Abstract
It is shown in this paper that under strict complementarity condition, a linear programming problem can be solved by a single orthogonal projection operation onto the cone generated by rows of constraint matrix and corresponding right-hand sides. The efficient projection procedure with the finite termination is provided and computational experiments are reported.
Similar content being viewed by others
References
Floudas, C.A., Gounaris, C.E.: A review of recent advances in global optimization. J. Glob. Optim. 45(1), 3–38 (2009)
Shen, P.: Linearization method of global optimization for generalized geometric programming. Appl. Math. Comput. 162, 353–370 (2005)
Meyer, C.D.: Matrix Analysis and Applied Linear Algebra, p. 718. SIAM, Philadelphia, PA (2000)
Bersecas, D.P.: Nonlinear Programming 780. Athena Scientific, Nashua (2004)
Solodov, M.V., Svaiter, B.F.: A new projection method for variational inequality problems. SIAM J. Control Optim. 37, 765–776 (1999)
Bauschke, H.H., Borwein, J.M.: On projection algorithms for solving convex feasibility problems. SIAM Rev. 38(3), 367–426 (1996)
Björck, A.: Numerical Methods for Least Squares Problems. SIAM, Philadelphia, PA (1996)
Censor, Y.: Row-action methods for huge and sparse systems and their applications. SIAM Rev. 23, 444–466 (1981)
Kaczmarz, S.: Angenherte Auflsung von Systemen linearer Gleichungen, Bulletin International de l’Acadmie Polonaise des Sciences et des Lettres. Classe des Sciences Mathmatiques et Naturelles. Srie A, Sciences Mathmatiques, v. 35, 355–357 (1937)
Cimmino, G.: Calcolo approssimato per le soluzioni dei sistemi di equazioni lineari. LaRicerca Scientifica (Roma) 1, 326–333 (1938)
Agmon, S.: The relaxation method for linear inequalities. Can. J. Math. 6, 382–392 (1954)
Motzkin, T.S., Schoenberg, I.J.: The relaxation method for linear inequalities. Can. J. Math. 6, 393–404 (1954)
Censor, Y., Zenios, S.A.: Parallel Optimization: Theory, Algorithms, and Applications. Oxford University Press, New York (1997)
Rami, M.A., Helmke, U., Moore, J.B.: A finite steps algorithm for solving convex feasibility problems. J. Glob. Optim. 38(1), 143–160 (2007)
Gould, N.I.M.: How good are projection methods for convex feasibility problems? Report no. NA-07/02 Numerical Analysis Group Oxford University Computing Laboratory Oxford University (see also Comput. Optim. Appl. 40, 1–12 (2008))
Censor, Y., Chen, W., Combettes, P.L., Davidi, R., Herman, G.T.: On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints. Comput. Optim. Appl. 51(3), 1065–1088 (2012)
Gould, N.I.M.: How good are extrapolated bi-projection methods for linear feasibility problems? Comput. Optim. Appl. 51(3), 1089–1095 (2012)
Khachiyan, L.G.: A polynomial algorithm in linear programming. Soviet Math. Dokl. 20, 191–194 (1979)
Khachiyan, L.G.: Polynomial algorithms in linear programming. USSR Comp. Math. 20(1), 51–68 (1980)
Deutsch, F., Hundal, H.: The rate of convergence for the cyclic projections algorithm I: Angles between convex sets. J. Approx. Theory 142(1), 36–55 (2006)
Deutsch, F., Hundal, H.: The rate of convergence for the cyclic projections algorithm III: Regularity of convex sets. J. Approx. Theory 155(2), 155–184 (2008)
Xiua, Naihua: Jianzhong Zhangb Some recent advances in projection-type methods for variational inequalities. J. Comput. Appl. Math. 152, 559–585 (2003)
Bertsecas, D.P.: Extended Monotropic Programming and Duality, 18, Report LIDS—2692, (2010)
Rockafellar, R.T.: Network Flows and Monotropic Optimization. Athena Scientific, Nashua (1998)
Von Hohenbalken, B.: A finite algorithm to maximize certain pseaudoconcave functions on polytopes. Math. Program. 13, 49–68 (1975)
Wolfe, P.: Finding the nearest point in a polytope. Math. Program. 13, 49–68 (1976)
Nurminski, E.A.: Convergence of the suitable affine subspace method for finding the least distance to a simplex. Comput. Math. Math. Phys. 45(11), 1915–1922 (2005)
Calamai, P.H., Mori, J.J.: Projected gradient methods for linearly constrained problems. Math. Program. 39, 93–116 (1987)
Author information
Authors and Affiliations
Corresponding author
Additional information
This research supported by the Russian Foundation for Basic Research Grant 13-07-12010.
Rights and permissions
About this article
Cite this article
Nurminski, E.A. Single-projection procedure for linear optimization. J Glob Optim 66, 95–110 (2016). https://doi.org/10.1007/s10898-015-0337-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-015-0337-9