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, ...) Evaluate the constraint functions and their gradient. cb_opt_objective_snoptb(mode, nn_obj, xn_vect) Evaluate the objective function and gradient. cb_snopt_dummy_func(mode, nn_con, nn_jac, ...) Return a dummy output for unconstrained problems. Deactivate the progress bar. driver_has_option(option_key) Check 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) Filter 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) Initialize 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. new_iteration_callback([x_vect]) raises FtolReached If the defined relative or absolute function tolerance is reached.
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

The available algorithms.

cb_opt_constraints_snoptb(mode, nn_con, nn_jac, ne_jac, xn_vect, n_state)[source]

Evaluate the constraint functions and their gradient.

Use the snOpt conventions (from web.stanford.edu/group/SOL/guides/sndoc7.pdf).

Parameters
• mode (int) – A flag that indicates whether the obj, the 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 (int) – The number of non-linear constraints.

• nn_jac (int) – The number of dv involved in non-linear constraint functions.

• ne_jac (int) – The number of non-zero elements in the constraints gradient. If dcstr is 2D, then ne_jac = nn_con*nn_jac.

• xn_vect (numpy.ndarray) – The normalized design vector.

• n_state (int) – An indicator for the first and last call to the 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

The solution status, the evaluation of the constraint function and

Return type

Tuple[int, numpy.ndarray, numpy.ndarray]

cb_opt_objective_snoptb(mode, nn_obj, xn_vect, n_state=0)[source]

Evaluate the objective function and gradient.

Use the snOpt conventions for mode and status (from web.stanford.edu/group/SOL/guides/sndoc7.pdf).

Parameters
• mode (int) – Flag to indicate whether the obj, the gradient or both must be assigned during the present call of the function (0 $$\leq$$ mode $$\leq$$ 2). mode = 2, assign the obj and the known components of the gradient. mode = 1, assign the known components of gradient. obj is ignored. mode = 0, only the obj needs to be assigned; the gradient is ignored.

• nn_obj (int) – The number of design variables.

• xn_vect (numpy.ndarray) – The normalized design vector.

• n_state (int) –

An indicator for the first and last call to the 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.

By default it is set to 0.

Returns

The solution status, the evaluation of the objective function and its

Return type

Tuple[int, numpy.ndarray, numpy.ndarray]

static cb_snopt_dummy_func(mode, nn_con, nn_jac, ne_jac, xn_vect, n_state)[source]

Return a dummy output for unconstrained problems.

Parameters
• mode (int) – A flag that indicates whether the obj, the 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 (int) – The number of non-linear constraints.

• nn_jac (int) – The number of dv involved in non-linear constraint functions.

• ne_jac (int) – The number of non-zero elements in the constraints gradient. If dcstr is 2D, then ne_jac = nn_con*nn_jac.

• xn_vect (numpy.ndarray) – The normalized design vector.

• n_state (int) – An indicator for the first and last call to the 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

A dummy output.

Return type

float

deactivate_progress_bar()

Deactivate the progress bar.

Return type

None

driver_has_option(option_key)

Check if the option key exists.

Parameters

option_key (str) – The name of the option.

Returns

Whether the option is in the grammar.

Return type

bool

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

By default it is set to True.

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)

By default it is set to None.

• options – the options dict for the algorithm

Filter the algorithms capable of solving the problem.

Parameters

problem (Any) – The opt_problem to be solved.

Returns

The list of adapted algorithms names.

Return type

bool

finalize_iter_observer()

Finalize the iteration observer.

Return type

None

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)

By default it is set to None.

• status

Default value = None)

By default it is set to 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 (int) – The maximum number of iterations.

• message (str) – The message to display at the beginning.

Raises

ValueError – If the max_iter is not greater than or equal to one.

Return type

None

init_options_grammar(algo_name)

Initialize the options grammar.

Parameters

algo_name (str) – The name of the algorithm.

Return type

gemseo.core.grammars.json_grammar.JSONGrammar

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(x_vect=None)
Raises
• FtolReached – If the defined relative or absolute function tolerance is reached.

• XtolReached – If the defined relative or absolute x tolerance is reached.

Parameters

x_vect (Optional[numpy.ndarray]) –

By default it is set to None.

Return type

None