ampl: option solver xpress; # change the solver ampl: option xpress_options 'option1=value1 option2=value2'; # specify options ampl: solve; # solve the problem
Solver options obtained with
$ xpress -=.
XPRESS Optimizer Options for AMPL -------------------------------------------- To set these options, assign a string specifying their values to the AMPL option "xpress_options". For example: ampl: option xpress_options 'mipgap=1e-6'; Options: acc:abs Solver acceptance level for 'AbsConstraint', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:and (acc:forall) Solver acceptance level for 'AndConstraint', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:indeq (acc:indlineq) Solver acceptance level for 'IndicatorConstraintLinEQ', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:indge (acc:indlinge) Solver acceptance level for 'IndicatorConstraintLinGE', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:indle (acc:indlinle) Solver acceptance level for 'IndicatorConstraintLinLE', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:max Solver acceptance level for 'MaxConstraint', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:min Solver acceptance level for 'MinConstraint', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:or (acc:exists) Solver acceptance level for 'OrConstraint', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:quadeq Solver acceptance level for 'QuadConEQ', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:quadge Solver acceptance level for 'QuadConGE', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:quadle Solver acceptance level for 'QuadConLE', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred acc:sos2 Solver acceptance level for 'SOS2Constraint', default 2: 0 - Not accepted natively, automatic redefinition will be attempted 1 - Accepted but automatic redefinition will be used where possible 2 - Accepted natively and preferred alg:addcutoff (addcutoff, mipaddcutoff) Amount to add to the objective function of the best integer solution found to give the new MIP cutoff; default -1e-5. alg:basis (basis) Whether to use or return a basis: 0 - No 1 - Use incoming basis (if provided) 2 - Return final basis 3 - Both (1 + 2 = default) alg:clamping (clamping) Control adjustements of the returned solution values such that they are always within bounds: -1 - Determined automatically 0 - Adjust primal solution to always be within primal bounds (default) 1 - Adjust primal slack values to always be within constraint bounds 2 - Adjust dual solution to always be within the dual bounds implied by the slacks 3 - Adjust reduced costs to always be within dual bounds implied by the primal solution alg:cutoff (cutoff) If the optimal objective value is worse than cutoff, report "objective cutoff" and do not return a solution. Default: 1.0E+40 for minimizing, -1.0E+40 for maximizing. alg:feastol (feastol) Primal feasibility tolerance (default 1e-6). alg:feastolperturb (feastolperturb) How much a feasible primal basic solution is allowed to be perturbed when performing basis changes. The tolerance specified by "alg:feastol" is always considered as an upper limit for the perturbations; default = 1.0E-06 alg:feastoltarget (feastoltarget) Specifies the target feasibility tolerance for the solution refiner. Default = 0 (use the value of "alg:feastol") alg:iisfind (iisfind, iis) Whether to find and export an IIS. Default = 0 (don't export). alg:indlinbigm (indlinbigm) Largest "big M" value to use in converting indicator constraints to regular constraints, default = 1e5 alg:lpfolding (lpfolding) Simplex and barrier: whether to fold an LP problem before solving it: .. value-table: alg:maxiis (maxiis) Maximum number of IIS to find; default=-1 (no limit) alg:method (method, lpmethod, defaultalg) Which algorithm to use for non-MIP problems or for the root node of MIP problems: 1 - Automatic choice (default) 2 - Dual simplex 3 - Primal simplex 4 - Netwon Barrier alg:randomseed (randomseed) Sets the initial seed to use for the pseudo-random number generator in the Optimizer; default=1 alg:refactor (refactor) Whether the optimization should restart using the current representation of the factorization in memory: -1 - Automatic choice (default) 0 - No 1 - Yes. alg:refineops (refineops) Bit vector: specifies wmhen the solution refiner should be executed to reduce solution infeasibilities. The refiner will attempt to satisfy the target tolerances for all original linear constraints before presolve or scaling has been applied: 1 - refine optimal LP solutions 2 - refine MIP solutions 8 - refine each node of the search tree 16 - refine non-global solutions 32 - apply the iterative refiner to refine the solution 64 - use higher precision in the iterative refinement 128 - iterative refiner will use the primal simplex algorithm 256 - iterative refiner will use the dual simplex algorithm 512 - refine MIP solutions such that rounding them keeps the problem feasible when reoptimized 1024 - ttempt to refine MIP solutions such that rounding them keeps the problem feasible when reoptimized, but accept integers solutions even if refinement fails alg:relax (relax) 0*/1: Whether to relax integrality of variables. alg:relcutoff (relcutoff, miprelcutoff) If the optimal objective value is (relatively) worse than relcutoff, report "objective cutoff" and do not return a solution. Default: 1.0E-4. alg:resourcestrategy (resourcestrategy) Wether to allow nondeterministic decisions to cope with low memory (affected by maxmemory and maxmemoryhard): 0 - No (default) 1 - Yes. alg:start (warmstart) Whether to use incoming primal (and dual, for LP) variable values in a warmstart: 0 - No 1 - Yes (for LP: if there is no incoming alg:basis) (default) 2 - Yes (for LP: ignoring the incoming alg:basis, if any.) alg:zerotol (matrixtol) The zero tolerance on matrix elements. If the value of a matrix element is less than or equal to this in absolute value, it is treated as zero, default=1e-9. bar:alg (baralg) Which barrier algorithm to use bar:cachesize (cachesize) Newton Barrier: L2 or L3 (see notes) cache size in kB (kilobytes) of the CPU (default -1). On Intel (or compatible) platforms a value of -1 may be used to determine the cache size automatically. bar:choleskyalg (choleskyalg) Type of Cholesky factorization used for barrier, sum of: : bar:choleskytol (choleskytol) Zero tolerance for Cholesky pivots in the Newton Barrier algorithm; default = 1e-15 bar:cores (barcores) If positive, number of CPU cores to assume present when using the barrier algorithm. Default = -1, which means automatic choice bar:corespercpu (corespercpu) Newton Barrier: number of cores to assume per cpu. Barrier cache = cachesize/corespercpu. Default -1 = automatic. bar:cpuplatform (cpuplatform) Which instruction are allowed to the Newton barrier method: : bar:crash (barcrash) Choice of crash procedure for crossover, higher number means more aggressive procedure: bar:crossover (crossover) How to transform a barrier solution to a basic one: -1 - automatic choice (default) 0 - none: return an interior solution 1 - primal crossover first 2 - dual crossover first bar:crossoverops (crossoverops) Bit vector affecting crossover after the barrier algorithm; sum of: 1 - return the barrier solution (rather than the last intermediate solution) when crossover stop early 2 - skip the second crossover stage 4 - skip pivots that are "less numerically reliable" 8 - do a slower but more numerically stable crossover bar:crossoverthreads (crossoverthreads) Limit on threads used during crossover; default -1 (determined by bar:threads). bar:crossovertol (crossovertol, crossoveraccuracytol) Tolerance (default 1e-6) for deciding whether to adjust the relative pivot tolerance during crossover when a new basis factorization is necessary. Errors in the recalculated basic solution above this tolerance cause the pivot tolerance to be adjusted. bar:densecollimit (densecollimit) Number of nonzeros above which a column is treated as dense in the barrier algorithm's Cholesky factorization. Default=0 (automatic). bar:dualstop (bardualstop) Barrier method convergence tolerance on dual infeasibilities; default = 0 (automatic choice) bar:gap (bargaptarget) Barrier algorithm target tolerance for the relative duality gap.If not satisfied and no further progress is possible but barstopgap is satisfied, then the current solution is considered optimal. bar:gapstop (bargapstop) Barrier method convergence tolerance on the relative duality gap; default = 0 bar:indeflimit (barindeflimit) Maximum indefinite factorizations to allow in the barrier algorithm for solving a QP: stop when the limit is hit default = 15 bar:kernel (barkernel) How the barrier algorithm weights centrality: >= +1.0 - More emphasis on centrality (default 1.0) <= -1.0 - Each iteration, adaptively select a value from [+1, -barkernel] bar:l1cache (l1cache) Newton barrier: L1 cache size in kB (kilo bytes) of the CPU. On Intel (or compatible) platforms a value of -1 may be used to determine the cache size automatically. bar:objperturb (barobjperturb) Defines how the barrier perturbs the objective (default 1e-6); values > 0 let the optimizer decide if to perturb the objective, values < 0 force the perturbation: n > 0 - automatic decison, scale n n = 0 - turn off perturbation n < 0 - force perturbation by abs(n) bar:objscale (barobjscale) How the barrier algorithm scales the objective; when the objective is quadratic, the quadratic diagonal is used in determining the scale: -1 - Automatic choice (default) 0 - Scale by the geometric mean of the objective coefficients > 0 - Scale so the argest objective coefficient in absolute value is <= barobjscale. bar:order (barorder) Cholesky factorization pivot order for barrier algorithm: 0 - automatic choice(default) 1 - minimum degree 2 - minimum local fill 3 - nested dissection bar:orderthreads (barorderthreads) Number of threads to use when choosing a pivot order for Cholesky factorization; default 0 (automatic choice). bar:output (baroutput) Amount of output for the barrier method: 0 - no output 1 - each iteration (default) bar:presolve (barpresolve) Level of barrier-specific presolve effort: 0 - no output 1 - each iteration (default) bar:primalstop (barprimalstop) Barrier method convergence tolerance on primal infeasibilities; default = 0 (automatic choice) bar:refiter (barrefiter) Maximum number of refinement iterations, helpful when the the solution is near to the optimum using barrier or crossover: 0 - default n > 0 - perform n refinement iterations bar:regularize (barreg, barrregularize) Regularization to use with "barrier. Default=-1 (automatic choice), else sum of: 1 - use "standard" regularization 2 - use "reduced" regularization: less perturbation than "standard" regularization 4 - keep dependent rows in the KKT system 8 - keep degenerate rows in the KKT system bar:start (barstart) Choice of starting point for barrier method: -1 - Use incoming solution for warm start 0 - Automatic choice (default) 1 - Heuristics based on magnitudes of matrix entries 2 - Use pseudoinverse of constraint matrix 3 - Unit starting point for homogeneous self - dual barrier algorithm. bar:stepstop (barstepstop) Barrier method convergence tolerance: stop when step size <= barstepstop; default = 1e-10 bar:threads (threads) number of threads used in the Newton Barrier algorithm; default = -1 (determined by "threads") cut:cover (covercuts) The number of rounds of lifted cover inequalities at the top node.Default=-1, automatic. cut:depth (cutdepth) Maximum MIP tree depth at which to generate cuts. Default -1 (automatic); a value of 0 will disable cuts generation. cut:factor (cutfactor) Limit on number of cuts and cut coefficients added while solving MIPs. Default=-1 (automatic); a value of 0 will disable cuts generation. cut:freq (cutfreq) Cuts are only generated at tree depths that are integer multiples of cutfreq. Default=-1 (automatic choice). cut:gomory (gomcuts) The number of rounds of Gomory or lift-and-project cuts at the top node.Default=-1, automatic. cut:lnpbest (lnpbest) Number of infeasible global entities to create lift-and-project cuts for during each round of Gomory cuts at the top node cut:lnpiterlimit (lnpiterlimit) Number of iterations to perform in improving each lift-and-project cut; default=-1 (automatic) cut:qccuts (qccuts) when using miqcpalg=1 to solve a mixed-integer problem that has quadratic constraints or second-order cone constraints, the number of rounds of outer approximation cuts at the top node; default = -1 (automatic choice). cut:select (cutselect) Detailed control of cuts at MIP root node; sum of: 32 - clique cuts 64 - mixed - integer founding(MIR) cuts 128 - lifted cover cuts 2048 - flow path cuts 4096 - implication cuts 8192 - automatic lift - and -project strategy 16384 - disable cutting from cut rows 32768 - lifted GUB cover cuts 65536 - zero - half cuts 131072 - indicator - constraint cuts -1 - all available cuts(default) cut:strategy (cutstrategy) How aggressively to generate MIP cuts; more ==> fewer nodes but more time per node: -1 - automatic (default) 0 - no cuts 1 - conservative strategy 2 - moderate strategy 3 - aggressive strategy cut:treecover (treecovercuts) The number of rounds of lifted cover inequalities at MIP nodes other than the top node. Default=-1 (automatic). cut:treegomory (treegomcuts) The number of rounds of Gomory or lift-and-project cuts at MIP nodes other than the top node. Default=-1 (automatic). cut:treeqccuts (treeqccuts) when using miqcpalg=1 to solve a MIP that has quadratic constraints or second-order cone constraints, the number of rounds of outer approximation cuts during the tree search; default = -1 (automatic choice). cut:treeselect (treecutselect) Detailed control of cuts created during the tree search; sum of: 32 - clique cuts 64 - mixed - integer founding(MIR) cuts 128 - lifted cover cuts 2048 - flow path cuts 4096 - implication cuts 8192 - automatic lift - and -project strategy 16384 - disable cutting from cut rows 32768 - lifted GUB cover cuts 65536 - zero - half cuts 131072 - indicator - constraint cuts -1 - all available cuts(default) cvt:bigM (cvt:bigm, cvt:mip:bigM, cvt:mip:bigm) Default value of big-M for linearization of logical constraints. Not used by default. Use with care (prefer tight bounds). Should be smaller than (1.0 / [integrality tolerance]) cvt:mip:eps (cvt:cmp:eps) Tolerance for strict comparison of continuous variables for MIP. Ensure larger than the solver's feasibility tolerance. cvt:plapprox:domain (plapprox:domain, plapproxdomain) For piecewise-linear approximated functions, both arguments and result are bounded to +-[pladomain]. Default 1e6. cvt:plapprox:reltol (plapprox:reltol, plapproxreltol) Relative tolerance for piecewise-linear approximation. Default 0.01. cvt:pre:all 0/1*: Set to 0 to disable most presolve in the flat converter. cvt:pre:eqbinary 0/1*: Preprocess reified equality comparison with a binary variable. cvt:pre:eqresult 0/1*: Preprocess reified equality comparison's boolean result bounds. cvt:quadcon (passquadcon) 0/1*: Multiply out and pass quadratic constraint terms to the solver, vs. linear approximation. cvt:quadobj (passquadobj) 0/1*: Multiply out and pass quadratic objective terms to the solver, vs. linear approximation. cvt:sos (sos) 0/1*: Whether to honor declared suffixes .sosno and .ref describing SOS sets. Each distinct nonzero .sosno value designates an SOS set, of type 1 for positive .sosno values and of type 2 for negative values. The .ref suffix contains corresponding reference values used to order the variables. cvt:sos2 (sos2) 0/1*: Whether to honor SOS2 constraints for nonconvex piecewise-linear terms, using suffixes .sos and .sosref provided by AMPL. lim:bariterlim (bar:iterlim, bariterlim) Limit on the number of barrier iterations (default 500). lim:crossoveriterlim (bar:crossoveriterlim, crossoveriterlim, crossoveritlim) Limit on crossover iterations after the barrier algorithm; default = 2147483645 lim:heurdiveiterlimit (heurdepth, mip:heurdiveiterlimit) Simplex iteration limit for reoptimizing during the diving heuristic; default = -1 (automatic selection); a value of 0 implies no iteration limit lim:lpiterlimit (lpiterlimit) The maximum number of iterations that will be performed by primal simplex or dual simplex before the optimization process terminates. For MIP problems, this is the maximum total number of iterations over all nodes. lim:lprefineiterlimit (lprefineiterlimit) This specifies the simplex iteration limit the solution refiner can spend in attempting to increase the accuracy of an LP solution; default=-1 (automatic). lim:maxcuttime (maxcuttime) The maximum amount of time allowed for generation of cutting planes and reoptimization;default=0 (no time limit) lim:maxmipsol (maxmipsol) Limit on the number of MIP solutions to be found (default no limit). lim:maxstalltime (maxstalltime) Maximum time in seconds that the MIP Optimizer will continue to search for improving solution after finding a new incumbent, default=0 (no limit) lim:mem (memlimit, maxmemoryhard) Hard limit (integer number of MB) on memory allocated, causing early termination if exceeded; default = 0 (no limit) lim:nodes (nodelim, nodelimit, maxnode) Maximum MIP nodes to explore (default: 2147483647). lim:softmem (softmemlimit, maxmemorysoft) Soft limit (integer number of MB) on memory allocated; default = 0 (no limit) lim:soltime (soltimelim, soltimelimit) Limit on solve time (in seconds; default: no limit) to be applied only after a solution has been found. lim:time (timelim, timelimit) Limit on solve time (in seconds; default: no limit). lp:bigm (bigm, bigmpenalty) Infeasibility penalty to be used if "BigM" method is used; default = 1024 lp:bigmmethod (bigmmethod) Simplex: This specifies whether to use the "Big M" method, or the standard phase I (achieving feasibility) and phase II (achieving optimality). the "Big M" method, the objective coefficients of the variables are considered during the feasibility phase, possibly leading to an initial feasible basis which is closer to optimal. The side-effects involve possible round-off errors. 0 - phase I / II 1 - bigM method (default) lp:crash (crash) Simplex: This determines the type of crash used when the algorithm begins.For primal simplex, the choices are listed below; for dual simplex the choices follow and are interpreted a bit-vector: 0 - none 1 - one-pass search for singletons 2 - multi-pass search for singletons 3 - multi-pass search including slacks 4 - at most 10 passes, only considering slacks at the end n>10 - like 4, but at most n-10 passes 0 (dual) - perform standard crash. 1 (dual) - perform additional numerical checks during crash 2 (dual) - extend the set of column candidates for crash 3 (dual) - extend the set of row candidates for crash 4 (dual) - force crash lp:dualforceparallel (forceparalleldual, dualforceparallel) Specifies whether the dual simplex solver should always use the parallel simplex algorithm lp:dualgradient (dualgradient) dual simplex pricing strategy: -1 - automatic (default) 0 - devex 1 - steepest edge 2 - direct steepest edge 3 - sparse devex lp:dualize (dualize) Whether to convert the primal problem to its dual and solve the converted problem: -1 - automatic (default) 0 - solve the primal problem 1 - solve the dual problem lp:dualizeops (dualizeops) When solving the dual problem after deriving it from the primal, whether to use primal simplex if dual simplex was specified and vice versa: 0 - No 1 - Yes (default) lp:dualperturb (dualperturb) Factor by which the problem will be perturbed prior to optimization by dual simplex. Default -1 (automatic); note that a value of 0 implies no perturbation lp:dualstrategy (dualstrategy) Bit vector controlling the dual simplex strategy (default 1): 1 - switch to primal when dual infeasible 2 - stop the solve instead of switching to primal 4 - use aggressive cut-off in MIP search 8 - use dual simplex to remove cost perturbations 16 - aggressive dual pivoting 32 - keep using dual simplex even when numerically unstable lp:dualthreads (dualthreads) Limit on threads used by parallel dual simplex; default -1 (determined by tech:threads). lp:etatol (etatol) Zero tolerance on eta elements, elements of eta vectors whose absolute value is smaller than etatol are taken to be zero. lp:invertfreq (invertfreq) Maximum simplex iterations before refactoring the basis; default -1 (automatic) lp:invertmin (invertmin) Minimum simplex iterations before refactoring the basis; default = 3 lp:keepbasis (keepbasis) Basis choice for the next LP iteration: 0 - ignore previous basis 1 - use previous basis (default) 2 - use previous basis only if the number of basic variables == number of constraints lp:keepnrows (keepnrows) Status for nonbinding rows: -1 - delete N type rows from the matrix (default) 0 - delete elements from N type rows leaving empty N type rows in the matrix 1 - keep N type rows lp:log (lplog) Frequency of printing simplex iteration log; default = 100.Values n < 0 display detailed outputs every -n iterations. lp:netstalllimit (netstalllimit) Limit the number of degenerate pivots of the network simplex algorithmm before switching to primal or dual: -1 - automatic (default) 0 - no limit n > 0 - limit to n network simplex iterations lp:optimalitytol (optimalitytol) This is the zero tolerance for reduced costs. On each iteration, the simplex method searches for a variable to enter the basis which has a negative reduced cost. The candidates are only those variables which have reduced costs less than the negative value of optimalitytol; default=1e-6 lp:optimalitytoltarget (optimalitytoltarget) Target optimality tolerance for the solution refiner; default=0 (use the value specified by lp:optimalitytol) lp:penalty (penalty) Minimum absolute penalty variable coefficient; default = automatic choice lp:pivtol (pivtol, markowitztol) Markowitz pivot tolerance (default = 0.01) lp:pricingalg (pricingalg) Primal simplex pricing method: -1 - partial pricing 0 - automatic choice (default) 1 - devex pricing 2 - steepest edge 3 - steepest edge with initial weights lp:primalunshift (primalunshift) Whether the primal alg. calls the dual to unshift: 0 - No 1 - Yes (default) lp:relpivottol (relpivottol) Relative pivot tolerance; default = 1e-6 lp:sifting (sifting) When using dual simplex, whether to enable sifting, which can speed up the solve when there are many more variables than constraints: -1 - Automatic choice (default) 0 - No 1 - Yes. lp:siftpasses (siftpasses) Determines how quickly we allow to grow the worker problems during the sifting algorithm; default 4. lp:siftpresolveops (siftpresolveops) Presolve operations for solving the subproblems during sifting: -1 - use the "presolveops" setting specified for the original problem >=0 - use the value (see "presolveops" for its semantic) lp:siftswitch (siftswitch) Determines which algorithm to use for solving the subproblems during sifting: -1 - dual simplex 0 - barrier >0 - use the barrier algorithm while the number of dual infeasibilities is larger than this value, otherwise use dual simplex mip:basis (fixmodel, mip:fix) Whether to compute duals / basis / sensitivity for MIP models: 0 - No (default) 1 - Yes. mip:bestbound (bestbound, return_bound) Whether to return suffix .bestbound for the best known MIP dual bound on the objective value: 0 - No (default) 1 - Yes. The suffix is on the objective and problem and is -Infinity for minimization problems and +Infinity for maximization problems if there are no integer variables or if a dual bound is not available. mip:branchchoice (branchchoice) Control the choice of branching when solving a MIP problem: 0 - explore branch with min.estimate first(default) 1 - explore branch with max.estimate first 2 - if an incumbent solution exists, first explore the branch satisfied by the incumbent; otherwise use choice 0 (min.est.first). 3 - (default) explore the first branch that moves the branching variable away from its value at the root node; if the branching entity is not a simple variable, assume branchchoice=0. mip:branchdisj (branchdisj) Whether to branch on general split disjunctions while solving MIPs: -1 - automatic choice (default) 0 - disabled 1 - cautious strategy : create branches only for general integers with a wide range 2 - moderate strategy 3 - aggressive strategy : create disjunctive branches for both binaryand integer variables mip:branchstructural (branchstructural, branchstruct) Whether to search for special structure during branch and bound: -1 - Automatic choice (default) 0 - No 1 - Yes. mip:breadthfirst (breadthfirst) Number of MIP nodes included in best-first search before switching to local-first search; default=11. mip:components (mipcomponents) Determines whether disconnected components in a MIP should be solved as separate MIPs: -1 - Automatic choice (default) 0 - No 1 - Yes. mip:concurrentnodes (mipconcurrentnodes) Node limit to choose the winning solve when concurrent solves are enabled: -1 - automatic (default) n > 0 - number of nodes to complete mip:concurrentsolves (mipconcurrentsolves) Select the number of concurrent solves to start for a MIP: -1 - enabled, the number of concurrent solves depends on mipthreads 0 - disabled (default) 1 - disabled (default) n > 1 - number of concurrent solves = n mip:deterministic (deterministic) Whether a MIP search should be deterministic: mip:dualreductions (mipdualreductions) Kinds of dual reductions allowed during branch and bound: 0 - none 1 - all (default) 2 - restrict dual reductions to continuous variables If poolnbest > 1 is specified, specifying mipdualreductions = 2 might be prudent. mip:feasibilityjump (feasibilityjump) Decides whether to run the Feasibility Jump heuristic at the top node during branch-and-bound: 0 - No 1 - Yes (default) mip:feasibilitypump (feasibilitypump) Decides whether to run the Feasibility Pump heuristic at the top node during branch-and-bound: -1 - automatic (default) 0 - turned off 1 - always run 2 - run if other heuristics have failed to find an integer solution mip:gap (mipgap) Max. relative MIP optimality gap (default 1e-4). mip:gapabs (mipgapabs) Max. absolute MIP optimality gap (default 0). mip:heurbeforelp (heurbeforelp) Whether primal heuristics should be run before the initial LP relaxation has been solved: -1 - Automatic choice (default) 0 - No 1 - Yes. mip:heurdiverandomize (hdive_rand, heurdiverandomize) The level of randomization to apply in the diving heuristic; values range from 0.0=none to 1.0=full. mip:heurdivesoftrounding (hdive_rounding, heurdivesoftrounding) Whether to use soft rounding in the MIP diving heuristic (to push variables to their bounds via the objective rather than fixing them): -1 - automatic (default) 0 - do not use soft rounding 1 - cautious use 2 - aggressing use mip:heurdivespeedup (hdive_speed, heurdivespeedup) Controls tradeoff between speed and solution quality in the diving heuristic: .. value-table: mip:heurdivestrategy (hdive_strategy, heurdivestrategy) Chooses the strategy for the diving heuristic: -1 - automatic selection (default) 0 - disable heuristics 1-18 - available pre-set strategies for rounding infeasible global entities mip:heuremphasis (heuremphasis) Chooses the strategy for the diving heuristic: -1 - default strategy (default) 0 - disable heuristics 1 - focus on reducing the gap early 2 - extremely aggressive heuristics mip:heurforcespecialobj (heurforcespecobj, heurforcespecialobj) Whether to use special objective heuristics on large problems and even if an incumbant exists: 0 - No (default) 1 - Yes. mip:heurfreq (heurfreq) During branch and bound, heuristics are applied at nodes whose depth from the root is zero modulo "heurfreq"; default -1 (automatic). mip:heursearcheffort (heursearcheffort) Adjusts the overall level of the local search heuristics; default 1.0 (normal level). mip:heursearchfreq (heurfreq, heursearchfreq) Specifies how often the local search heuristic should be run in the tree: -1 - automatic (default) 0 - disabled in the tree n>0 - number of nodes between each run mip:heursearchrootcutfreq (heurrootcutfreq, heursearchrootcutfreq) How often to run the local search heuristic while cutting at the root node: -1 - automatic (default) 0 - disabled during cutting n>0 - number cutting rounds between each run mip:heursearchrootselect (heursearchrootselect) A bit vector control for selecting which local search heuristics to apply on the root node of a global solve; default 117: 1 - local search with a large neighborhood. Potentially slow but is good for finding solutions that differs significantly from the incumbent 2 - local search with a small neighborhood centered around a node LP solution 4 - local search with a small neighborhood centered around an integer solution. This heuristic will often provide smaller, incremental improvements to an incumbent solution 8 - local search with a neighborhood set up through the combination of multiple integer solutions. 32 - local search without an objective function 64 - local search with an auxiliary objective function mip:heursearchtreeselect (heursearchtreeselect) A bit vector control for selecting which local search heuristics to apply during the tree search of a global solve, default 17: 1 - local search with a large neighborhood. Potentially slow but is good for finding solutions that differs significantly from the incumbent 2 - local search with a small neighborhood centered around a node LP solution 4 - local search with a small neighborhood centered around an integer solution. This heuristic will often provide smaller, incremental improvements to an incumbent solution 8 - local search with a neighborhood set up through the combination of multiple integer solutions. 32 - local search without an objective function 64 - local search with an auxiliary objective function mip:heurthreads (heurtreads) Number of threads to dedicate to running heuristics on the root node: mip:historycosts (historycosts) How to update the pseudo cost for a global entity when a strong branch or a regular branch is applied: mip:intfeastol (intfeastol) Feasibility tolerance for integer variables (default 5e-06). mip:kappafreq (mipkappafreq) During branch-and-bound, how often to compute basis condition numbers: 0 - never (default) 1 - every node n > 1 - once per node at level n of the branch-and-bound tree When mipkappafreq > 0, a final summary shows the number of sampled nodes that are: "stable": kappa < 10^7 "suspicious": 10^7 <= kappa < 10^10 "unstable": 10^10 <= kappa < 10^13 "ill-posed": 10^13 <= kappa. A "Kappa attention level" between 0 and 1 is also reported. Condition numbers use the Frobenius norms of the basis and its inverse. mip:localchoice (localchoice) when to backtrack between two child nodes during a "dive": 1 - never backtrack from the first child unless it is dropped (i.e., is infeasible or cut off) (default) 2 - always solve both child nodes before deciding which child to continue with 3 - automatically determined mip:log (miplog) Frequency of printing MIP iteration log; default = -100.Values n < 0 display detailed outputs every -n iterations. mip:maxlocalbacktrack (maxlocalbacktrack, maxlocalbt) Max height above current node to look for a local backtrack candidate node; default=-1(automatic) mip:maxtasks (maxmiptasks) Maximum tasks to run in parallel during a MIP solve; default = -1 (use mip:threads).For mip:maxtasks > 0, branch-and-bound nodes are solved in a deterministic way, but the barrier algorithm (if used) may cause a nondeterministic MIP solve unless bar:threads = 1. mip:miprefineiterlimit (miprefiterlim, miprefineiterlimit) Max. simplex iterations per reoptimization in MIP refiner when refineops is 2 or 3; default -1 (automatic). mip:nodeprobingeffort (nodeprobingeffort) Multiplier on the default amount of work node probing should do. Setting the control to zero disables node probing. mip:nodeselection (nodeselection) Determines which nodes will be considered for solution once the current node has been solved: 1 - local first: choose between descendant and sibling nodes if available; choose from all outstanding nodes otherwise 2 - best first: choose from all outstanding nodes 3 - local depth first: choose between descendant and sibling nodes if available; choose from the deepest nodes otherwise 4 - best first, then local first: best first is used for the first BREADTHFIRST nodes, after which local first is used 5 - pure depth first: choose from the deepest outstanding nodes mip:presolve (mippresolve) Type of integer processing to be performed. If set to 0, no processing will be performed (default automatic): 1 - reduced-cost fixing at each node 2 - primal reductions will be performed at each node 8 - allow changing continuous-variable bounds 16 - allow dual reductions 32 - allow global tightening of the problem 64 - use objective function 128 - allow restarting 256 - allow use of symmetry mip:pseudocost (pseudocost) Default pseudo-cost assumed for forcing an integer variable to an integer value; default = 0.01 mip:qcrootalg (qcrootalg) when using miqcpalg = 1 to solve a mixed - integer problem that has quadratic constraints or second - order cone constraints, the algorithm for solving the root node: -1 - automatic (default) 0 - use barrier 1 - use dual simplex on outer approximation mip:rampup (miprampup) Whether to limit the number of parallel tasks during the ramp-up phase of the parallel MIP algorithm: -1 - automatic choice (default) 0 - no: use as many tasks as possible 1 - yes, until finished with initial dives mip:relaxtreememorylimit (relaxtreemem, relaxtreememorylimit) Fraction of memory limit by which to relax "treememlimit" when too much structural data appears; default 0.1. Set to 0 to never relax the memory limit in this way. mip:restart (miprestart) Control strategy for in-tree restarts: -1 - automatic choice (default) 0 - disable in-tree restarts 1 - normal aggressiveness 2 - higher aggressiveness mip:restartfactor (miprestartfactor) Fine tune initial conditions to trigger an in-tree restart; values > 1 increase the aggressiveness, < 1 decrease it (default 1.0) mip:restartgapthreshold (miprestartgapthreshold) Initial gap threshold to delay in-tree restart; the restart is delayed if the relative gap is below the threshold (default 0.02) mip:return_gap (return_mipgap) Whether to return mipgap suffixes or include mipgap values (|objectve - .bestbound|) in the solve_message: sum of 1 - Return .relmipgap suffix (relative to |obj|) 2 - Return .absmipgap suffix (absolute mipgap) 4 - Suppress mipgap values in solve_message. Default = 0. The suffixes are on the objective and problem. Returned suffix values are +Infinity if no integer-feasible solution has been found, in which case no mipgap values are reported in the solve_message. mip:round (round) Whether to round integer variables to integral values before returning the solution, and whether to report that the solver returned noninteger values for integer values: sum of 1 ==> Round nonintegral integer variables 2 ==> Modify solve_result 4 ==> Modify solve_message Default = 0. Modifications that were or would be made are reported in solve_result and solve_message only if the maximum deviation from integrality exceeded mip:round_reptol. mip:round_reptol (round_reptol) Tolerance for reporting rounding of integer variables to integer values; see "mip:round". Default = 1e-9. mip:sbbest (sbbest) Number of infeasible global entities to initialize pseudo costs for on each node: -1 - automatic (default) 0 - disable strong branching n > 1 - perform strong branching on up to n entities at each node mip:sbeffort (sbeffort) Adjusts the overall amount of effort when using strong branching to select an infeasible global entity to branch on; default = 1. mip:sbestimate (sbestimate) How to compute pseudo costs from the local node when selecting an infeasible entity to branch on: -1 - automatic (default) 1-6 - different variants of local pseudo costs. mip:sbiterlimit (sbiterlimit) Number of dual iterations to perform the strong branching; 0=none, default = -1 (automatic choice) mip:sbselect (sbselect) size of candidate list for strong branching: -2 - automatic - low effort (default) -1 - automatic - high effort n > 0 - include max(n, sbbest) candidates mip:symmetry (symmetry) Amount of effort to detect symmetry in MIP problems: 0 - no simmetry detection 1 - conservative effort 2 - intensive effort mip:symselect (symselect) Adjusts the overall amount of effort for symmetry detection: 0 - search the whole matrix (otherwise the 0, 1 and -1 coefficients only) 1 - search all entities(otherwise binaries only) mip:threads (mipthreads) Determines the number of threads implemented to run the parallel MIP code; default -1: alg:threads will determine the number of threads. mip:toltarget (miptoltarget) Value of miptol used for refining equalities on MIP problems when refineops is 2 or 3; default = 0. mip:varselection (varselection) How to score the integer variables at a MIP node, for branching on a variable with minimum score: - 1 - automatic choice(default) 1 - minimum of the 'up' and 'down' pseudo - costs 2 - 'up' pseudo - cost + 'down' pseudo - cost 3 - maximum of the 'up' and 'down' pseudo - costs plus twice their minimum 4 - maximum of the 'up' and 'down' pseudo - costs 5 - the 'down' pseudo - cost 6 - the 'up' pseudo - cost 7 - weighted combination of the 'up' and 'down' pseudo costs 8 - product of 'up' and 'down' pseudo costs obj:multi (multiobj) 0*/1: Whether to use multi-objective optimization. If set to 1 multi-objective optimization is performed using lexicographic method with the first objective treated as the most important, then the second objective and so on. obj:no (objno) Objective to optimize: 0 - None 1 - First (default, if available) 2 - Second (if available), etc. pre:basisred (prebasisred) Determines if a lattice basis reduction algorithm should be attempted as part of presolve: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:bndredcone (prebndredcone) Determines if second order cone constraints should be used for inferring bound reductions on variables when solving a MIP: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:bndredquad (prebndredquad) Determines if convex quadratic contraints should be used for inferring bound reductions on variables when solving a MIP pre:cliquestrategy (precliquestrategy) Determines how much effort to spend on clique covers in presolve; default=-1. pre:coefelim (precoefelim) Specifies whether the optimizer should attempt to recombine constraints: 0 - disabled 1 - remove as many coefficients as possible 2 - cautious eliminations pre:components (precomponents) Determines whether small independent components should be detected and solved as individual subproblems during root node processing: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:componentseffort (precomponentseffort) adjusts the overall effort for the independent component presolver; default = 1.0. pre:configuration (preconfiguration) Whether to reformulate binary rows with very few coefficients: 0 - No 1 - Yes (default) pre:convertseparable (preconvertseparable) Reformulate problem with non-diagonal quadratic objective and/or constraints as diagonal quadratic or second-order conic constraints: -1 - automatic (default) 0 - disable 1 - enable reformulation to diagonal quadratic constraints. 2 - 1, plus reduction to second-order cones 3 - 2, plus the objective function is converted to a constraint and treated as a quadratic constraint pre:domcol (predomcol) Whether presolve should remove variables when solving MIP problems: -1 - automatic (default) 0 - disable 1 - cautious 2 - aggressive: all candidate will be checked pre:domrow (predomrow) Whether presolve should remove constraints when solving MIP problems: -1 - automatic choice (default) 0 - disabled 1 - cautious 2 - medium 3 - aggressive pre:duprow (preduprow) How presolve should deal with duplicate rows in MIP problems: -1 - automatic (default) 0 - disable 1 - eliminate only rows that are identical in all variables 2 - 1 plus eliminate duplicate rows with simple penalty variable expressions 3 - 2 plus eliminate duplicate rows with more complex penalty variable expressions pre:elimfillin (elimfillin) Maximum fillins allowed for a presolve elimination; default = 10 pre:elimquad (preelimquad) Allows for elimination of quadratic variables via doubleton rows: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:elimtol (elimtol) The Markowitz tolerance for the elimination phase of the presolve; default=0.001 pre:folding (prefolding) Determines if a folding procedure should be used to aggregate continuous columns in an equitable partition: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:genconsdualreductions (genconsdualreductions) Whether dual reductions should be applied to reduce the number of columns and rows added when transforming general constraints to MIP structs: 0 - No 1 - Yes (default) pre:implications (preimplications) Determines whether to use implication structures to remove redundant rows: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:indlinbigm (indprelinbigm) Largest "big M" value to use in converting indicator constraints to regular constraints during XPRESS presolve; default = 100.0 pre:lindep (prelindep) Determines whether to check for and remove linearly dependent equality constraints when presolving a problem: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:maxgrow (presolvemaxgrow) Limit on how much the number of non-zero coefficients is allowed to grow during presolve, specified as a ratio of the number of non-zero coefficients in the original problem; default=0.1 pre:maximpliedbound (maximpliedbound) When preprocessing MIP problems, only use computed bounds at most maximpliedbound (default 1e8) in absolute value pre:maxscalefactor (maxscalefactor) Maximum log2 factor that can be applied during scaling, must be >=0 and <=64; default=64. pre:objcutdetect (preobjcutdetect) MIP: Determines whether to check for constraints that are parallel or near parallel to a linear objective function, and which can safely be removed: 0 - No 1 - Yes (default) pre:objscalefactor (objscalefactor) Power of 2 (default 0) by which the objective is scaled. Nonzero objscalfactor values override automatic global objective scaling pre:ops (presolveops) Reductions to use in XPRESS's presolve, sum of: 1 = 2^0 - Remove singleton columns 2 = 2^1 - Remove singleton constraints (rows) 4 = 2^2 - Forcing row removal 8 = 2^3 - Dual reductions 16 = 2^4 - Redundant row removal 32 = 2^5 - Duplicate column removal 64 = 2^6 - Duplicate row removal 128 = 2^7 - Strong dual reductions 256 = 2^8 - Variable eliminations 512 = 2^9 - No IP reductions 1024 = 2^10 - No semi-continuous variable detection 2048 = 2^11 - No advanced IP reductions 4096 = 2^12 - No eliminations on integers 16384 = 2^14 - Linearly dependant row removal 32768 = 2^15 - No integer variable and SOS detection 536870912 = 2^29 - No dual reduction on globals (default 511 = bits 0-8 set) pre:passes (presolvepasses) Number of reduction rounds to be performed in presolve; default=1. pre:permute (prepermute) Bit vector: specifies whether to randomly permute rows, columns and global information when starting the presolve (default 0): 1 - permute rows 2 - permute columns 4 - permute global information (for MIP) pre:permuteseed (prepermuteseed) Sets the seed for the pseudo-random number generator for permuting; default=0 pre:probing (preprobing) Amount of probing to perform on binary variables during presolve. This is done by fixing a binary to each of its values in turn and analyzing the implications: -1 - automatic choice (default) 0 - disabled 1 - cautious 2 - medium 3 - aggressive pre:protectdual (preprotectdual) Specifies whether the presolver should protect a given dual solution by maintaining the same level of dual feasibility: .. value-table: pre:pwldualreductions (pwldualreductions) Whether dual reductions should be applied to reduce the number of columns, rows and SOS-constraints added when transforming piecewise linear objectives and constraints to MIP structs: 0 - No 1 - Yes (default) pre:pwlnonconvextransformation (pwlnonconvextransformation) Reformulation method for piecewise linear constraints at the beginning of the search: -1 - automatic (default) 0 - use a formulation based on SOS2-constraints 1 - use a formulation based on binary variables pre:rootpresolve (rootpresolve) Whether to presolve after root cutting and heuristics: -1 - Automatic choice (default) 0 - No 1 - Yes. pre:scaling (scaling) Bit vector determining how to scale the constraint matrix before optimizing: 1 - row scaling 2 - column scaling 4 - row scaling again 8 - maximum 16 - Curtis-Red 32 - 0->geometric mean, 1->maximum element 64 - no special handling for BigM rows 128 - scale objective function for the simplex method 256 - exclude the quadratic part of constraints when calculating scaling factors 512 - scale before presolve 1024 - do not scale constraints up 2048 - do not scale variables down 4096 - do not apply automatic global objective scaling 8192 - RHS scaling 16384 - disable aggressive quadratic scaling 32768 - explicit linear slack scaling pre:solve (presolve) Whether to use Xpress' presolve: 0 - No 1 - Yes, removing redundant bounds (default) 2 - Yes, retaining redundant bounds pre:sosreftol (sosreftol) Minimum relative gap between the ordering values of elements in a special ordered set; default=1e-6. pre:trace (trace) Display the infeasibility diagnosis during presolve: 0 - No (default) 1 - Yes. qp:eigenvaluetol (eigenvaluetol) Regard the matrix in a quadratic form as indefinite if its smallest eigvenalue is < -eigevnaltol; default = 1e-6 qp:miqcpalg (miqcpalg) Which algorithm is to be used to solve mixed integer quadratic constrained and mixed integer second order cone problems: -1 - automatic (default) 0 - barrier 1 - outer approximations qp:nonconvex (nonconvex) Determines if the convexity of the problem is checked before optimization: 0 - No 1 - Yes (default) qp:repairindefiniteq (repairindefq, repairindefiniteq) whether to repair indefinite quadratic forms: 0 - yes 1 - no (default) qp:simplexops (qsimplexops) Bit vector, controls the behavior of the quadratic simplex solvers: 1 - traditional primal first phase (default) 2 - force Big M primal first phase 4 - force traditional dual first phase 8 - force BigM dual first phase 16 - always use artificial bounds in dual 32 - use original problem basis only when warmstarting the KKT 64 - skip the primal bound flips for ranged primals (might cause more trouble than good if the bounds are very large) 128 - also do the single pivot crash 256 - do not apply aggressive perturbation in dual qp:unshift (quadunshift, quadraticunshift) whether quadratic simplex should do an extra purification after finding a solution: -1 - Automatic choice (default) 0 - No 1 - Yes. sol:count (countsolutions) 0*/1: Whether to count the number of solutions and return it in the ".nsol" problem suffix. sol:pooldualred (pooldualred) Whether to suppress removal of dominated solutions(via "dual reductions") when poolstub is specified: 0 - Yes (default, can be expensive) 1 - No 2 - Honor presolveops bit 3 (2^3 = 8) sol:pooldupcol (pooldupcol) Whether to suppress duplicate variable removal when poolstub is specified: 0 - Yes (default, can be expensive) 1 - No 2 - Honor presolveops bit 3 (2^3 = 8) sol:pooldups (poold/ups) How poolstub should handle duplicate solutions: 0 - Retain all duplicates 1 - Discard exact matches 2 - Discard exact matches of continuous variables and matches of rounded values of discrete variables 3 - Discard matches of rounded values of discrete variables (default) Rounding of discrete variables is affected bypoolmiptol and poolfeastol sol:poolfeastol (poolfeastol) Zero tolerance for discrete variables in the solution pool (default 1e-6) sol:poolmiptol (poolmiptol) Error (nonintegrality) allowed in discrete variables in the solution pool (default 5e-6) sol:poolnbest (poolnbest, poollimit) Whether the solution pool (see poolstub) should contain inferior solutions. When poolnbest = n > 1, the solution pool is allowed to keep the n best solutions. sol:stub (ams_stub, solstub, solutionstub) Stub for solution files. If "solutionstub" is specified, found solutions are written to files ("solutionstub & '1' & '.sol'") ... ("solutionstub & Current.nsol & '.sol'"), where "Current.nsol" holds the number of returned solutions. That is, file names are obtained by appending 1, 2, ... "Current.nsol" to "solutionstub". tech:cputime (cputime) How time should be measured when timings are reported in the log and when checking against time limits : -1 - disable the timer 0 - use elapsed time (default) 1 - use process time tech:debug (debug) 0*/1: whether to assist testing & debugging, e.g., by outputting auxiliary information. tech:exportfile (writeprob, writemodel) Specifies the name of a file where to export the model before solving it. This file name can have extension ".lp()", ".mps", etc. Default = "" (don't export the model). tech:globalfileloginterval (globalfileloginterval) Seconds between additions to the logfile about, additions to the "global file", a temporary file written during a global search. Default = 60. tech:globalfilemax (globalfilemax) Maximum megabytes for temporary files storing the global search tree: a new file is started if globalfilemax megabytes would be exceeded. tech:justexportfile (justwriteprob, justwritemodel) Specifies the name of a file where to export the model, do not solve it.This file name can have extension ".lp()", ".mps", etc. Default = "" (don't export the model). tech:optionfile (optionfile, option:file) Name of solver option file. (surrounded by 'single' or "double" quotes if the name contains blanks). Lines that start with # are ignored. Otherwise, each nonempty line should contain "name=value". tech:outlev (outlev) Whether to write xpress log lines (chatter) to stdout and to file: 0 - none 1 - all 2 - information 3 - warnings & errors only (default) 4 - errors 5 - none tech:sleeponthreadwait (sleeponthreadwait) Whether threads should sleep while awaiting work: -1 - automatically determined 0 - no (busy-wait) >0 - yes (sleep, might add overhead) tech:threads (threads) The default number of threads used during optimization.;default - 1 ==> automatic choice. tech:timing (timing) 0*/1: Whether to display timings for the run. tech:tunebase (tunerdir, tunebase) Base name for results of running XPRESS's search for best parameter settings. The search is run only when tunebase is specified. This control only defines the root path for the tuner output. For each problem, the tuner result will be output to a subfolder underneath this path. For example, by default, the tuner result for a problem called prob will be located at tuneroutput/prob/ tech:tunename (tunesessionname) Set problem name within the tuner "tunebase" is specified. tech:tuneoutput (tuneroutput, tuneoutput) Output tuner results and logs to the file system when "tunebase" is specified: 0 - No 1 - Yes (default) tech:tunerhistory (tunerhistory) Reuse and append to previous tuner results of the same problem: 0 - Discard any previous result 1 - Append new results but do not reuse them 2 - Reuse and append new results tech:tunermethod (tunermethod) Method for tuning when "tunebase" is specified: - 1 - automatic choice(default) 0 - default LP tuner 1 - default MIP tuner 2 - more elaborate MIP tuner 3 - root - focused MIP tuner 4 - tree - focused MIP tuner 5 - simple MIP tuner 6 - default SLP tuner 7 - default MISLP tuner 8 - MIP tuner using primal heuristics tech:tunertarget (tunertarget) What to measure to compare two problem solutions when running the XPRESS tuner: - 1 - automatic choice(default) 0 - solution time, then integrality gap 1 - solution time, then best bound 2 - solution time, then best integer solution 3 - the "primal dual integral", whatever that is 4 - just solution time (default for LPs) 5 - just objective value 6 - validation number (probably not relevant) 7 - gap only 8 - best bound only 9 - best integer solution only 10 - best primal integral - only for individual instances tech:tunerthreads (tunerthreads) Number of tuner threads to run in parallel; default=-1 (automatic) tech:tunerverbose (tunerverbose) whether the tuner should prints detailed information for each run: 0 - No 1 - Yes (default) tech:tunetimelim (tunermaxtime, tunetimelim, lim:tunetime) Time limit (in seconds) on tuning when "tunebase" is specified; default 0 (no time limit). tech:version (version) Single-word phrase: report version details before solving the problem. tech:wantsol (wantsol) In a stand-alone invocation (no "-AMPL" on the command line), what solution information to write. Sum of 1 - Write ".sol" file 2 - Primal variables to stdout 4 - Dual variables to stdout 8 - Suppress solution message. tech:writegraph (writegraph, exportgraph) File to export conversion graph. Format: JSON Lines.