Previous |  Up |  Next

Article

Title: An adaptive long step interior point algorithm for linear optimization (English)
Author: Salahi, Maziar
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 46
Issue: 4
Year: 2010
Pages: 722-729
Summary lang: English
.
Category: math
.
Summary: It is well known that a large neighborhood interior point algorithm for linear optimization performs much better in implementation than its small neighborhood counterparts. One of the key elements of interior point algorithms is how to update the barrier parameter. The main goal of this paper is to introduce an ``adaptive'' long step interior-point algorithm in a large neighborhood of central path using the classical logarithmic barrier function having $O(n\operatorname{log}\frac{(x^0)^Ts^0}{\epsilon})$ iteration complexity analogous to the classical long step algorithms. Preliminary encouraging numerical results are reported. (English)
Keyword: linear optimization
Keyword: interior point methods
Keyword: long step algorithms
Keyword: large neighborhood
Keyword: polynomial complexity
MSC: 90C05
MSC: 90C51
idZBL: Zbl 1203.90110
idMR: MR2722097
.
Date available: 2010-10-22T05:28:59Z
Last updated: 2013-09-21
Stable URL: http://hdl.handle.net/10338.dmlcz/140780
.
Reference: [1] Mehrotra, S.: On the implementation of a (primal-dual) interior point method. SIAM J. Optim. 2 (1992), 575–601. Zbl 0773.90047, MR 1186163, 10.1137/0802028
Reference: [2] Mizuno, S., Todd, M. J., Ye, Y.: On adaptive-step primal-dual interior-point algorithms for linear programming. Math. Oper. Res. 18 (1993), 4, 964–981. Zbl 0810.90091, MR 1251690, 10.1287/moor.18.4.964
Reference: [3] Potra, F. A.: A superliner convergent predictor–corrector method for degenerate LCP in a wide neighborhood of the central path with $O(\sqrt{n}L)$-iteration complexity. Math. Program. Ser. A 100 (2004), 2, 317–337. MR 2062930, 10.1007/s10107-003-0472-9
Reference: [4] Roos, C., Terlaky, T., Vial, J. P.: Interior Ooint Algorithms for Linear Optimization.Second edition. Springer Science, 2005.
Reference: [5] Salahi, M., Terlaky, T.: A hybrid adaptive algorithm for linear optimization. Asia-Pacific J. Oper. Res. 26 (2009), 2, 235–256. Zbl 1168.90616, MR 2536039, 10.1142/S0217595909002183
Reference: [6] Sonnevend, G.: An “analytic center" for polyhedrons and new classes of global algorithms for linear (smooth, convex) programming.In: Proc. 12th IFIP Conference System Modeling and Optimization (A. Prékopa, J. Szelezsán, and B. Strazicky, eds.), Budapest 1985. Lecture Notes in Control and Information Sciences, pp. 866–876. Springer Verlag, Berlin, 1986. MR 0903521
Reference: [7] Wright, S. J.: Primal-dual Interior-point Methods.SIAM, Philadelphia 1997. Zbl 0863.65031, MR 1422257
Reference: [8] Zhao, G.: Interior–point algorithms for linear complementarity problems based on large neighborhoods of the central path. SIAM J. Optim. 8 (1998), 397-�413. Zbl 0913.90254, MR 1618810, 10.1137/S1052623494275574
.

Files

Files Size Format View
Kybernetika_46-2010-4_9.pdf 210.4Kb application/pdf View/Open
Back to standard record
Partner of
EuDML logo