Hybrid Steepest Descent Method for the Variational Inequality Problem Over the Intersection of Fixed Point Sets of Nonexpansive Mappings, ed. by D. Butnariu, Y. Censor, S. Reich. Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, Studies in Computational Mathematics, vol. 8 (Elsevier Science, Amsterdam, 2001), pp. 473–504

    Google Scholar 

  346. I. Yamada, N. Ogura, Hybrid steepest descent method for variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings. Numer. Funct. Anal. Optim. 25, 619–655 (2004)

    Google Scholar 

  347. I. Yamada, N. Ogura, Adaptive projected subgradient method for asymptotic minimization of sequence of nonnegative convex functions. Numer. Funct. Anal. Optim. 25, 593–617 (2004)

    Google Scholar 

  348. I. Yamada, N. Ogura, N. Shirakawa, in A Numerically Robust Hybrid Steepest Descent Method for the Convexly Constrained Generalized Inverse Problems, ed. by Z. Nashed, O. Scherzer. Inverse Problems, Image Analysis and Medical Imaging, American Mathematical Society, Contemp. Math., vol. 313 (2002), pp. 269–305

    Google Scholar 

  349. I. Yamada, N. Ogura, Y. Yamashita, K. Sakaniwa, Quadratic optimization of fixed points of nonexpansive mappings in Hilbert space. Numer. Funct. Anal. Optim. 19, 165–190 (1998)

    Google Scholar 

  350. Q.Z. Yang, The relaxed CQ algorithm solving the split feasibility problem. Inverse Probl. 20, 1261–1266 (2004)

    Google Scholar 

  351. K. Yang, K.G. Murty, New iterative methods for linear inequalities. JOTA 72, 163–185 (1992)

    Google Scholar 

  352. Q. Yang, J. Zhao, Generalized KM theorems and their applications. Inverse Probl. 22, 833–844 (2006)

    Google Scholar 

  353. Y. Yao, Y.-C. Liou, Weak and strong convergence of Krasnoselski–Mann iteration for hierarchical fixed point problems. Inverse Probl. 24, 015015, 8 (2008)

    Google Scholar 

  354. D. Youla, Generalized image restoration by the method of alternating orthogonal projections. IEEE Trans. Circ. Syst. 25, 694–702 (1978)

    Google Scholar 

  355. M. Yukawa, I. Yamada, Pairwise optimal weight realization – Acceleration technique for set-theoretic adaptive parallel subgradient projection algorithm. IEEE Trans. Signal Process. 54, 4557–4571 (2006)

    Google Scholar 

  356. M. Zaknoon, Algorithmic Developments for the Convex Feasibility Problem, Ph.D. Thesis, University of Haifa, Haifa, Israel, 2003

    Google Scholar 

  357. E.H. Zarantonello, in Projections on Convex Sets in Hilbert Space and Spectral Theory, ed. by E.H. Zarantonello. Contributions to Nonlinear Functional Analysis (Academic, New York, 1971), pp. 237–424

    Google Scholar 

  358. E. Zeidler, Nonlinear Functional Analysis and Its Applications, III – Variational Methods and Optimization (Springer, New York, 1985)

    Google Scholar 

  359. J. Zhang, A.K. Katsaggelos, in Image Recovery Using the EM Algorithm, ed. by V.K. Madisetti, D.B. Williams. Digital Signal Processing Handbook (CRC Press LLC, Boca Raton, 1999)

    Google Scholar 

  360. D.F. Zhao, The principles and practice of iterative alternating projection algorithm: Solution for non-LTE stellar atmospheric model with the method of linearized separation. Chin. Astron. Astrophys. 25, 305–316 (2001)

    Google Scholar 

  361. J. Zhao, Q. Yang, Several solution methods for the split feasibility problem. Inverse Probl. 21, 1791–1799 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cegielski, A. (2012). Algorithmic Operators. In: Iterative Methods for Fixed Point Problems in Hilbert Spaces. Lecture Notes in Mathematics, vol 2057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30901-4_2

Download citation

Publish with us

Policies and ethics