|
MoochoPack : Framework for Large-Scale Optimization Algorithms
Version of the Day
|
|
MoochoPack : Framework for Large-Scale Optimization Algorithms
Version of the Day
|
The following is the contents of the file Moocho.opt.MoochoSolver which are options specific to the class MoochoPack::MoochoSolver. For options specific to the NLPAlgoConfigMamaJama configuration class see the documentation for MoochoPack::NLPAlgoConfigMamaJama.
*** Begin Moocho.opt.MoochoSolver
*************************************************************************************
*** All of these options can be used with the class MoochoSolver.
***
*** This file will be maintained and will include every option that
*** users can set. Most of these options the user will want to
*** leave alone but they are there in any case.
***
*** For options specific to the NLPAlgoConfigMamaJama configuration
*** class see the file 'Moocho.opt.NLPAlgoConfigMamaJama'.
***
**********************************************************
*** Options specific for the MoochoSolver class.
***
*** These options work on the highest level in determining
*** what output files are allowed, workspace requirements,
*** objective function scaling etc.
***
options_group MoochoSolver {
* workspace_MB = -1.0; *** [default]
*** (+-dbl) If > 0, givens the number of megabytes that are allocated for
*** temporary workspace for automatic arrays. If < 0 then the
*** this will be determined internally. This value should be set by the
*** user for whatever is appropriate for the computing environment. See
*** the summary output for statistics on memory allocation usage when the
*** algorithm finishes. Example values:
*** -1.0 : (< 0) Allow the algorithm to decide how much to allocate.
*** 100 : Allocate 100 MB of ram for this purpose
* obj_scale = 1.0; *** [default]
*** (+-dbl) Scale for the objective function. This can have a dramatic impact
*** on the behavior of the algorithm in some cases. Changing this value
*** does a lot to change the weight between minimizing the objective and converging
*** the constraints. Example values:
*** 1e-8 : Really scale the objective down a lot!
*** 1.0 : Leave the objective unscaled [default].
*** 1e+8 : Really scale the objective up a lot!
* test_nlp = true; *** [default]
* test_nlp = false;
*** If true then the NLP will be tested at the initial point. The
*** vector spaces (see options_group VectorSpaceTester),
*** the NLP interface (see options_group NLPTester), and the gradients
*** (see options_group NLPDirectTester and NLPFirstOrderInfoTester)
*** will all be tested. With all of the default options in place
*** these tests are fairly cheap so it is recommended that you perform
*** these tests when first getting started.
* console_outputting = true; *** [default]
* console_outputting = false;
*** If true, then output from MoochoTrackerConsoleStd is sent to the
*** console_out stream (which is std::cout by default)
* summary_outputting = true; *** [default]
* summary_outputting = false;
*** If true, then output from MoochoTrackerSummaryStd is sent to the
*** summary_out stream (which is the file 'MoochoSummary.out' by default)
* journal_outputting = true; *** [default]
* journal_outputting = false;
*** If true, then output from the algorithm steps an other detailed testing
*** output is set to the journal_out stream (which is the file
*** 'MoochoJournal.out' by default)
* algo_outputting = true; *** [default]
* algo_outputting = false;
*** If true, then an algorithm description is sent to the algo_out stream
*** (which is the file 'MoochoAlgo.out' by default)
* print_algo = true; *** [default]
* print_algo = false;
*** [algo_outputting == true]
*** If true then the algorithm will be printed to the algo_out stream
*** (the file 'NLPAlgo.out' by default). In order to get more insight into
*** what all of the options do it is a good idea to print the algorithm
*** description out and search for the options you are curious about.
* algo_timing = true; *** [default]
* algo_timing = false;
*** [summary_outputting == true]
*** If true, then the steps in the algorithm will be timed and a table of the
*** algorithm and step times will be sent to the summary_out stream (the file
*** 'MoochoSummary.out' by default). This feature is very useful in examining
*** performance of the algorithm and can give more detailed information than you
*** get from a profiler in may ways.
* generate_stats_file = true;
* generate_stats_file = false; *** [default]
*** If true, then a MoochoTrackerStatsStd object will be used to generate
*** statistics about the solution process to an NLP. The track object
*** will overwrite the file 'MoochoStats.out' in the current directory.
* print_opt_grp_not_accessed = true; *** [default]
* print_opt_grp_not_accessed = false;
*** [algo_outputting == true]
*** If true, then the options groups that are specified but are not read by
*** some software entity are printed to the algo_out stream (the file 'MoochoAlgo.out'
*** by default). This may help you catch problems associated with spelling the name of
*** an options group improperly and then having its default options used instead of the
*** options that you set. Note that some options groups are only looked for depending
*** on option values from other options groups.
* configuration = mama_jama; *** [default]
* configuration = interior_point;
*** decides which configuration object will be used:
*** mama_jama : standard reduced-space SQP configuration
*** interior_point : configuration for a simple reduced-space interior point method
}
***********************************************************
*** Options for NLPSolverClientInterface
***
*** These are options that are used by every algorithm
*** configuration and they are available to the
*** optimization steps.
***
*** These include basic algorithmic control parameters.
***
*** Note that an algorithm can be interrupted at any time
*** by pressing Ctrl+C.
***
options_group NLPSolverClientInterface {
* max_iter = 1000; *** [default]
*** (+int) Maximum number of SQP iterations allowed.
* max_run_time = 1e+10; *** In minutes [default]
*** (+dbl) Maximum runtime for the SQP algorithm (in minutes).
*** The default is to run forever.
* opt_tol = 1e-6; *** [default]
*** (+dbl) Convergence tolerance for (scaled) KKT linear dependence of gradients.
*** This is usually the hardest error to reduce. The exact definition of this tolerance
*** depends on the algorithms used and may use different scalings (see other options and
*** outputted algorithm description for details). Example values:
*** 0.0 : The algorithm will never converge except in trivial cases.
*** 1e-6 : Only converge when opt_kkt_err is below this value [default].
*** 1e+50 : (big number) Converged when any of the other tolerances are satisfied.
* feas_tol = 1e-6; *** [default]
*** (+dbl) Convergence tolerance for (scaled) feasibility of the equality constraints
*** ||c(x)||inf. The norm of the constraints ||c(x)||inf may be scaled (see other
*** options and the outputted algorithm description). Example values:
*** 0.0 : Never converge the algorithm except in trivial cases.
*** 1e-6 : Only converge when feas_kkt_err is below this value [default].
*** 1e+50 : (big number) Converged when any of the other tolerances are satisfied.
* step_tol = 1e-2; *** [default]
*** (+dbl) Convergence tolerance for (scaled) step size ||x_k(+1)-x_k||inf.
*** This tolerance is usually scaled by x is some way (see other output algorithm
*** description). Example values:
*** 0.0 : Never converge the algorithm except in trivial cases.
*** 1e-2 : Only converge when the max (scaled) step size is below this value [default].
*** 1e+50 : (big number) Converged when any of the other tolerances are satisfied.
* journal_output_level = PRINT_NOTHING; * No output to journal from algorithm
* journal_output_level = PRINT_BASIC_ALGORITHM_INFO; * O(1) information usually
* journal_output_level = PRINT_ALGORITHM_STEPS; * O(iter) output to journal [default]
* journal_output_level = PRINT_ACTIVE_SET; * O(iter*nact) output to journal
* journal_output_level = PRINT_VECTORS; * O(iter*n) output to journal (lots!)
* journal_output_level = PRINT_ITERATION_QUANTITIES; * O(iter*n*m) output to journal (big lots!)
*** [MoochoSolver::journal_outputting == true]
*** This option determines the type and amount of output to the journal_out stream
*** (the file 'MoochoJournal.out' by default) that is generated while the algorithm runs.
*** In general, each increasing value includes the same output from the lower options
*** (i.e. PRINT_VECTORS includes all the output for PRINT_ACTIVE_SET and more). Above,
*** the identifier 'iter' is the number of total rSQP iterations (see max_iter above), 'nact'
*** is the total number of active inequality constraints, 'n' is the total number
*** of NLP variables, and 'm' is the total number of equality constraints. The higher output
*** values are generally used for debugging. For most problems the value
*** PRINT_ALGORITHM_STEPS is usually the most appropriate and will give a great deal
*** of information about the algorithm without generating excessive output.
*** For the fastest possible execution you should set this to PRINT_NOTHING.
* null_space_journal_output_level = DEFAULT; * Set to journal_output_level [default]
* null_space_journal_output_level = PRINT_ACTIVE_SET; * O(iter*nact) output to journal
* null_space_journal_output_level = PRINT_VECTORS; * O(iter*(n-m)) output to journal (lots!)
* null_space_journal_output_level = PRINT_ITERATION_QUANTITIES; * O(iter*(n-m)^2) output to journal (big lots!)
*** [MoochoSolver::journal_outputting == true]
*** This option determines the type and amount of output to the journal_out stream
*** (the file 'MoochoJournal.out' by default) that is generated for quantities in the
*** null space while the algorithm runs. If null_space_journal_output_level is
*** set to DEFAULT then it will default to the value of journal_output_level.
*** If set to some other value then this value overrides journal_output_level
*** for quantities in the null space. For problems where the null space is small but
*** the full space is much larger, setting the value of null_space_journal_output_level higher
*** than journal_output_level can yield significantly more information while not generating
*** too much output or impacting runtime to any great extent.
* journal_print_digits = 6; *** [default]
*** [MoochoSolver::journal_outputting == true]
*** (+int) Number of decimal significant figures to print to journal_out stream.
*** With a higher number more significant figures will be printed. This may be useful
*** for debugging or in seeing the effects of subtle rounding differences. For IEEE double
*** precision, 18 is usually the maximum number of unique decimal significant figures.
* check_results = true; *** (costly?)
* check_results = false; *** [default]
*** If true then all computation that can be reasonably checked will be checked at runtime.
*** When all of the other default testing options are used, this overhead usually will
*** not dominate the cost of the algorithm so if speed is not critical then it is a
*** good idea to turning testing on. If your problem is not solving then you should
*** definitely try turning this on and try to see if it will catch any errors. However,
*** for the fastest possible execution you should set this to 'false'.
* calc_conditioning = true; *** (costly?)
* calc_conditioning = false; *** [default]
*** If true then estimates of the condition numbers of all of the important nonsingular
*** matrices used in the algorithm will be computed and printed. Note that this can be
*** a fairly expensive operation (especially when iterative solvers are being used)
*** so it should be used with care. Warning! see the option calc_matrix_info_null_space_only
*** as it affects the behavior of this option.
* calc_matrix_norms = true; *** (costly?)
* calc_matrix_norms = false; *** [default]
*** If true, then estimates of the matrix norms of all of the important
*** matrices used in the algorithm will be computed and printed. Note that this can be
*** a fairly expensive operation (especially if iterative solvers are being used) so
*** it should be used with care. Warning! see the option calc_matrix_info_null_space_only.
* calc_matrix_info_null_space_only = true; *** (costly?)
* calc_matrix_info_null_space_only = false; *** [default]
*** If true, then the options calc_conditioning and calc_matrix_norms will only
*** apply to quantities in the null space and not the quasi-range space
*** or the full space for which these options will be considered to be false.
}
************************************************************
*** Options for testing the NLP interface
***
*** [MoochoSolver::test_nlp == true]
***
options_group NLPTester {
* print_all = true;
* print_all = false; *** [default]
*** If true, then everything about the NLP will be printed
*** to journal_out (i.e. the file 'MoochoJournal.out').
*** This is useful for initial debugging but not recommended
*** for larger problems.
}
*************************************************************
*** Options for testing the vector spaces from the NLP object
***
*** [MoochoSolver::test_nlp == true]
***
options_group VectorSpaceTester {
* print_all_tests = true;
* print_all_tests = false;
* print_vectors = true;
* print_vectors = false;
* throw_exception = true;
* throw_exception = false;
* num_random_tests = 4; *** [default]
* warning_tol = 1e-14; *** [default]
* error_tol = 1e-10; *** [default]
}
***********************************************************
*** Options for the finite derivative testing for a
*** standard NLP.
***
*** See options_group NLPFirstDerivTester in
*** Moocho.opt.NLPAlgoConfigMamaJama
***
****************************************************************
*** Options for the finite difference derivative tester for a
*** direct sensitivity NLP.
***
*** See options_group NLPDirectTester in
*** Moocho.opt.NLPAlgoConfigMamaJama
***
****************************************************************
*** Options for the BasisSystem tester used to validate the
*** basis of the constraints Jacobian.
***
*** See options_group BasisSystemTester in
*** Moocho.opt.DecompositionSystemStateStepBuilderStd
***
****************************************************************
*** Options for the default BasisSystem factory object for
*** the constraints of the Jacobian used by
*** NLPSerialPreprocessExplJac
***
options_group BasisSystemFactoryStd {
* direct_linear_solver = DENSE; *** Use LAPACK xGETRF()
* direct_linear_solver = MA28; *** Use Harwell MA28 (see options_group DirectSparseSolverMA28)
* direct_linear_solver = MA48; *** Not supported yet
* direct_linear_solver = SUPERLU; *** Use SuperLU (see options_group DirectSparseSolverSuperLU)
*** Direct fortran-compatible linear solver for the basis of the Jacobian.
*** When a general NLP is being solved this selects the sparse linear solver used.
*** If the user specializes the BasisSystem object this option might be meaningless.
}
*****************************************************************
*** Set options for the MA28 solver.
***
*** [BasisSystemFactoryStd::direct_linear_solver == MA28]
***
options_group DirectSparseSolverMA28 {
* estimated_fillin_ratio = 10.0; *** [default]
*** (+dbl) Estimated amount of fillin ( > 1.0 ) for the
*** the sparse LU factorization. If this is too little
*** then more storage will automatically be allocated
*** on the fly (at the cost some wasted computations).
*** This parameter is mostly problem dependent and can
*** be adjusted to a proper size to reduce memory requirements.
*** Example values:
*** 1.0 : No fill-in?
*** 10.0 : LU factors have three times the number of nonzeros
* u = 0.1; *** [default]
*** (+dbl) Control parameter (0 <= u <= 1.0) that us used
*** to balance sparsity and accuracy.
*** Example values:
*** 0.0 : Pivot for sparsity only
*** 0.1 : Balance sparsity and stability
*** 1.0 : Pivot for stability only
* grow = true;
* grow = false; *** [default]
*** See MA28 documentation.
* nsrch = 4; *** [default]
*** (+int) Number of columns that MA28 will search to find
*** pivots to try to reduce fill-in. Warning, setting a large
*** value for 'nsrch' can greatly slow down the initial
*** rSQP iteration.
*** Example values:
*** 0 : No columns are searched
*** 4 : Four columns are searched
*** 1000 : A thousand columns are searched
* lbig = true;
* lbig = false; *** [default]
*** See MA28 documentation.
* print_ma28_outputs = true;
* print_ma28_outputs = false; *** [default]
*** If true, then the values of the MA28 output will
*** be dumped to the journal output stream
*** (if journal_output_level >= PRINT_ALGORITHM_STEPS).
* output_file_name = NONE; *** [default]
*** Gives the file name that MA28 Fortran output is set to (from LP and MP).
*** Example values:
*** NONE : No output file
*** any_other_name : Output from MA28 will be sent to this file in the
*** current directory
}
*** End Moocho.opt.MoochoSolver
1.7.6.1