A parallel interior-point algorithm for linear programming on a shared memory machine

The XPRESS interior point optimizer is an “industrial strength” code for solution of large-scale sparse linear programs. The purpose of the present paper is to discuss how the XPRESS interior point optimizer has been parallelized for a Silicon Graphics multi processor computer. The ma jor computational task, performed in each iteration of the interior-point method implemented in the XPRESS interior point optimizer is the solution of a symmetric and positive definite system of linear equations. Therefore, parallelization of the Cholesky decomposition and the triangular solve procedure are discussed in detail. Finally, computational results are presented to demonstrate the parallel efficiency of the optimizer. It should be emphasized that the methods discussed can be applied to the solution of large-scale sparse linear least squares problems

[1]  L. D. J. C. Loyens,et al.  A parallel interior point algorithm for linear programming on a network of transputers , 1993, Ann. Oper. Res..

[2]  Bruce Hendrickson,et al.  Sparse Matrix Ordering Methods for Interior Point Linear Programming , 1998, INFORMS J. Comput..

[3]  Roy E. Marsten,et al.  Feature Article - Interior Point Methods for Linear Programming: Computational State of the Art , 1994, INFORMS J. Comput..

[4]  Robert E. Bixby,et al.  Progress in Linear Programming , 1993 .

[5]  Barr E. Bauer Practical parallel programming , 1992 .

[6]  Michael T. Heath,et al.  Parallel Algorithms for Sparse Linear Systems , 1991, SIAM Rev..

[7]  Anoop Gupta,et al.  Efficient sparse matrix factorization on high performance workstations—exploiting the memory hierarchy , 1991, TOMS.

[8]  Stephen J. Wright Modified Cholesky Factorizations in Interior-Point Algorithms for Linear Programming , 1999, SIAM J. Optim..

[9]  Alan George,et al.  The Evolution of the Minimum Degree Ordering Algorithm , 1989, SIAM Rev..

[10]  Vipin Kumar,et al.  A parallel formulation of interior point algorithms , 1994, Proceedings of Supercomputing '94.

[11]  Gautam Mitra,et al.  The interior point method for LP on parallel computers , 1992 .

[12]  George Karypis,et al.  A High Performance Sparse Cholesky Factorization Algorithm , 1994 .

[13]  Elizabeth R. Jessup,et al.  Parallel Factorization of Structured Matrices Arising in Stochastic Programming , 1994, SIAM J. Optim..

[14]  GuptaAnoop,et al.  Efficient sparse matrix factorization on high performance workstationsexploiting the memory hierarchy , 1991 .

[15]  Roy E. Marsten,et al.  Numerical Factorization Methods for Interior Point Algorithms , 1994, INFORMS J. Comput..

[16]  Barry W. Peyton,et al.  A Supernodal Cholesky Factorization Algorithm for Shared-Memory Multiprocessors , 1991, SIAM J. Sci. Comput..

[17]  Guangye Li,et al.  An implementation of a parallel primal-dual interior point method for block-structured linear programs , 1992, Comput. Optim. Appl..

[18]  Stephen J. Wright,et al.  pPCx: Parallel Software for Linear Programming , 1997, PPSC.

[19]  Stavros A. Zenios,et al.  A Scalable Parallel Interior Point Algorithm for Stochastic Linear Programming and Robust Optimization , 1997, Comput. Optim. Appl..

[20]  Knud D. Andersen A modified Schur-complement method for handling dense columns in interior-point methods for linear programming , 1996, TOMS.

[21]  Roy E. Marsten,et al.  On Implementing Mehrotra's Predictor-Corrector Interior-Point Method for Linear Programming , 1992, SIAM J. Optim..

[22]  Patrick R. Amestoy,et al.  An Approximate Minimum Degree Ordering Algorithm , 1996, SIAM J. Matrix Anal. Appl..

[23]  Irvin J. Lustig,et al.  Gigaflops in linear programming , 1996, Oper. Res. Lett..

[24]  Jacek Gondzio,et al.  Implementation of Interior Point Methods for Large Scale Linear Programming , 1996 .

[25]  Knud D. Andersen,et al.  The APOS linear programming solver: an implementation of the homogeneous algorithm , 1997 .

[26]  Barry W. Peyton,et al.  Progress in Sparse Matrix Methods for Large Linear Systems On Vector Supercomputers , 1987 .