gemseo / algos / opt

# lib_snopt module¶

## SNOPT optimization library wrapper¶

Classes:

 SNOPT optimization library interface.
class gemseo.algos.opt.lib_snopt.SnOpt[source]

SNOPT optimization library interface.

See OptimizationLibrary.

Constructor.

Generate the library dict, contains the list of algorithms with their characteristics:

• does it handle equality constraints

• does it handle inequality constraints

Attributes:

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'
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'
ROUND_INTS_OPTION = 'round_ints'
STOP_CRIT_NX = 'stop_crit_n_x'
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

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

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