dantzig until the factorization becomes denser. This is still a work in progress. <Possible options for primalPivot are: auto(matic) exa(ct) dant(zig) part(ial) steep(est) change sprint; current auto(matic)> printi(ngOptions) : Print options This changes the amount and format of printing a solution: normal - nonzero column variables integer - nonzero integer column variables special - in format suitable for OsiRowCutDebugger rows - nonzero column variables and row activities all - all column variables and row activities. For non-integer problems 'integer' and 'special' act like 'normal'. Also see printMask for controlling output. <Possible options for printingOptions are: normal integer special rows all csv bound(ranging) rhs(ranging) objective(ranging) stats boundsint boundsall; current normal> scal(ing) : Whether to scale problem Scaling can help in solving problems which might otherwise fail because of lack of accuracy. It can also reduce the number of iterations. It is not applied if the of elements is small. When unscaled it is possible that there may be small primal and/or infeasibilities. <Possible options for scaling are: off equi(librium) geo(metric) auto(matic) dynamic rows(only); current auto(matic)> : Whether factorization treated as sparse <Possible options for sparseFactor are: on off; current on> timeM(ode) : Whether to use CPU or elapsed time cpu uses CPU time for stopping, while elapsed uses elapsed time. (On Windows, elapsed time is always used).
dantzig until the factorization becomes denser. This is still a work in progress. <Possible options for primalPivot are: auto(matic) exa(ct) dant(zig) part(ial) steep(est) change sprint; current auto(matic)> printi(ngOptions) : Print options This changes the amount and format of printing a solution: normal - nonzero column variables integer - nonzero integer column variables special - in format suitable for OsiRowCutDebugger rows - nonzero column variables and row activities all - all column variables and row activities. For non-integer problems 'integer' and 'special' act like 'normal'. Also see printMask for controlling output. <Possible options for printingOptions are: normal integer special rows all csv bound(ranging) rhs(ranging) objective(ranging) stats boundsint boundsall; current normal> scal(ing) : Whether to scale problem Scaling can help in solving problems which might otherwise fail because of lack of accuracy. It can also reduce the number of iterations. It is not applied if the of elements is small. When unscaled it is possible that there may be small primal and/or infeasibilities. <Possible options for scaling are: off equi(librium) geo(metric) auto(matic) dynamic rows(only); current auto(matic)> : Whether factorization treated as sparse <Possible options for sparseFactor are: on off; current on> timeM(ode) : Whether to use CPU or elapsed time cpu uses CPU time for stopping, while elapsed uses elapsed time. (On Windows, elapsed time is always used).
CLP-1-16-9-standalone-help-text.docx