Integration of a primal simplex network algorithm with a large-scale mathematical programming system
ACM Transactions on Mathematical Software (TOMS)
PERUSE: An Interactive System for Mathematical Programs
ACM Transactions on Mathematical Software (TOMS)
The Design of the XMP Linear Programming Library
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
A Status Report on Computing Algorithms for Mathematical Programming
ACM Computing Surveys (CSUR)
Modeling languages versus matrix generators for linear programming
ACM Transactions on Mathematical Software (TOMS)
Representing super-sparse matrices with perturbed values
Communications of the ACM
The apex systems past and future
ACM SIGMAP Bulletin
Recent and future developments in SPERRY UNIVAC's FMPS product
ACM SIGMAP Bulletin
Data structures in honeywell's MPS/66
ACM SIGMAP Bulletin
Matrix storage schemes in linear programming
ACM SIGMAP Bulletin
Some tactics for 0-1 programming
ACM SIGMAP Bulletin
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
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Unconventional methods for matricial compression indicate that large linear programming constraint matrices may comfortably remain core-resident during optimization. Minor changes in the computational aspects of the simplex algorithm coupled with efficient inverse matrix representation show that the major portion of the inverse in product form of a basis may be embedded in the constraint matrix. A method for generating a sparse inverse matrix is presented.