Option | Description | Default |

bool:acceptcycling | should cycling solutions be accepted during iterative refinement? | `0` |

bool:computedegen | should the degeneracy be computed for each basis? | `0` |

bool:decompositiondualsimplex | should the decomposition based dual simplex be used to solve the LP? | `0` |

bool:explicitviol | Should violations of the original problem be explicitly computed in the decomposition simplex? | `0` |

bool:fullperturbation | should perturbation be applied to the entire problem? | `0` |

bool:lifting | should lifting be used to reduce range of nonzero matrix coefficients? | `0` |

bool:persistentscaling | should persistent scaling be used? | `1` |

bool:powerscaling | round scaling factors for iterative refinement to powers of two? | `1` |

bool:ratfacjump | continue iterative refinement with exact basic solution if not optimal? | `0` |

bool:rowboundflips | use bound flipping also for row representation? | `0` |

bool:testdualinf | should dual infeasibility be tested in order to try to return a dual solution even if primal infeasible? | `0` |

bool:usecompdual | should the dual of the complementary problem be used in the decomposition simplex? | `0` |

int:algorithm | type of algorithm (0 - primal, 1 - dual)
Range: [`0` , `1` ] | `1` |

int:decomp_displayfreq | the frequency that the decomposition based simplex status output is displayed.
Range: [`1` , ∞] | `50` |

int:decomp_iterlimit | the number of iterations before the decomposition simplex initialisation solve is terminated
Range: [`1` , ∞] | `100` |

int:decomp_maxaddedrows | maximum number of rows that are added to the reduced problem when using the decomposition based simplex
Range: [`1` , ∞] | `500` |

int:decomp_verbosity | the verbosity of decomposition based simplex (0 - error, 1 - warning, 2 - debug, 3 - normal, 4 - high, 5 - full).
Range: [`1` , `5` ] | `0` |

int:displayfreq | display frequency
Range: [`1` , ∞] | `200` |

int:factor_update_max | maximum number of LU updates without fresh factorization (0 - auto) | `0` |

int:factor_update_type | type of LU update (0 - eta update, 1 - Forrest-Tomlin update)
Range: [`0` , `1` ] | `1` |

int:hyperpricing | mode for hyper sparse pricing (0 - off, 1 - auto, 2 - always)
Range: [`0` , `2` ] | `1` |

int:iterlimit | iteration limit (-1 - no limit)
Range: [`-1` , ∞] | `GAMS iterlim` |

int:leastsq_maxrounds | maximum number of conjugate gradient iterations in least square scaling | `50` |

int:pricer | pricing method (0 - auto, 1 - dantzig, 2 - parmult, 3 - devex, 4 - quicksteep, 5 - steep)
Range: [`0` , `5` ] | `0` |

int:printcondition | print condition number during the solve (0 - off, 1 - ratio estimate , 2 - sum estimate, 3 - product estimate, 4 - exact)
Range: [`0` , `4` ] | `0` |

int:ratiotester | method for ratio test (0 - textbook, 1 - harris, 2 - fast, 3 - boundflipping)
Range: [`0` , `3` ] | `3` |

int:reflimit | refinement limit (-1 - no limit)
Range: [`-1` , ∞] | `-1` |

int:representation | type of computational form (0 - auto, 1 - column representation, 2 - row representation)
Range: [`0` , `2` ] | `0` |

int:scaler | scaling (0 - off, 1 - uni-equilibrium, 2 - bi-equilibrium, 3 - geometric, 4 - iterated geometric, 5 - least squares, 6 - geometric-equilibrium)
Range: [`0` , `6` ] | `2` |

int:simplifier | simplifier (0 - off, 1 - auto)
Range: [`0` , `1` ] | `1` |

int:stallreflimit | stalling refinement limit (-1 - no limit)
Range: [`-1` , ∞] | `-1` |

int:starter | crash basis generated when starting from scratch (0 - none, 1 - weight, 2 - sum, 3 - vector)
Range: [`0` , `3` ] | `0` |

int:timer | type of timer (1 - cputime, aka. usertime, 2 - wallclock time, 0 - no timing)
Range: [`0` , `2` ] | `2` |

int:verbosity | verbosity level (0 - error, 1 - warning, 2 - debug, 3 - normal, 4 - high, 5 - full)
Range: [`0` , `5` ] | `3` |

real:epsilon_factorization | zero tolerance used in factorization
Range: [`0` , `1` ] | `1e-20` |

real:epsilon_pivot | pivot zero tolerance used in factorization
Range: [`0` , `1` ] | `1e-10` |

real:epsilon_update | zero tolerance used in update of the factorization
Range: [`0` , `1` ] | `1e-16` |

real:epsilon_zero | general zero tolerance
Range: [`0` , `1` ] | `1e-16` |

real:feastol | primal feasibility tolerance
Range: [`0` , `1` ] | `1e-06` |

real:fpfeastol | working tolerance for feasibility in floating-point solver during iterative refinement
Range: [`1e-12` , `1` ] | `1e-09` |

real:fpopttol | working tolerance for optimality in floating-point solver during iterative refinement
Range: [`1e-12` , `1` ] | `1e-09` |

real:infty | infinity threshold
Range: [`1e+10` , ∞] | `maxdouble` |

real:leastsq_acrcy | accuracy of conjugate gradient method in least squares scaling (higher value leads to more iterations)
Range: [`1` , ∞] | `1000` |

real:liftmaxval | lower threshold in lifting (nonzero matrix coefficients with smaller absolute value will be reformulated)
Range: [`10` , ∞] | `1024` |

real:liftminval | lower threshold in lifting (nonzero matrix coefficients with smaller absolute value will be reformulated)
Range: [`0` , `0.1` ] | `0.000976562` |

real:maxscaleincr | maximum increase of scaling factors between refinements
Range: [`1` , ∞] | `1e+25` |

real:minred | minimal reduction (sum of removed rows/cols) to continue simplification
Range: [`0` , `1` ] | `0.0001` |

real:objlimit_lower | lower limit on objective value
Range: [-∞, ∞] | `mindouble` |

real:objlimit_upper | upper limit on objective value
Range: [-∞, ∞] | `maxdouble` |

real:opttol | dual feasibility tolerance
Range: [`0` , `1` ] | `1e-06` |

real:refac_basis_nnz | refactor threshold for nonzeros in last factorized basis matrix compared to updated basis matrix
Range: [`1` , `100` ] | `10` |

real:refac_mem_factor | refactor threshold for memory growth in factorization since last refactorization
Range: [`1` , `10` ] | `1.5` |

real:refac_update_fill | refactor threshold for fill-in in current factor update compared to fill-in in last factorization
Range: [`1` , `100` ] | `5` |

real:representation_switch | threshold on number of rows vs. number of columns for switching from column to row representations in auto mode | `1.2` |

real:sparsity_threshold | sparse pricing threshold (violations < dimension * SPARSITY_THRESHOLD activates sparse pricing)
Range: [`0` , `1` ] | `0.6` |

real:timelimit | time limit in seconds | `GAMS reslim` |