gemseo / algos / opt

lib_snopt module

SNOPT optimization library wrapper.

Classes:

SnOpt()

SNOPT optimization library interface.

class gemseo.algos.opt.lib_snopt.SnOpt

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:

COMPLEX_STEP_METHOD
DESCRIPTION
DIFFERENTIATION_METHODS
EQ_TOLERANCE
FINITE_DIFF_METHOD
F_TOL_ABS
F_TOL_REL
HANDLE_EQ_CONS
HANDLE_INEQ_CONS
INEQ_TOLERANCE
INTERNAL_NAME
LIB
LIB_COMPUTE_GRAD
LS_STEP_NB_MAX
LS_STEP_SIZE_MAX
MAX_DS_SIZE_PRINT
MAX_FUN_EVAL
MAX_ITER
MAX_TIME
MESSAGES_DICT
NORMALIZE_DESIGN_SPACE_OPTION
OPTIONS_DIR
OPTIONS_MAP
PG_TOL
POSITIVE_CONSTRAINTS
PROBLEM_TYPE
REQUIRE_GRAD
ROUND_INTS_OPTION
STOP_CRIT_NX
USER_DEFINED_GRADIENT
USE_DATABASE_OPTION
VERBOSE
WEBSITE
X_TOL_ABS
X_TOL_REL
algorithms	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, ...)	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_progress_bar()	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_iter_observer()	Finalize the iteration observer.
get_optimum_from_database([message, status])	Retrieves the optimum from the database and builds an optimization result object from it.
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.
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'

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

The available algorithms.

cb_opt_constraints_snoptb(mode, nn_con, nn_jac, ne_jac, xn_vect, n_state)

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

its gradient.

Return type

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

cb_opt_objective_snoptb(mode, nn_obj, xn_vect, n_state=0)

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

gradient.

Return type

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

static cb_snopt_dummy_func(mode, nn_con, nn_jac, ne_jac, xn_vect, n_state)

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_adapted_algorithms(problem)

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

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(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