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An Accelerated Iterative Method with Diagonally Scaled Oblique Projections for Solving Linear Feasibility Problems

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Abstract

The Projected Aggregation Methods (PAM) for solving linear systems of equalities and/or inequalities, generate a new iterate xk+1 by projecting the current point xk onto a separating hyperplane generated by a given linear combination of the original hyperplanes or halfspaces. In Scolnik et al. (2001, 2002a) and Echebest et al. (2004) acceleration schemes for solving systems of linear equations and inequalities respectively were introduced, within a PAM like framework. In this paper we apply those schemes in an algorithm based on oblique projections reflecting the sparsity of the matrix of the linear system to be solved. We present the corresponding theoretical convergence results which are a generalization of those given in Echebest et al. (2004). We also present the numerical results obtained applying the new scheme to two algorithms introduced by Garcí a-Palomares and González-Castaño (1998) and also the comparison of its efficiency with that of Censor and Elfving (2002).

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References

  • Bauschke, H.H. and J.M. Borwein. (1996). “On Projection Algorithms for Solving Convex Feasibility Problems.” SIAM Rev. 38, 367–426.

    Article  Google Scholar 

  • Censor, Y. (1988). “Parallel Application of Block-Iterative Methods in Medical Imaging and Radiation Therapy.” Math. Programming 42, 307–325.

    Article  Google Scholar 

  • Censor, Y., D. Gordon, and R. Gordon. (2001a). “Component Averaging: An Efficient Iterative Parallel Algorithm for Large and Sparse Unstructured Problems.” Parallel Computing 27, 777–808.

    Article  Google Scholar 

  • Censor, Y., D. Gordon, and R. Gordon. (2001b). “BICAV: An Inherently Parallel Algorithm for Sparse Systems with Pixel-Dependent Weighting.” IEEE Trans. on Medical Imaging 20, 1050–1060.

    Article  Google Scholar 

  • Censor, Y. and T. Elfving. (2002). “Block-Iterative Algorithms with Diagonally Scaled Oblique Projections for the Linear Feasibility Problem.” SIAM Journal on Matrix Analysis and Applications 24, 40– 58.

    Google Scholar 

  • Echebest, N., M.T. Guardarucci, H.D. Scolnik, and M.C. Vacchino. (2004). “An Acceleration Scheme for Solving Convex Feasibility Problems Using Incomplete Projection Algorithms.” Numerical Algorithms 35, 335–350.

    Article  Google Scholar 

  • Garcí a-Palomares, U.M. (1993). “Parallel Projected Aggregation Methods for Solving the Convex Feasibility Problem.” SIAM J. Optim. 3, 882–900.

    Article  Google Scholar 

  • Garcí a-Palomares, U.M. and F.J. González-Castaño. (1998). “Incomplete Projection Algorithms for Solving the Convex Feasibility Problem.” Numerical Algorithms 18, 177–193.

    Article  Google Scholar 

  • Gubin, L.G., B.T. Polyak, and E.V. Raik. (1967). “The Method of Projections for Finding the Common Point of Convex Sets.” USSR Comput. Math. and Math.Phys. 7, 1–24.

    Article  Google Scholar 

  • Iusem, A.N. and A. De Pierro. (1986). “Convergence Results for an Accelerated Nonlinear Cimmino Algorithm.” Numer. Math. 49, 367–378.

    Article  Google Scholar 

  • Herman, G.T. and L.B. Meyer. (1993). “Algebraic Reconstruction Techniques can be Made Computationally Efficient.” IEEE Trans. Medical Imaging 12, 600–609.

    Article  Google Scholar 

  • Saad, Y. (l990). “SPARSKIT: A Basic Tool Kit for Sparse Matrix Computations.” Technical Report 90-20, Research Institute for Avanced Computer Science. NASA Ames Research Center, Moffet Field, CA.

  • Scolnik, H., N. Echebest, M.T. Guardarucci, and M.C. Vacchino. (2001). “New Optimized and Accelerated PAM Methods for Solving Large Non-symmetric Linear Systems: Theory and Practice.” In D. Butnariu, Y. Censor, and S. Reich (eds.), Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, Studies in Computational Mathematics. Amsterdam: Elsevier Science, Volume 8, pp. 457–470.

  • Scolnik, H., N. Echebest, M.T. Guardarucci, and M.C. Vacchino. (2002a). “A Class of Optimized Row Projection Methods for Solving Large Non-Symmetric Linear Systems.” Applied Numerical Mathematics 41, 499–513.

    Article  Google Scholar 

  • Scolnik, H., N. Echebest, M.T. Guardarucci, and M.C. Vacchino. (2002b). “Acceleration Scheme for Parallel Projected Aggregation Methods for Solving Large Linear Systems.” Annals of Operations Research 117, 95–115.

    Article  Google Scholar 

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Correspondence to H. D. Scolnik.

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Echebest, N., Guardarucci, M.T., Scolnik, H.D. et al. An Accelerated Iterative Method with Diagonally Scaled Oblique Projections for Solving Linear Feasibility Problems. Ann Oper Res 138, 235–257 (2005). https://doi.org/10.1007/s10479-005-2456-z

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