Abstract
The class of splitting preconditioners for the iterative solution of linear systems arising from Mehrotra’s predictor-corrector method for large scale linear programming problems needs to find a basis through a sophisticated process based on the application of a rectangular LU factorization. This class of splitting preconditioners works better near a solution of the linear programming problem when the matrices are highly ill-conditioned. In this study, we develop and implement a new approach to find a basis for the splitting preconditioner, based on standard rectangular LU factorization with partial permutation of the scaled transpose linear programming constraint matrix. In most cases, this basis is better conditioned than the existing one. In addition, we include a penalty parameter in Mehrotra’s predictor-corrector method in order to reduce ill-conditioning of the normal equations matrix. Computational experiments show a reduction in the average number of iterations of the preconditioned conjugate gradient method. Also, the increased efficiency and robustness of the new approach become evident by the performance profile.
Similar content being viewed by others
References
Amestoy, P.R., Davis, T.A., Duff, I.S.: Algorithm 837: Amd, an approximate minimum degree ordering algorithm. ACM Trans. Math. Softw. (TOMS) 30(3), 381–388 (2004)
Bergamaschi, L., Gondzio, J., Zilli, G.: Preconditioning indefinite systems in interior point methods for optimization. Comput. Optim. Appl. 28(2), 149–171 (2004)
Bocanegra, S., Campos, F., Oliveira, A.R.: Using a hybrid preconditioner for solving large-scale linear systems arising from interior point methods. Comput. Optim. Appl. 36(2–3), 149–164 (2007)
Campos, F.F., Rollet, J.S.: Controlled Cholesky Factorization for Preconditioning the Conjugate Gradient Method. Oxford University Computing Laboratory, Numerical Analysis Group (1995)
Carolan, W.J., Hill, J.E., Kennington, J.L., Niemi, S., Wichmann, S.J.: An empirical evaluation of the korbx \(\textregistered \) algorithms for military airlift applications. Oper. Res. 38(2), 240–248 (1990)
Chai, J., Toh, K.: Preconditioning and iterative solution of symmetric indefinite linear system arising from interior point methods for linear programming. Comput. Optim. Appl. 36, 221–247 (2007)
Czyzyk, J., Mehrotra, S., Wagner, M., Wright, S.J.: PCx an interior point code for linear programming. Optim. Methods Softw. 11–2(1–4), 397–430 (1999)
Davis, T.A.: Algorithm 832: Umfpack v4. 3—an unsymmetric-pattern multifrontal method. ACM Trans. Math. Softw. (TOMS) 30(2), 196–199 (2004)
Davis, T.A., Duff, I.S.: An unsymmetric-pattern multifrontal method for sparse lu factorization. SIAM J. Matrix Anal. Appl. 18(1), 140–158 (1997)
Dolan, E.D., More, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)
Drazic, M.D., Lazovic, R.P., Kovacevic-Vujcic, V.V.: Sparsity preserving preconditioners for linear systems in interior-point methods. Comput. Optim. Appl. 61(3), 557–570 (2015)
Duff, I.S.: Ma57–a code for the solution of sparse symmetric definite and indefinite systems. ACM Trans. Math. Softw. (TOMS) 30(2), 118–144 (2004)
Duff, I.S., Pralet, S.: Towards stable mixed pivoting strategies for the sequential and parallel solution of sparse symmetric indefinite systems. SIAM J. Matrix Anal. Appl. 29(3), 1007–1024 (2007)
Ghidini, C.T.L.S., Oliveira, A.R.L., Silva, J., Velazco, M.I.: Combining a hybrid preconditioner and a optimal adjustment algorithm to accelerate the convergence of interior point methods. Linear Algebra Appl. 218, 1267–1284 (2012)
Ghidini, C.T.L.S., Oliveira, A.R.L., Sorensen, D.C.: Computing a hybrid preconditioner approach to solve the linear systems arising from interior point methods for linear programming using the gradient conjugate method. Ann. Manag. Sci. 3, 45–66 (2014)
Golub, G.H., Van Loan, C.F.: Matrix Computations. JHU Press, Baltimore (2013)
Gondzio, J.: Interior point methods 25 years later. Eur. J. Oper. Res. 218(3), 587–601 (2012)
Kershaw, D.S.: The incomplete cholesky-conjugate gradient method for the iterative solution of systems of linear equations. J. Comput. Phys. 26(1), 43–65 (1978)
Kuzmin, A., Luisier, M., Schenk, O.: Fast methods for computing selected elements of the greens function in massively parallel nanoelectronic device simulations. In: Wolf, F., Mohr, B., Mey, D. (eds.) Euro-Par 2013 Parallel Processing. Lecture Notes in Computer Science, vol. 8097, pp. 533–544. Springer, Berlin (2013). doi:10.1007/978-3-642-40047-6_54
Luenberger, D.G.: Linear and Nonlinear Programming. Springer, New York (2003)
Lustig, I.J., Marsten, R.E., Shanno, D.F.: On implementing mehrotra’s predictor-corrector interior-point method for linear programming. SIAM J. Optim. 2(3), 435–449 (1992)
Markowitz, H.M.: The elimination form of the inverse and its application to linear programming. Manag. Sci. 3(3), 255–269 (1957)
Mehrotra, S.: On the implementation of a primal-dual interior point method. SIAM J. Optim. 2, 575–601 (1992)
Oliveira, A.R., Sorensen, D.C.: A new class of preconditioners for large-scale linear systems from interior point methods for linear programming. Linear Algebra Appl. 394, 1–24 (2005)
Schenk, O., Bollhöfer, M., Römer, R.A.: On large-scale diagonalization techniques for the Anderson model of localization. SIAM Rev. 50(1), 91–112 (2008). doi:10.1137/070707002
Schenk, O., Wächter, A., Hagemann, M.: Matching-based preprocessing algorithms to the solution of saddle-point problems in large-scale nonconvex interior-point optimization. Comput. Optim. Appl. 36(2–3), 321–341 (2007). doi:10.1007/s10589-006-9003-y
Velazco, M., Oliveira, A.R., Campos, F.: A note on hybrid preconditioners for large-scale normal equations arising from interior-point methods. Optim. Methods Softw. 25(2), 321–332 (2010)
Wright, S.J.: Primal-Dual Interior-Point Methods, vol. 54. SIAM (1997)
Yannakakis, M.: Computing the minimum fill-in is NP-complete. SIAM J. Algebr. Discret. Methods 2(1), 77–79 (1981)
Acknowledgements
Thanks to CNPq, FAPESP and UMSA for their financial support.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Suñagua, P., Oliveira, A.R.L. A new approach for finding a basis for the splitting preconditioner for linear systems from interior point methods. Comput Optim Appl 67, 111–127 (2017). https://doi.org/10.1007/s10589-016-9887-0
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10589-016-9887-0