Good Morning We have an application that requires that we run hundreds of thousands (or even several million) slightly revised sparse LP problems per run. These problems are not huge but are not trivial with constraint matrices with up to 10,000+ constraints and variables. At each iteration we are changing from 1 to 0.01 percent of the non-zero coefficients.
We have written a Fortran interface to the compiled glpk code and we have found that we can substantially decrease the total computation time by having the Fortran interface (1) loop through the LP problems, (2) pass the revised matrix coefficients directly into glpk, (3) pull the revised solutions and objective back into Fortran, and (4) having Fortran pass the results back to our R session upon completion of the LP runs. Our current Fortran code passes the revised LP problem's entire set of non-zero constraint coefficients back into glpk at each iteration. Is there a way (or would the glpk maintainers consider adding a way) that we could pass only the revised coefficients rather than having to pass the entire set of nonzero coefficients into glpk? lpSolveAPI has the ability to pass only the revised coefficients into the problem, and as a result, using lpSolveAPI (directly from R) runs substantially faster than running glpkAPI (directly from R). We have written a Fortran interface for both lpSolve and glpk and find that the Fortran-glpk interface improves performance substantially relative to using lpSolve. With lpSolve, passing only the changed coefficients gives a performance boost relative to passing the complete set of nonzero coefficients at each iteration. We are wanting to determine whether we could similarly decrease computation time for glpk by passing only the revised coefficients. Joe Atwood
_______________________________________________ Help-glpk mailing list [email protected] https://lists.gnu.org/mailman/listinfo/help-glpk
