lib_snopt module¶
SNOPT optimization library wrapper¶
Classes:
|
SNOPT optimization library interface. |
- class gemseo.algos.opt.lib_snopt.SnOpt[source]¶
Bases:
gemseo.algos.opt.opt_lib.OptimizationLibrary
SNOPT optimization library interface.
See OptimizationLibrary.
Constructor.
Generate the library dict, contains the list of algorithms with their characteristics:
does it require gradient
does it handle equality constraints
does it handle inequality constraints
Attributes:
Return the available algorithms.
Methods:
algorithm_handles_eqcstr
(algo_name)Returns True if the algorithms handles equality constraints.
algorithm_handles_ineqcstr
(algo_name)Returns True if the algorithms handles inequality constraints.
cb_opt_constraints_snoptb
(mode, nn_con, …)Constraints function + constraints gradient of the optimizer for snOpt (from web.stanford.edu/group/SOL/guides/sndoc7.pdf)
cb_opt_objective_snoptb
(mode, nn_obj, xn_vect)Objective function + objective gradient of the optimizer for snOpt (from web.stanford.edu/group/SOL/guides/sndoc7.pdf)
cb_snopt_dummy_func
(mode, nn_con, nn_jac, …)Dummy function required for unconstrained problem.
driver_has_option
(option_key)Checks if the option key exists.
ensure_bounds
(orig_func[, normalize])Project the design vector onto the design space before execution.
execute
(problem[, algo_name])Executes the driver.
filter_adapted_algorithms
(problem)Filters the algorithms capable of solving the problem.
Finalize the iteration observer.
get_optimum_from_database
([message, status])Retrieves the optimum from the database and builds an optimization result object from it.
Transforms the problem constraints into their opposite sign counterpart if the algorithm requires positive constraints.
get_x0_and_bounds_vects
(normalize_ds)Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.
init_iter_observer
(max_iter, message)Initialize the iteration observer.
init_options_grammar
(algo_name)Initializes the options grammar.
is_algo_requires_grad
(algo_name)Returns True if the algorithm requires a gradient evaluation.
is_algo_requires_positive_cstr
(algo_name)Returns True if the algorithm requires positive constraints False otherwise.
is_algorithm_suited
(algo_dict, problem)Checks if the algorithm is suited to the problem according to its algo dict.
Callback called at each new iteration, ie every time a design vector that is not already in the database is proposed by the optimizer.
- COMPLEX_STEP_METHOD = 'complex_step'¶
- DESCRIPTION = 'description'¶
- DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']¶
- EQ_TOLERANCE = 'eq_tolerance'¶
- FINITE_DIFF_METHOD = 'finite_differences'¶
- F_TOL_ABS = 'ftol_abs'¶
- F_TOL_REL = 'ftol_rel'¶
- HANDLE_EQ_CONS = 'handle_equality_constraints'¶
- HANDLE_INEQ_CONS = 'handle_inequality_constraints'¶
- INEQ_TOLERANCE = 'ineq_tolerance'¶
- INTERNAL_NAME = 'internal_algo_name'¶
- LIB = 'lib'¶
- LIB_COMPUTE_GRAD = False¶
- LS_STEP_NB_MAX = 'max_ls_step_nb'¶
- LS_STEP_SIZE_MAX = 'max_ls_step_size'¶
- MAX_DS_SIZE_PRINT = 40¶
- MAX_FUN_EVAL = 'max_fun_eval'¶
- MAX_ITER = 'max_iter'¶
- MAX_TIME = 'max_time'¶
- MESSAGES_DICT = {1: 'optimality conditions satisfied', 2: 'feasible point found', 3: 'requested accuracy could not be achieved', 11: 'infeasible linear constraints', 12: 'infeasible linear equalities', 13: 'nonlinear infeasibilities minimized', 14: 'infeasibilities minimized', 21: 'unbounded objective', 22: 'constraint violation limit reached', 31: 'iteration limit reached', 32: 'major iteration limit reached', 33: 'the superbasics limit is too small', 41: 'current point cannot be improved ', 42: 'singular basis', 43: 'cannot satisfy the general constraints', 44: 'ill-conditioned null-space basis', 51: 'incorrect objective derivatives', 52: 'incorrect constraint derivatives', 61: 'undefined function at the first feasible point', 62: 'undefined function at the initial point', 63: 'unable to proceed into undefined region', 72: 'terminated during constraint evaluation', 73: 'terminated during objective evaluation', 74: 'terminated from monitor routine', 81: 'work arrays must have at least 500 elements', 82: 'not enough character storage', 83: 'not enough integer storage', 84: 'not enough real storage', 91: 'invalid input argument', 92: 'basis file dimensions do not match this problem', 141: 'wrong number of basic variables', 142: 'error in basis package'}¶
- NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'¶
- OPTIONS_DIR = 'options'¶
- OPTIONS_MAP = {'max_iter': 'Iteration_limit'}¶
- PG_TOL = 'pg_tol'¶
- POSITIVE_CONSTRAINTS = 'positive_constraints'¶
- PROBLEM_TYPE = 'problem_type'¶
- REQUIRE_GRAD = 'require_grad'¶
- ROUND_INTS_OPTION = 'round_ints'¶
- STOP_CRIT_NX = 'stop_crit_n_x'¶
- USER_DEFINED_GRADIENT = 'user'¶
- USE_DATABASE_OPTION = 'use_database'¶
- VERBOSE = 'verbose'¶
- WEBSITE = 'website'¶
- X_TOL_ABS = 'xtol_abs'¶
- X_TOL_REL = 'xtol_rel'¶
- algorithm_handles_eqcstr(algo_name)¶
Returns True if the algorithms handles equality constraints.
- Parameters
algo_name – the name of the algorithm
- Returns
True or False
- algorithm_handles_ineqcstr(algo_name)¶
Returns True if the algorithms handles inequality constraints.
- Parameters
algo_name – the name of the algorithm
- Returns
True or False
- property algorithms¶
Return the available algorithms.
- cb_opt_constraints_snoptb(mode, nn_con, nn_jac, ne_jac, xn_vect, n_state)[source]¶
Constraints function + constraints gradient of the optimizer for snOpt (from web.stanford.edu/group/SOL/guides/sndoc7.pdf)
- Parameters
mode – indicates whether obj or gradient or both must be assigned during the present call of function (0 ≤ mode ≤ 2). mode = 2, assign obj and the known components of gradient mode = 1, assign the known components of gradient. obj is ignored. mode = 0, only obj need be assigned; gradient is ignored.
nn_con – number of non-linear constraints
nn_jac – number of dv involved in non-linear constraint functions
ne_jac – number of non-zero elements in constraints gradient. dcstr is 2D ==> ne_jac = nn_con*nn_jac
xn_vect – normalized design vector
n_state – indicator for first and last call to current function n_state = 0: NTR n_state = 1: first call to driver.cb_opt_objective_snoptb n_state > 1, snOptB is calling subroutine for the last time and: n_state = 2 and the current x is optimal n_state = 3, the problem appears to be infeasible n_state = 4, the problem appears to be unbounded; n_state = 5, an iterations limit was reached.
- Returns
status: may be used to indicate that you are unable to evaluate cstr or its gradients at the current x. (For example, the problem functions may not be defined there). cstr: constraints function (except perhaps if mode = 1) dcstr constraints jacobian array (except perhaps if mode = 0)
- Return type
integer, np array, np array
- cb_opt_objective_snoptb(mode, nn_obj, xn_vect, n_state=0)[source]¶
Objective function + objective gradient of the optimizer for snOpt (from web.stanford.edu/group/SOL/guides/sndoc7.pdf)
- Parameters
mode – indicates whether obj or gradient or both must be assigned during the present call of function (0 \(\leq\) mode \(\leq\) 2). mode = 2, assign obj and the known components of gradient mode = 1, assign the known components of gradient. obj is ignored. mode = 0, only obj need be assigned; gradient is ignored.
nn_obj – number of design variables
xn_vect – normalized design vector
n_state – indicator for first and last call to current function n_state = 0: NTR n_state = 1: first call to driver.cb_opt_objective_snoptb n_state > 1, snOptB is calling subroutine for the last time and: n_state = 2 and the current x is optimal n_state = 3, the problem appears to be infeasible n_state = 4, the problem appears to be unbounded; n_state = 5, an iterations limit was reached. (Default value = 0)
- Returns
status: may be used to indicate that you are unable to evaluate obj_f or its gradients at the current x. (For example, the problem functions may not be defined there). obj_f, objective function (except perhaps if mode = 1) objective jacobian array (except perhaps if mode = 0)
- Return type
integer, np array, np array
- static cb_snopt_dummy_func(mode, nn_con, nn_jac, ne_jac, xn_vect, n_state)[source]¶
Dummy function required for unconstrained problem.
- Parameters
mode – param nn_con:
nn_jac – param ne_jac:
xn_vect – param n_state:
nn_con –
ne_jac –
n_state –
- driver_has_option(option_key)¶
Checks if the option key exists.
- Parameters
option_key – the name of the option
- Returns
True if the option is in the grammar
- ensure_bounds(orig_func, normalize=True)¶
Project the design vector onto the design space before execution.
- Parameters
orig_func – the original function
normalize – if True, use the normalized design space
- Returns
the wrapped function
- execute(problem, algo_name=None, **options)¶
Executes the driver.
- Parameters
problem – the problem to be solved
algo_name – name of the algorithm if None, use self.algo_name which may have been set by the factory (Default value = None)
options – the options dict for the algorithm
- filter_adapted_algorithms(problem)¶
Filters the algorithms capable of solving the problem.
- Parameters
problem – the opt_problem to be solved
- Returns
the list of adapted algorithms names
- finalize_iter_observer()¶
Finalize the iteration observer.
- get_optimum_from_database(message=None, status=None)¶
Retrieves the optimum from the database and builds an optimization result object from it.
- Parameters
message – Default value = None)
status – Default value = None)
- get_right_sign_constraints()¶
Transforms the problem constraints into their opposite sign counterpart if the algorithm requires positive constraints.
- get_x0_and_bounds_vects(normalize_ds)¶
Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.
- Parameters
normalize_ds – if True, normalizes all input vars that are not integers, according to design space normalization policy
- Returns
x, lower bounds, upper bounds
- init_iter_observer(max_iter, message)¶
Initialize the iteration observer.
It will handle the stopping criterion and the logging of the progress bar.
- Parameters
max_iter – maximum number of calls
message – message to display at the beginning
- init_options_grammar(algo_name)¶
Initializes the options grammar.
- Parameters
algo_name – name of the algorithm
- is_algo_requires_grad(algo_name)¶
Returns True if the algorithm requires a gradient evaluation.
- Parameters
algo_name – name of the algorithm
- is_algo_requires_positive_cstr(algo_name)¶
Returns True if the algorithm requires positive constraints False otherwise.
- Parameters
algo_name – the name of the algorithm
- Returns
True if constraints must be positive
- Return type
logical
- static is_algorithm_suited(algo_dict, problem)¶
Checks if the algorithm is suited to the problem according to its algo dict.
- Parameters
algo_dict – the algorithm characteristics
problem – the opt_problem to be solved
- new_iteration_callback()¶
Callback called at each new iteration, ie every time a design vector that is not already in the database is proposed by the optimizer.
Iterates the progress bar, implements the stop criteria