test_knitro.opt

# Artelys Knitro 12.0.0 Options file
# http://www.artelys.com/tools/knitro_doc/

# Which algorithm to use.
#   auto   = 0 = let Knitro choose the algorithm
#   direct = 1 = use Interior (barrier) Direct algorithm
#   cg     = 2 = use Interior (barrier) CG algorithm
#   active = 3 = use Active Set SLQP algorithm
#   sqp    = 4 = use Active Set SQP algorithm
#   multi  = 5 = run multiple algorithms (perhaps in parallel)
algorithm    auto

# LP algorithm for subproblems in active-set/SQP algorithms.
#   default    = 0
#   primal     = 1
#   dual       = 2
#   barrier    = 3
act_lpalg    default

# Dump LP subproblems to MPS files in active-set algorithm.
#   no         = 0
#   yes        = 1
act_lpdumpmps     no

# Feasibility tolerance for LP subproblems in Active or SQP algorithms.
act_lpfeastol   1e-08

# Constraint penalization for LP subproblems.
#   auto       = 0
#   all        = 1
#   nonlinear  = 2
#   dynamic    = 3
act_lppenalty    all

# Controls LP presolve for subproblems in active-set/SQP algorithms.
#   off        = 0
#   on         = 1
act_lppresolve   off

# Which LP solver to use in the Active or SQP algorithm.
#   internal = 1 = use internal LP solver
#   cplex    = 2 = CPLEX (if user has a valid license)
#   xpress   = 3 = XPRESS (if user has a valid license)
act_lpsolver internal

# Use parametric approach in active-set algorithm.
#   no         = 0 = never
#   maybe      = 1 = use selectively
#   yes        = 2 = always use parametric approach
act_parametric    maybe

# Which algorithm to use for QP subproblem solves in Active or SQP algorithms.
#   auto   = 0 = let Knitro choose the algorithm
#   direct = 1 = use Interior (barrier) Direct algorithm
#   cg     = 2 = use Interior (barrier) CG algorithm
#   active = 3 = use Active Set SLQP algorithm
act_qpalg    auto

# Enable specialized algorithm for conic constraints.
#   none       = 0
#   soc        = 1
bar_conic_enable  none

# When using the Interior/Direct algorithm, this parameter
# controls the maximum number of consecutive CG steps before
# trying to force the algorithm to take a direct step again.
# (negative implies auto; only used for alg=1).
bar_directinterval  -1

# Whether feasibility is given special emphasis.
#   no       = 0 = no emphasis on feasibility
#   stay     = 1 = iterates must honor inequalities
#   get      = 2 = emphasize first getting feasible before optimizing
#   get_stay = 3 = implement both options 1 and 2 above
bar_feasible no

# Specifies the tolerance for entering the stay feasible mode
# (only valid when bar_feasible = stay or bar_feasible = get_stay).
bar_feasmodetol  0.0001

# Initial value for the barrier parameter (non-positive implies auto).
bar_initmu   -1

# Initial value for the barrier MPEC penalty parameter.
bar_initpi_mpec  0

# Strategy for setting initial x, lambda and slacks with barrier algorithms.
# This option only affects the initial x value when not provided by user.
#   auto    = 0 = let Knitro choose the strategy
#   convex  = 1 = initial point strategy 1 (mainly for convex problems)
#   nearbnd = 2 = initial point strategy staying closer to bounds
#   central = 3 = more central initial point strategy
bar_initpt   auto

# Maximum number of correctors allowed when computing primal-dual barrier step
# (negative implies auto).
bar_maxcorrectors  -1

# Maximum number of crossover iterations to allow for barrier algorithms.
bar_maxcrossit   0

# Maximum number of refactorizations of the KKT system per iteration of the
# Interior Direct algorithm before reverting to a CG step.
# (negative implies auto; only used for alg=1).
bar_maxrefactor  -1

# Which barrier parameter update strategy.
#   auto     = 0 = let Knitro choose the strategy
#   monotone = 1
#   adaptive = 2
#   probing  = 3
#   dampmpc  = 4
#   fullmpc  = 5
#   quality  = 6
bar_murule   auto

# Whether or not to penalize constraints in the barrier algorithms.
#   auto       = -1 = let Knitro choose the strategy
#   none       =  0 = Do not apply penalty approach to any constraints
#   all        =  2 = Apply a penalty approach to all general constraints
#   equalities =  3 = Apply a penalty approach to equality constraints only
bar_penaltycons   auto

# Which penalty parameter update strategy for barrier algorithms.
#   auto     = 0 = let Knitro choose the strategy
#   single   = 1 = use single penalty parameter approach
#   flex     = 2 = use more tolerant flexible strategy
bar_penaltyrule   auto

# Whether to try to refine the barrier solution for better precision.
#   no     = 0 = do not refine the barrier solution
#   yes    = 1 = try to refine the barrier solution
bar_refinement    no

# Whether to relax the general constraints for barrier algorithms.
#   none   = 0 = do not relax any constraints
#   eqs    = 1 = relax only equality constraints
#   ineqs  = 2 = relax only inequality constraints
#   all    = 3 = relax all general constraints
bar_relaxcons     ineqs

# Amount by which barrier slacks are initially pushed interior
# (non-positive implies auto).
bar_slackboundpush  -1

# Objective form when switching to feasibility phase.
#   none       = 0 = no objective
#   scalarprox = 1 = proximal point objective with scalar weighting
#   diagprox   = 2 = proximal point objective with diagonal weighting
bar_switchobj     scalarprox

# Switching rule strategy for barrier algorithms that controls
# switching between optimality and feasibility phases.
#   auto       = -1 = let Knitro choose the strategy
#   never      =  0 = never switch
#   moderate   =  2 = allow moderate switching
#   aggressive =  3 = more aggressive switching
bar_switchrule    auto

# Whether to activate watchdog heuristic for barrier algorithms.
#   no     = 0 = no watchdog heuristic
#   yes    = 1 = allow watchdog heuristic to be used
bar_watchdog      no

# Which BLAS/LAPACK library to use.  Intel MKL library is only available
# on some platforms; see the User Manual for details.
#   knitro  = 0 = use Knitro version of netlib functions
#   intel   = 1 = use Intel MKL functions
#   dynamic = 2 = use dynamic library of functions
blasoption   intel

# Valid range for constraint or variable bounds.
bndrange     1e+20

# Maximum allowable CG iterations per trial step
# (if 0 then Knitro determines the best value).
cg_maxit      0

# Amount of memory used by incomplete Choleski preconditioner.
cg_pmem       10

# Whether or not to use incomplete Choleski preconditioner.
cg_precond    0

# Stopping tolerance for CG subproblems.
cg_stoptol    0.01

# Declare the problem as convex.
#   auto       = -1
#   no         = 0
#   yes        = 1
convex            auto

# target CPU platform/architecture.
#   auto       = -1 = determine automatically
#   compatible =  1 = aim for more compatible performance across architectures
#   sse2       =  2 = SSE2
#   avx        =  3 = AVX
#   avx2       =  4 = AVX-2
#   avx512     =  5 = AVX-512 (experimental)
cpuplatform       auto

# Whether to perform extra data checks on the model.
#   no     = 0 = no extra data checks
#   yes    = 1 = perform extra data checks
datacheck    yes

# Specifies debugging level of output.  Debugging output is intended for Artelys
# developers.  Debugging mode may impact performance and is NOT recommended
# for production operation.
#   none      = 0 = no extra debugging
#   problem   = 1 = help debug solution of the problem
#   execution = 2 = help debug execution of the solver
debug        none

# Initial trust region radius scaling factor, used to determine
# the initial trust region size.
delta        1

# Whether to perform a derivative check on the model.
#   none    = 0 = no derivative check
#   first   = 1 = check first derivatives
#   second  = 2 = check second derivatives
#   all     = 3 = check all derivatives
derivcheck   none

# Termination for derivative check.
#   error  = 1 = stop when there is an error detected
#   always = 2 = always stop after the derivative check
derivcheck_terminate error

# Specifies the relative tolerance used for the derivative check.
derivcheck_tol 1e-06

# Type of derivative check.
#   forward = 1 = check using forward finite-differences
#   central = 2 = check using central finite-differences
derivcheck_type forward

# Enable evaluating gradients with functions in one callback.
#   no         = 0
#   yes        = 1
eval_fcga         no

# Specifies the final relative stopping tolerance for the feasibility
# error. Smaller values of feastol result in a higher degree of accuracy
# in the solution with respect to feasibility.
feastol      1e-06

# Specifies the final absolute stopping tolerance for the feasibility error.
# Smaller values of feastol_abs result in a higher degree of accuracy in the
# solution with respect to feasibility.
feastol_abs  0.001

# Termination method when using finite-difference gradients.
#   none   = 0 = no special termination
#   errest = 1 = terminate on gradient error estimates
findiff_terminate errest

# Value used for objective function value based termination.
fstopval     1.79769e+308

# Tolerance for stopping on small changes to the objective.
ftol         1e-15

# Consecutive iterations for stopping on small changes to the objective.
ftol_iters   5

# How to compute/approximate the gradient of the objective
# and constraint functions.
#   exact        = 1 = user supplies exact first derivatives
#   forward      = 2 = gradients computed by internal forward finite differences
#   central      = 3 = gradients computed by internal central finite differences
#   user_forward = 4 = gradients computed by user-provided forward finite differences
#   user_central = 5 = gradients computed by user-provided central finite differences
gradopt      exact

# Whether to allow computing the Hessian of the Lagrangian without objective component.
#   forbid       = 0 = not allowed
#   allow        = 1 = user can provide this version of the Hessian if requested
hessian_no_f forbid

# How to compute/approximate the Hessian of the Lagrangian.
#   auto            = 0 = determined automatically by Knitro
#   exact           = 1 = user supplies exact second derivatives
#   bfgs            = 2 = Knitro computes a dense quasi-Newton BFGS Hessian
#   sr1             = 3 = Knitro computes a dense quasi-Newton SR1 Hessian
#   product_findiff = 4 = Knitro computes Hessian-vector products by finite differences
#   product         = 5 = user supplies exact Hessian-vector products
#   lbfgs           = 6 = Knitro computes a limited-memory quasi-Newton BFGS Hessian
hessopt      auto

# Whether to enforce satisfaction of simple bounds at all iterations.
#   auto    = -1 = setting determined automatically by Knitro
#   no      =  0 = allow iterations to violate the bounds
#   always  =  1 = enforce bounds satisfaction of all iterates
#   initpt  =  2 = enforce bounds satisfaction of initial point
honorbnds    auto

# Specifies relative stopping tolerance used to declare infeasibility.
infeastol    1e-08

# Initial value for the merit function penalty parameter.
initpenalty  10

# Which linesearch method to use.
#   auto        = 0 = let Knitro choose the linesearch method
#   backtrack   = 1 = backtracking linesearch
#   interpolate = 2 = interpolation based linesearch
linesearch   auto

# Maximum allowable number of trial values during the linesearch of the
# Interior Direct or SQP algorithm.
linesearch_maxtrials  3

# Which linear system solver to use.
#   auto       = 0 = let Knitro choose the solver
#   internal   = 1 = use internal solver provided with Knitro
#                  (not currently active; reserved for future use)
#   hybrid     = 2 = use a mixture of linear solvers depending on the linear systems
#   qr         = 3 = use dense QR solver always (only for small problems)
#   ma27       = 4 = use sparse HSL solver ma27 always
#   ma57       = 5 = use sparse HSL solver ma57 always
#   mklpardiso = 6 = use sparse Intel MKL Pardiso solver always
#   ma97       = 7 = use parallel, deterministic HSL ma97 solver
#   ma86       = 8 = use parallel, non-deterministic HSL ma86 solver
linsolver    auto

# Whether to use out-of-core version of linsolver=mklpardiso.
#   no     = 0 = always use in-core version
#   maybe  = 1 = will use out-of-core version beyond a certain size
#   yes    = 2 = always use out-of-core version
linsolver_ooc no

# Specifies the initial pivot threshold used in the factorization routine.
# The value must be in the range [0 0.5] with higher values resulting
# in more pivoting (more stable factorization). Values less than 0 will
# be set to 0 and values larger than 0.5 will be set to 0.5. If pivot
# is non-positive initially no pivoting will be performed. Smaller values
# may improve the speed of the code but higher values are recommended for
# more stability.
linsolver_pivottol 1e-08

# Number of limited memory pairs to store when Hessian choice is lbfgs.
lmsize       10

# Maximum allowable CPU time in seconds for the complete multi algorithm
# solution when 'alg=multi'.  Use maxtime_cpu to additionally limit time
# spent per each algorithm.
ma_maxtime_cpu  1e+08

# Maximum allowable real time in seconds for the complete multi algorithm
# solution when 'alg=multi'.  Use maxtime_real to additionally limit time
# spent per each algorithm.
ma_maxtime_real 1e+08

# Specifies multi algorithm subproblem solve output control.
#   0 = no output from subproblem solves
#   1 = Subproblem output enabled, controlled by option 'outlev'.
#       Output is directed to a file 'knitro_ma_*.log'
ma_outsub 0

# Specifies conditions for terminating when 'algorithm=multi'.
#   all       = 0 = terminate after all algorithms complete
#   optimal   = 1 = terminate at first local optimum
#   feasible  = 2 = terminate at first feasible solution estimate
#   any       = 3 = terminate at first completed solve
ma_terminate optimal

# Maximum number of function evaluations to allow
# (a negative number implies no limit is imposed).
maxfevals    -1

# Maximum number of iterations to allow
# (if 0 then Knitro determines the best value).
# Default values are 10000 for NLP and 3000 for MIP.
maxit        0

# Maximum allowable CPU time in seconds for one algorithm solve.
# If multistart, multi algorithm or MIP is active, this limits time spent
# on just one subproblem solve.
maxtime_cpu  1e+08

# Maximum allowable real time in seconds for one algorithm solve.
# If multistart, multi algorithm or MIP is active, this limits time spent
# on just one subproblem solve.
maxtime_real 1e+08

# Specifies the MIP branching rule for choosing a variable.
#   auto        = 0 = let Knitro choose the rule
#   most_frac   = 1 = most fractional (most infeasible) variable
#   pseudocost  = 2 = use pseudo-cost value
#   strong      = 3 = use strong branching
mip_branchrule auto

# Specifies the MIP select direction for choosing a node.
#   down        = 0 = choose the lesser-than node first
#   up          = 1 = choose the greater-than node first
mip_cutfactor 1

# Specifies debugging level for MIP solution.
#   none = 0 = no MIP debugging info
#   all  = 1 = write debugging to the file kdbg_mip.log
mip_debug none

# Whether to branch on generalized upper bounds (GUBs).
#   no   = 0 = do not branch on GUBs
#   yes  = 1 = branch on GUBs
mip_gub_branch no

# Maximum number of nodes explored (0 means no limit).
mip_heuristic auto

# Specifies which MIP heuristic search approach to apply
# to try to find an initial integer feasible point.
#   auto     = -1 = let Knitro choose the heuristic
#   none     =  0 = no heuristic search applied
#   feaspump =  2 = apply feasibility pump heuristic
#   mpec     =  3 = apply MPEC heuristic
mip_heuristic_maxit 100

# Specifies which algorithm to use for standard node subproblem solves in MIP
#   auto   = 0 = let Knitro choose the algorithm
#   direct = 1 = use Interior (barrier) Direct algorithm
#   cg     = 2 = use Interior (barrier) CG algorithm
#   active = 3 = use Active Set SLQP algorithm
#   sqp    = 4 = use Active Set SQP algorithm
#   multi  = 5 = run multiple algorithms (perhaps in parallel)
mip_heuristic_terminate feasible

# Whether to add logical implications deduced from
# branching decisions at a MIP node.
#   no   = 0 = do not add logical implications
#   yes  = 1 = add logical implications
mip_implications yes

# Threshold for deciding if a variable value is integral.
mip_integer_tol 1e-08

# Specifies absolute stop tolerance for sufficiently small integrality gap.
mip_integral_gap_abs 1e-06

# Specifies relative stop tolerance for sufficiently small integrality gap.
mip_integral_gap_rel 1e-06

# Maximum number of levels on which to perform MIP strong branching.
mip_intvar_strategy none

# Specifies rules for adding MIP knapsack cuts.
#   none      = 0 = do not add knapsack cuts
#   ineqs     = 1 = add cuts derived from inequalities
#   lifted    = 2 = add lifted knapsack cuts
#   all       = 3 = add inequality and lifted knapsack cuts
mip_knapsack ineqs

# Limit on the number of cuts added to node NLP; if nonnegative,
# a maximum of mip_cutfactor times number of constraints cuts is
# possibly appended.
mip_zerohalf root

# Specifies which algorithm to use for the root node solve in MIP
#   auto   = 0 = let Knitro choose the algorithm
#   direct = 1 = use Interior (barrier) Direct algorithm
#   cg     = 2 = use Interior (barrier) CG algorithm
#   active = 3 = use Active Set SLQP algorithm
#   sqp    = 4 = use Active Set SQP algorithm
#   multi  = 5 = run multiple algorithms (perhaps in parallel)
mip_lpalg auto

# Specifies conditions for terminating the MIP algorithm.
#   optimal   = 0 = terminate at optimum
#   feasible  = 1 = terminate at first integer feasible point
mip_maxnodes 100000

# Maximum number of subproblem solves allowed (0 means no limit).
mip_maxsolves 200000

# Maximum allowable CPU time in seconds for the complete MIP solution.
# Use maxtime_cpu to additionally limit time spent per subproblem solve.
mip_maxtime_cpu 1e+08

# Maximum allowable real time in seconds for the complete MIP solution.
# Use maxtime_real to additionally limit time spent per subproblem solve.
mip_maxtime_real 1e+08

# Which MIP method to use.
#   auto  = 0 = let Knitro choose the method
#   BB    = 1 = standard branch and bound
#   HQG   = 2 = hybrid Quesada-Grossman
#   MISQP = 3 = mixed-integer SQP
mip_method auto

# Whether integer variables are relaxable.
#   none   = 0 = integer variables not relaxable
#   all    = 1 = all integer variables are relaxable
mip_nodealg auto

# Specifies printing interval for mip_outlevel.
#   1 = print every node
#   2 = print every 2nd node
#   N = print every Nth node
mip_outinterval 10

# How much MIP information to print.
#   none      = 0 = nothing
#   iters     = 1 = one line for every node
#   iterstime = 2 = also print accumulated time every node
#   root      = 3 = also print output from root node relaxation solve
mip_outlevel iters

# Specifies MIP subproblem solve output control.
#   0 = no output from subproblem solves
#   1 = Subproblem output enabled, controlled by option 'outlev'
#   2 = Subproblem output enabled and print problem characteristics
mip_outsub 0

# Specifies MIP subproblem solve output control.
#   0 = no output from subproblem solves
#   1 = Subproblem output enabled, controlled by option 'outlev'
#   2 = Subproblem output enabled and print problem characteristics
mip_pseudoinit auto

# How to handle integer variables by default.
#   none   = 0 = no special treatment
#   relax  = 1 = relax integer variables
#   mpec   = 2 = convert to mpec constraints
mip_relaxable  all

# Specifies the MIP rounding rule to apply.
#   auto          = -1 = let Knitro choose the rule
#   none          =  0 = do not round if a node is infeasible
#   heur_only     =  2 = round using heuristic only (fast)
#   nlp_sometimes =  3 = round and solve NLP if likely to succeed
#   nlp_always    =  4 = always round and solve NLP
mip_rootalg auto

# Specifies rules for adding MIP ZERO HALF cuts.
#   none      = 0 = do not add zerohalf cuts
#   ineqs     = 1 = add cuts derived in the root node
#   lifted    = 2 = add zerohalf cuts in the tree
#   all       = 3 = add zero half cuts in the root and he tree
mip_rounding auto

# Specifies conditions for terminating the MIP heuristic.
#   feasible  = 1 = terminate at first feasible point
#   limit     = 2 = run heuristic until it hits limit
mip_selectdir down

# Specifies the MIP select rule for choosing a node.
#   auto        = 0 = let Knitro choose the rule
#   depth_first = 1 = search the tree depth first
#   best_bound  = 2 = node with the best relaxation bound
#   combo_1     = 3 = depth first unless pruned, then best bound
mip_selectrule auto

# Maximum number of iterations to allow for MIP strong branching solves.
mip_strong_candlim 10

# Maximum number of candidates to explore for MIP strong branching.
mip_strong_level 10

# How to initialize pseudo-costs.
#   auto   = 0 = let Knitro choose the method
#   ave    = 1 = use average value
#   strong = 2 = use strong branching
mip_strong_maxit 1000

# Specifies which algorithm to use for LP subproblem solves in MIP.
#   auto   = 0 = let Knitro choose the algorithm
#   direct = 1 = use Interior (barrier) Direct algorithm
#   cg     = 2 = use Interior (barrier) CG algorithm
#   active = 3 = use Active Set (simplex) algorithm
mip_terminate optimal

# Whether to use a deterministic version of multistart.
#   no   = 0 = multithreaded multistart is non-deterministic
#   yes  = 1 = multithreaded multistart is deterministic
#              (when ms_terminate=maxsolves)
ms_deterministic  yes

# Whether to enable multistart to find a better local minimum.
#   no   = 0 = Knitro solves from a single initial point
#   yes  = 1 = Knitro solves using multiple start points
ms_enable    no

# Specifies the maximum range that an unbounded variable can vary over when
# multistart computes new start points.
ms_maxbndrange 1000

# How many Knitro solutions to compute if multistart is enabled.
#   choose any positive integer, or
#   0 = Knitro sets it to min{200,10*n}
ms_maxsolves 0

# Maximum allowable CPU time in seconds for the complete multistart
# solution.  Use maxtime_cpu to additionally limit time spent per start point.
ms_maxtime_cpu  1e+08

# Maximum allowable real time in seconds for the complete multistart
# solution.  Use maxtime_real to additionally limit time spent per start point.
ms_maxtime_real 1e+08

# How many feasible multistart points to save in file knitro_mspoints.log.
#   choose any positive integer, or
#   0 = save none
ms_num_to_save 0

# Specifies parallel multistart subproblem solve output control.
#   0 = no output from subproblem solves
#   1 = Subproblem output enabled, controlled by option 'outlev'.
#       Output is directed to a file 'knitro_ms_*.log'
ms_outsub 0

# Specifies the tolerance for deciding two feasible points are the same.
ms_savetol 1e-06

# Specifies the seed for random initialization of the multistart procedure.
# Seed value should an integer >= 0.  Negative values will be reset to 0.
ms_seed 0

# Specifies the maximum range that any variable can vary over when
# multistart computes new start points.
ms_startptrange 1e+20

# Specifies conditions for terminating the multistart procedure.
#   maxsolves = 0 = terminate after maxsolves
#   optimal   = 1 = terminate at first local optimum
#   feasible  = 2 = terminate at first feasible solution estimate
#   any       = 3 = terminate at first completed solve
ms_terminate maxsolves

# Specifies additional action to take after every iteration.
# Iterations result in a new solution estimate.
#   none     = 0 = no additional action
#   saveone  = 1 = save the latest new point to file knitro_newpoint.log
#   saveall  = 2 = append the latest new point to file knitro_newpoint.log
#   user     = 3 = allow a user-specified routine to run after iterations
newpoint     none

# Valid range of obective values.
objrange     1e+20

# Specifies the final relative stopping tolerance for the KKT (optimality)
# error. Smaller values of opttol result in a higher degree of accuracy in
# the solution with respect to optimality.
opttol       1e-06

# Specifies the final absolute stopping tolerance for the KKT (optimality)
# error. Smaller values of opttol_abs result in a higher degree of accuracy
# in the solution with respect to optimality.
opttol_abs   0.001

# Whether to generate a csv solution file.
#   no     = 0 = no csv solution file is generated
#   yes    = 1 = generate a solution file 'knitro_solve.csv' 
out_csvinfo no

# Name for the csv file generated by 'out_csvinfo' (default 'knitro_solve.csv').
# This option should be set before calling KTR_init_problem().
#out_csvname  .

# Enable output printing of hints for setting parameters.
#   no         = 0
#   yes        = 1
out_hints         yes

# Specifies whether to append to output files.
# This option should be set before calling KTR_init_problem().
#   no     = 0 = erase existing files when opening
#   yes    = 1 = append to existing files
outappend    no

# Directory for all output files.
# This option should be set before calling KTR_init_problem().
#outdir       .

# Specifies the verbosity of output.
#   none         = 0 = nothing
#   summary      = 1 = only final summary information
#   iter_10      = 2 = information every 10 iterations is printed
#   iter         = 3 = information at each iteration is printed
#   iter_verbose = 4 = more verbose information at each iteration is printed
#   iter_x       = 5 = in addition, values of solution vector (x) are printed
#   all          = 6 = in addition, constraints (c) and multipliers (lambda)
outlev       iter_10

# Where to direct the output.
#   screen  = 0 = directed to stdout
#   file    = 1 = directed to a file (default name 'knitro.log')
#   both    = 2 = both stdout and file (default name 'knitro.log')
outmode      screen

# Name for the standard log file generated by Knitro (default 'knitro.log').
# This option should be set before calling KTR_init_problem().
#outname      .

# Whether to allow simultaneous evaluations in parallel.
#   no   = 0 = only one thread can perform an evaluation at a time
#   yes  = 1 = allow multi-threaded simultaneous evaluations
par_blasnumthreads 0

# Number of threads to use in parallel features.
#   choose any positive integer, or
#   0 = value determined by OMP_NUM_THREADS environment variable
#  -1 = run sequential version of Knitro code
par_concurrent_evals  yes

# Number of threads to use in parallel BLAS.
#   choose any positive integer, or
#   0 = determine automatically based on par_numthreads
par_lsnumthreads 0

# Number of threads to use in parallel linear solver.
#   choose any positive integer, or
#   0 = determine automatically based on par_numthreads
par_msnumthreads 0

# Number of threads to use in parallel linear solver.
#   choose any positive integer, or
#   0 = determine automatically based on par_numthreads
par_numthreads 1

# Whether to apply a presolve operation to the model.
#   none   = 0 = no presolve
#   basic  = 1 = Knitro performs basic presolve
presolve     basic

# Specifies the tolerance used to determine whether or not deduced bounds.
# from the presolve operation are infeasible.
presolve_tol 1e-06

# Maximum number of internal restarts to allow.
restarts        0

# Maximum number of iterations before invoking restart heuristic.
restarts_maxit  0

# Whether to perform scaling of the problem.
# no            = 0 = no scaling done
# user_internal = 1 = user, if defined, otherwise internal
# user_none     = 2 = user, if defined, otherwise none
# internal      = 3 = Knitro performs internal scaling
scale        user_internal

# Whether to use the Second Order Correction (SOC) option.
#   no     = 0 = never do second order corrections
#   maybe  = 1 = SOC steps attempted on some iterations
#   yes    = 2 = SOC steps always attempted when constraints are nonlinear
soc          maybe

# Enable a warm-start strategy.
#   no         = 0
#   yes        = 1
strat_warm_start  no

# Whether to use the Knitro Tuner.
#   off    = 0 = Knitro Tuner turned off
#   on     = 1 = Knitro Tuner enabled
tuner        off

# Maximum allowable CPU time in seconds for the complete Tuner procedure
# when 'tuner=on'.  Use maxtime_cpu to additionally limit time
# spent per each individual solve.
tuner_maxtime_cpu  1e+08

# Maximum allowable real time in seconds for the complete Tuner procedure
# when 'tuner=on'.  Use maxtime_real to additionally limit time
# spent per each individual solve.
tuner_maxtime_real  1e+08

# Specifies Tuner subproblem solve output control.
#   0 = no output from subproblem solves and no subproblem summary file
#   1 = Subproblem output summary directed to a file 'knitro_tuner_summary.log'
#   2 = Subproblem output enabled, controlled by option 'outlev'.
#       Output is directed to a file 'knitro_tuner_*.log'
tuner_outsub 0

# Specifies conditions for terminating Tuner procedure.
#   all       = 0 = terminate after all Tuner runs complete
#   optimal   = 1 = terminate at first local optimum
#   feasible  = 2 = terminate at first feasible solution estimate
#   any       = 3 = terminate at first completed solve
tuner_terminate all

# Step size tolerance used for terminating the optimization.
xtol         1e-12

# Consecutive iterations for stopping on small changes in the solution estimate.
xtol_iters   0