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MoochoPack : Framework for Large-Scale Optimization Algorithms
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MoochoPack : Framework for Large-Scale Optimization Algorithms
Version of the Day
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The following is the contents of the file Moocho.opt.DecompositionSystemStateStepBuilderStd which are options that are shared by different specific configuration classes (for example, see MoochoPack::NLPAlgoConfigMamaJama).
*** Moocho.opt.DecompositionSystemStateStepBuilderStd
**********************************************************************************
*** All of these options can be used with any NLPAlgoConfig
*** subclass that uses the standard DecompositionSystemStateStepBuilderStd
*** class.
***
*** 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.
***
***********************************************************
*** Options for IterationPack::Algorithm
***
*** These are options that are used by every IterationPack::Algorithm
*** object that gets created and used.
***
options_group IterationPack_Algorithm {
* interrupt_file_name = ""; *** Does not check for interrupt file [default]
* interrupt_file_name = "interrupt.in"; *** checks for this file in current directory
*** This specifies a file name that is looked for at the end of every
*** step in a running algorithm. If this file exists, it is read for
*** algorithm termination criteria (see the class IterationPack::Algorithm
*** and the option interrupt_file_name). Using a file to interrupt an running
*** algorithm allows the algorithm to be gracefully terminated when run in batch
*** mode or when access to STDOUT or STDIN is not possible.
}
*****************************************************************
*** Options specific for the shared rSQP algorithm builder
*** class DecompositionSystemStateStepBuilderStd.
***
options_group DecompositionSystemStateStepBuilderStd {
*** Variable Reduction range/null space decomposition
* null_space_matrix = AUTO; *** Let the solver decide [default]
* null_space_matrix = EXPLICIT; *** Compute and store D = -inv(C)*N explicitly
* null_space_matrix = IMPLICIT; *** Perform operations implicitly with C, N (requires adjoints)
*** This option is used to determine the type of implementation to use for
*** the variable reduction null space matrix Z = [ -inv(C)*N; I ].
*** AUTO : Let the algorithm decide. The algorithm will take into
*** account the number of degrees of freedom in the problem
*** (n-r), the number of active inequality constraints and
*** other issues when deciding what implementation to use within
*** each iteration. Warning! These automatic tests are only
*** affective when a direct solver for C is used.
*** EXPLICIT : The matrix D = -inv(C)*N is computed and formed explicitly and is used
*** to form Z = [ D; I ].
*** IMPLICIT : The matrix D = -inv(C)*N is not formed explicitly but is
*** instead used implicitly in matrix-vector and other
*** related operations.
* range_space_matrix = AUTO; *** Let the algorithm decide dynamically [default]
* range_space_matrix = COORDINATE; *** Y = [ I; 0 ] (Cheaper computationally)
* range_space_matrix = ORTHOGONAL; *** Y = [ I; -N'*inv(C') ] (more stable)
*** This option is used to determine the selection of the range space matrix Y.
*** AUTO : Let the algorithm decide. The algorithm will take into
*** account the number of degrees of freedom in the problem
*** (n-r) and other issues when deciding what representation to use.
*** Warning! These automatic tests are only affective when a direct
*** solver for C is used.
*** COORDINATE : Use the coordinate decomposition Y = [ I; 0 ]
*** ORTHOGONAL : Use the orthogonal decomposition Y = [ I; N'*inv(C') ].
*** Warning! For general NLPs this option costs approximately
*** O((n-r)^2*r) dense flops per rSQP iteration and will dominate
*** the runtime for large (n-r). In addition, this option
*** assumes that you will be using null_space_matrix=EXPLICIT.
*** Reduced Hessian Approximations
* max_dof_quasi_newton_dense = -1; *** [default]
*** [quasi_newton == AUTO] (+-int) This option is used to
*** determine when the algorithm will switch from quasi_newton=BFGS
*** to quasi_newton=LBFGS and from range_space_matrix=ORTHOGONAL
*** to range_space_matrix=COORDINATE.
*** Example values:
*** -1 : (< 0) Let the solver decide dynamically [default]
*** 0 : Always use limited memory LBFGS and COORDINATE.
*** 500 : Use LBFGS when n-r >= 500 and dense BFGS when n-m < 500
*** Use COORDINATE when (n-m)*r >= 500 and ORTHOGONAL when
*** (n-r)*r <= 500.
*** 1e10: Always use the dense BFGS and the orthogonal decomposition.
}
***************************************************************
*** Options group for CalcFiniteDiffProd class
***
*** These options control how finite differences are computed
*** for testing and for other purposes.
***
options_group CalcFiniteDiffProd {
* fd_method_order = FD_ORDER_ONE; *** Use O(eps) one sided finite differences
* fd_method_order = FD_ORDER_TWO; *** Use O(eps^2) one sided finite differences
* fd_method_order = FD_ORDER_TWO_CENTRAL; *** Use O(eps^2) two sided central finite differences
* fd_method_order = FD_ORDER_TWO_AUTO; *** Uses FD_ORDER_TWO_CENTRAL or FD_ORDER_TWO
* fd_method_order = FD_ORDER_FOUR; *** Use O(eps^4) one sided finite differences
* fd_method_order = FD_ORDER_FOUR_CENTRAL; *** Use O(eps^4) two sided central finite differences
* fd_method_order = FD_ORDER_FOUR_AUTO; *** [default] Use FD_ORDER_FOUR_CENTRAL or FD_ORDER_FOUR
*** Selects the finite differencing method to use. Several different
*** methods of different orders are available. For more accuracy use a higher order
*** method, for faster execution time use a lower order method.
* fd_step_select = FD_STEP_ABSOLUTE; *** [default] Use absolute step size fd_step_size
* fd_step_select = FD_STEP_RELATIVE; *** Use relative step size fd_step_size * ||x||inf
*** Determines how the actual finite difference step size that is used is selected.
*** FD_STEP_ABSOLUTE : The actual step size used is taken from fd_step_size or
*** is determined by the implementation if fd_step_size < 0.0.
*** Taking an absolute step size can result in inaccurate gradients
*** for badly scaled NLPs.
*** FD_STEP_RELATIVE : The actual step size used is taken from fd_step_size or
*** is determined by the implementation if fd_step_size < 0.0
*** and then multiplied by ||x||inf. Taking a relative step
*** will not always result in accurate gradients and the user
*** may have to play with fd_step_size some.
* fd_step_size = -1.0; *** [default] Let the implementation decide
*** Determines what finite difference step size to use. If fd_step_select=FD_STEP_ABSOLUTE
*** then this is the absolute step size that is used. If fd_step_select=FD_STEP_RELATIVE
*** the actual step size used in fd_step_size * ||x||inf.
*** Some common values are:
*** < 0.0 : let the implementation decide.
*** 1e-8 : Optimal step size for FD_ORDER_ONE for IEEE double and perfect scaling?
*** 1e-5 : Optimal step size for FD_ORDER_TWOx for IEEE double and perfect scaling?
*** 1e-3 : Optimal step size for FD_ORDER_FOURx for IEEE double and perfect scaling?
* fd_step_size_min = -1.0; *** [default] Let the implementation decide.
*** Determines the minimum step size that will be taken to compute the finite differences.
*** This option is used to forbid the computation of a finite difference with a very small
*** step size as required by the variable bounds. Computing finite difference derivatives
*** for such small step sizes generally result in a lot of roundoff error. If
*** fd_step_size_min < 0.0, then the implementation will pick a default value that is
*** smaller than the default value for fd_step_size.
* fd_step_size_f = -1.0; *** [default] Let the implementation decide
*** Determines what finite difference step size to use for the objective function
*** f(x). If fd_step_size_f < 0.0, then the selected value for fd_step_size will be
*** used (see the options fd_step_size and fd_step_select). This option allows
*** fine-tuning of the finite difference computations.
* fd_step_size_c = -1.0; *** [default] Let the implementation decide
*** Determines what finite difference step size to use for the equality constraints
*** c(x). If fd_step_size_c < 0.0, then the selected value for fd_step_size will be
*** used (see the options fd_step_size and fd_step_select). This option allows
*** fine-tuning of the finite difference computations.
* fd_step_size_h = -1.0; *** [default] Let the implementation decide
*** Determines what finite difference step size to use for the inequality constraints
*** h(x). If fd_step_size_h < 0.0, then the selected value for fd_step_size will be
*** used (see the options fd_step_size and fd_step_select). This option allows
*** fine-tuning of the finite difference computations.
}
***************************************************************
*** Options for EvalNewPoint for NLPFirstOrder.
*** See options_group NLPFirstDerivTester
***
options_group EvalNewPointStd {
* fd_deriv_testing = FD_DEFAULT; *** [default] Test if check_results==true (see above)
* fd_deriv_testing = FD_TEST; *** Always test
* fd_deriv_testing = FD_NO_TEST; *** never test
*** Determines if the derivatives of the NLP returned from the NLPFirstOrder interface
*** are correct using finite differences (see the options_group NLPFirstDerivTester).
*** Valid options include:
*** FD_DEFAULT : Perform the finite difference tests if check_results==true.
*** FD_TEST : Always test, regardless of the value of check_results.
*** FD_NO_TEST : Never test, regardless of the value of check_results.
* decomp_sys_testing = DST_DEFAULT; *** [default] Test if check_results==true (see above)
* decomp_sys_testing = DST_TEST; *** Always test
* decomp_sys_testing = DST_NO_TEST; *** never test
*** Determines if the range/null decomposition matrices from DecompositionSystem are
*** tested or not (see the options_group DecompositionSystemTester).
*** Valid options include:
*** FD_DEFAULT : Perform the tests if check_results==true.
*** FD_TEST : Always test, regardless of the value of check_results.
*** FD_NO_TEST : Never test, regardless of the value of check_results.
* decomp_sys_testing_print_level = DSPL_USE_GLOBAL; *** [default] Use the value in journal_print_level (see above).
* decomp_sys_testing_print_level = DSPL_LEAVE_DEFAULT; *** Leave whatever setting in already in use.
*** This option allows the user to determine how the testing print level is determined.
*** Valid options include:
*** DSLP_USE_GLOBAL : The value of journal_print_level will be used to determine reasonable values for
*** print_tests and dump_all.
*** DSLP_LEAVE_DEFAULT : Whatever values are currently set for DecompositionSystemTester::print_tests and
*** DecompositionSystemTester::dump_all will be used (see the options_group
*** DecompositionSystemTester).
}
***************************************************************
*** Options for determining if variable bounds
*** xL <= x <= xU are violated by
*** more than an acceptable tolerance.
***
options_group VariableBoundsTester {
* warning_tol = 1e-10; *** [default]
* error_tol = 1e-5; *** [default]
}
***********************************************************
*** Options for the finite difference testing of derivatives for a
*** standard NLP.
***
options_group NLPFirstDerivTester {
* fd_testing_method = FD_COMPUTE_ALL; *** Compute all of the derivatives (O(m))
* fd_testing_method = FD_DIRECTIONAL; *** [default] Only compute along random directions (O(1))
* num_fd_directions = 1; *** [fd_testing_method == DIRECTIONAL]
* num_fd_directions = -1; *** [fd_testing_method == DIRECTIONAL] Use single direction y=1.0
* warning_tol = 1e-8; *** [default]
* warning_tol = 0.0; *** Show me all comparisons.
* error_tol = 1e-3; *** [default]
}
***************************************************************
*** Options for EvalNewPoint for a "Tailored Approach" NLP.
*** See options_group NLPDirectTester
***
options_group EvalNewPointTailoredApproach {
* fd_deriv_testing = FD_DEFAULT; *** [default] Test if check_results==true (see above)
* fd_deriv_testing = FD_TEST; *** Always test
* fd_deriv_testing = FD_NO_TEST; *** never test
}
****************************************************************
*** Options for the finite difference derivative tester for a
*** direct sensitivity NLP.
***
options_group NLPDirectTester {
* Gf_testing_method = FD_COMPUTE_ALL; *** Compute all of the derivatives (O(n))
* Gf_testing_method = FD_DIRECTIONAL; *** [default] Only compute along random directions (O(1))
* Gf_warning_tol = 1e-10;
* Gf_error_tol = 1e-5;
* Gc_testing_method = FD_COMPUTE_ALL; *** Compute all of the derivatives (O(n-m))
* Gc_testing_method = FD_DIRECTIONAL; *** [default] Only compute along random directions (O(1))
* Gc_warning_tol = 1e-10;
* Gc_error_tol = 1e-5;
* num_fd_directions = 1; *** [testing_method == DIRECTIONAL]
* dump_all = true;
* dump_all = false; *** [default]
}
****************************************************************
*** Options for the BasisSystem tester used to validate the
*** basis of the constraints Jacobian.
***
options_group BasisSystemTester {
* print_tests = PRINT_NONE; *** [default]
* print_tests = PRINT_BASIC;
* print_tests = PRINT_MORE;
* print_tests = PRINT_ALL;
* dump_all = true;
* dump_all = false; *** [default]
* num_random_tests = 1; *** (+int) Number of sets of random tests to perform
* warning_tol = 1e-15; *** (+dbl) Warning tolerance
* error_tol = 1e-12; *** (+dbl) Error tolerance
}
****************************************************************
*** Options for the DecompositionSystem tester used to validate
*** range/null decomposition matrices (NLPFirstOrder only).
***
options_group DecompositionSystemTester {
* print_tests = PRINT_NONE; *** [default]
* print_tests = PRINT_BASIC;
* print_tests = PRINT_MORE;
* print_tests = PRINT_ALL;
* dump_all = true; *** (costly)
* dump_all = false; *** [default]
* num_random_tests = 1; *** (+int) Number of sets of random test to perform
* mult_warning_tol = 1e-14; *** (+dbl) Warning tolerance for checking matrix-vector multiplication
* mult_error_tol = 1e-8; *** (+dbl) Error tolerance for checking matrix-vector multiplication
* solve_warning_tol = 1e-14; *** (+dbl) Warning tolerance for checking linear solves
* solve_error_tol = 1e-8; *** (+dbl) Error tolerance for checking linear solves
}
*** End Moocho.opt.DecompositionSystemStateStepBuilderStd
1.7.6.1