PyomoNLP

(class from pyomo.contrib.pynumero.interfaces.pyomo_nlp)

class pyomo.contrib.pynumero.interfaces.pyomo_nlp.PyomoNLP(pyomo_model, nl_file_options=None)

Bases: AslNLP

__init__(pyomo_model, nl_file_options=None)

Pyomo nonlinear program interface

Parameters:

pyomo_model (pyomo.environ.ConcreteModel) – Pyomo concrete model

Methods

__init__(pyomo_model[, nl_file_options])	Pyomo nonlinear program interface
constraint_names()	Return an ordered list of the Pyomo constraint names in the order corresponding to internal constraint order
constraints_lb()	Returns vector of lower bounds for the constraints
constraints_ub()	Returns vector of upper bounds for the constraints
create_new_vector(vector_type)	Creates a vector of the appropriate length and structure as requested
equality_constraint_names()	Return an ordered list of the Pyomo ConData names in the order corresponding to the equality constraints.
evaluate_constraints([out])	Returns the values for the constraints evaluated at the values given for the primal variales in set_primals
evaluate_eq_constraints([out])	Returns the values for the equality constraints evaluated at the values given for the primal variales in set_primals
evaluate_grad_objective([out])	Returns gradient of the objective function evaluated at the values given for the primal variables in set_primals
evaluate_hessian_lag([out])	Return the Hessian of the Lagrangian function evaluated at the values given for the primal variables in set_primals and the dual variables in set_duals
evaluate_ineq_constraints([out])	Returns the values of the inequality constraints evaluated at the values given for the primal variables in set_primals
evaluate_jacobian([out])	Returns the Jacobian of the constraints evaluated at the values given for the primal variables in set_primals
evaluate_jacobian_eq([out])	Returns the Jacobian of the equality constraints evaluated at the values given for the primal variables in set_primals
evaluate_jacobian_ineq([out])	Returns the Jacobian of the inequality constraints evaluated at the values given for the primal variables in set_primals
evaluate_objective()	Returns value of objective function evaluated at the values given for the primal variables in set_primals
extract_submatrix_hessian_lag(...)	Return the submatrix of the hessian of the lagrangian that corresponds to the list of Pyomo variables provided
extract_submatrix_jacobian(pyomo_variables, ...)	Return the submatrix of the jacobian that corresponds to the list of Pyomo variables and list of Pyomo constraints provided
extract_subvector_constraints(pyomo_constraints)	Return the values of the constraints corresponding to the list of Pyomo constraints provided
extract_subvector_grad_objective(pyomo_variables)	Compute the gradient of the objective and return the entries corresponding to the given Pyomo variables
get_constraint_indices(pyomo_constraints)	Return the list of indices for the constraints corresponding to the list of Pyomo constraints provided
get_constraints_scaling()	Return the desired scaling factors to use for the for the constraints.
get_duals()	Get a copy of the values of the dual variables as provided in set_duals.
get_duals_eq()	Get a copy of the values of the dual variables of the equality constraints as provided in set_duals_eq.
get_duals_ineq()	Get a copy of the values of the dual variables of the inequality constraints as provided in set_duals_eq.
get_eq_constraints_scaling()	Return the desired scaling factors to use for the for the equality constraints.
get_equality_constraint_indices(constraints)	Return the list of equality indices for the constraints corresponding to the list of Pyomo constraints provided.
get_ineq_constraints_scaling()	Return the desired scaling factors to use for the for the inequality constraints.
get_inequality_constraint_indices(constraints)	Return the list of inequality indices for the constraints corresponding to the list of Pyomo constraints provided.
get_obj_factor()	Get the value of the objective function factor as set by set_obj_factor.
get_obj_scaling()	Return the desired scaling factor to use for the for the objective function.
get_primal_indices(pyomo_variables)	Return the list of indices for the primals corresponding to the list of Pyomo variables provided
get_primals()	Get a copy of the values of the primal variables as provided in set_primals.
get_primals_scaling()	Return the desired scaling factors to use for the for the primals.
get_pyomo_constraints()	Return an ordered list of the Pyomo ConData objects in the order corresponding to the primals
get_pyomo_equality_constraints()	Return an ordered list of the Pyomo ConData objects in the order corresponding to the equality constraints.
get_pyomo_inequality_constraints()	Return an ordered list of the Pyomo ConData objects in the order corresponding to the inequality constraints.
get_pyomo_objective()	Return an instance of the active objective function on the Pyomo model.
get_pyomo_variables()	Return an ordered list of the Pyomo VarData objects in the order corresponding to the primals
ineq_lb()	Returns vector of lower bounds for inequality constraints
ineq_ub()	Returns vector of upper bounds for inequality constraints
inequality_constraint_names()	Return an ordered list of the Pyomo ConData names in the order corresponding to the inequality constraints.
init_duals()	Returns vector with initial values for the dual variables of the constraints
init_duals_eq()	Returns vector with initial values for the dual variables of the equality constraints
init_duals_ineq()	Returns vector with initial values for the dual variables of the inequality constraints
init_primals()	Returns vector with initial values for the primal variables
load_state_into_pyomo([bound_multipliers])
n_constraints()	Returns number of constraints
n_eq_constraints()	Returns number of equality constraints
n_ineq_constraints()	Returns number of inequality constraints
n_primals()	Returns number of primal variables
nnz_hessian_lag()	Returns number of nonzero values in hessian of the lagrangian function
nnz_jacobian()	Returns number of nonzero values in jacobian of equality constraints
nnz_jacobian_eq()	Returns number of nonzero values in jacobian of equality constraints
nnz_jacobian_ineq()	Returns number of nonzero values in jacobian of inequality constraints
primals_lb()	Returns vector of lower bounds for the primal variables
primals_names()	Return an ordered list of the Pyomo variable names in the order corresponding to the primals
primals_ub()	Returns vector of upper bounds for the primal variables
pyomo_model()	Return optimization model
report_solver_status(status_code, status_message)	Report the solver status to NLP class using the values for the primals and duals defined in the set methods
set_duals(duals)	Set the value of the dual variables for the constraints to be used in calls to the evaluation methods (hessian_lag)
set_duals_eq(duals_eq)	Set the value of the dual variables for the equality constraints to be used in calls to the evaluation methods (hessian_lag)
set_duals_ineq(duals_ineq)	Set the value of the dual variables for the inequality constraints to be used in calls to the evaluation methods (hessian_lag)
set_obj_factor(obj_factor)	Set the value of the objective function factor to be used in calls to the evaluation of the hessian of the lagrangian (evaluate_hessian_lag)
set_primals(primals)	Set the value of the primal variables to be used in calls to the evaluation methods
variable_names()	DEPRECATED.

Attributes

symbol_map

Member Documentation

constraint_names()

Return an ordered list of the Pyomo constraint names in the order corresponding to internal constraint order

constraints_lb()

Returns vector of lower bounds for the constraints

Return type:

vector-like

constraints_ub()

Returns vector of upper bounds for the constraints

Return type:

vector-like

create_new_vector(vector_type)

Creates a vector of the appropriate length and structure as requested

Parameters:

vector_type ({'primals', 'constraints', 'eq_constraints', 'ineq_constraints',) – ‘duals’, ‘duals_eq’, ‘duals_ineq’} String identifying the appropriate vector to create.

Return type:

numpy.ndarray

equality_constraint_names()

Return an ordered list of the Pyomo ConData names in the order corresponding to the equality constraints.

evaluate_constraints(out=None)

Returns the values for the constraints evaluated at the values given for the primal variales in set_primals

Parameters:

out (array_like, optional) – Output array. Its type is preserved and it must be of the right shape to hold the output.

Return type:

vector_like

evaluate_eq_constraints(out=None)

Returns the values for the equality constraints evaluated at the values given for the primal variales in set_primals

Parameters:

out (array_like, optional) – Output array. Its type is preserved and it must be of the right shape to hold the output.

Return type:

vector_like

evaluate_grad_objective(out=None)

Returns gradient of the objective function evaluated at the values given for the primal variables in set_primals

Parameters:

out (vector_like, optional) – Output vector. Its type is preserved and it must be of the right shape to hold the output.

Return type:

vector_like

evaluate_hessian_lag(out=None)

Return the Hessian of the Lagrangian function evaluated at the values given for the primal variables in set_primals and the dual variables in set_duals

Parameters:

out (matrix_like (e.g., coo_matrix), optional) – Output matrix with the structure of the hessian already defined. Optional

Return type:

matrix_like

evaluate_ineq_constraints(out=None)

Returns the values of the inequality constraints evaluated at the values given for the primal variables in set_primals

Parameters:

out (array_like, optional) – Output array. Its type is preserved and it must be of the right shape to hold the output.

Return type:

vector_like

evaluate_jacobian(out=None)

Returns the Jacobian of the constraints evaluated at the values given for the primal variables in set_primals

Parameters:

out (matrix_like (e.g., coo_matrix), optional) – Output matrix with the structure of the jacobian already defined.

Return type:

matrix_like

evaluate_jacobian_eq(out=None)

Returns the Jacobian of the equality constraints evaluated at the values given for the primal variables in set_primals

Parameters:

out (matrix_like (e.g., coo_matrix), optional) – Output matrix with the structure of the jacobian already defined.

Return type:

matrix_like

evaluate_jacobian_ineq(out=None)

Returns the Jacobian of the inequality constraints evaluated at the values given for the primal variables in set_primals

Parameters:

out (matrix_like (e.g., coo_matrix), optional) – Output matrix with the structure of the jacobian already defined.

Return type:

matrix_like

evaluate_objective()

Returns value of objective function evaluated at the values given for the primal variables in set_primals

Return type:

float

extract_submatrix_hessian_lag(pyomo_variables_rows, pyomo_variables_cols)

Return the submatrix of the hessian of the lagrangian that corresponds to the list of Pyomo variables provided

Parameters:

• pyomo_variables_rows (list of Pyomo Var or VarData objects) – List of Pyomo Var or VarData objects corresponding to the desired rows

• pyomo_variables_cols (list of Pyomo Var or VarData objects) – List of Pyomo Var or VarData objects corresponding to the desired columns

extract_submatrix_jacobian(pyomo_variables, pyomo_constraints)

Return the submatrix of the jacobian that corresponds to the list of Pyomo variables and list of Pyomo constraints provided

Parameters:

• pyomo_variables (list of Pyomo Var or VarData objects)

• pyomo_constraints (list of Pyomo Constraint or ConstraintData objects)

extract_subvector_constraints(pyomo_constraints)

Return the values of the constraints corresponding to the list of Pyomo constraints provided

Parameters:

pyomo_constraints (list of Pyomo Constraint or ConstraintData objects)

extract_subvector_grad_objective(pyomo_variables)

Compute the gradient of the objective and return the entries corresponding to the given Pyomo variables

Parameters:

pyomo_variables (list of Pyomo Var or VarData objects)

get_constraint_indices(pyomo_constraints)

Return the list of indices for the constraints corresponding to the list of Pyomo constraints provided

Parameters:

pyomo_constraints (list of Pyomo Constraint or ConstraintData objects)

get_constraints_scaling()

Return the desired scaling factors to use for the for the constraints. None indicates no scaling. This indicates potential scaling for the model, but the evaluation methods should return unscaled values

Return type:

array-like or None

get_duals()

Get a copy of the values of the dual variables as provided in set_duals. These are the values that will be used in calls to the evaluation methods.

get_duals_eq()

Get a copy of the values of the dual variables of the equality constraints as provided in set_duals_eq. These are the values that will be used in calls to the evaluation methods.

get_duals_ineq()

Get a copy of the values of the dual variables of the inequality constraints as provided in set_duals_eq. These are the values that will be used in calls to the evaluation methods.

get_eq_constraints_scaling()

Return the desired scaling factors to use for the for the equality constraints. None indicates no scaling. This indicates potential scaling for the model, but the evaluation methods should return unscaled values

Return type:

array-like or None

get_equality_constraint_indices(constraints)

Return the list of equality indices for the constraints corresponding to the list of Pyomo constraints provided.

Parameters:

constraints (list of Pyomo Constraints or ConstraintData objects)

get_ineq_constraints_scaling()

Return the desired scaling factors to use for the for the inequality constraints. None indicates no scaling. This indicates potential scaling for the model, but the evaluation methods should return unscaled values

Return type:

array-like or None

get_inequality_constraint_indices(constraints)

Return the list of inequality indices for the constraints corresponding to the list of Pyomo constraints provided.

Parameters:

constraints (list of Pyomo Constraints or ConstraintData objects)

get_obj_factor()

Get the value of the objective function factor as set by set_obj_factor. This is the value that will be used in calls to the evaluation of the hessian of the lagrangian (evaluate_hessian_lag)

get_obj_scaling()

Return the desired scaling factor to use for the for the objective function. None indicates no scaling. This indicates potential scaling for the model, but the evaluation methods should return unscaled values

Return type:

float or None

get_primal_indices(pyomo_variables)

Return the list of indices for the primals corresponding to the list of Pyomo variables provided

Parameters:

pyomo_variables (list of Pyomo Var or VarData objects)

get_primals()

Get a copy of the values of the primal variables as provided in set_primals. These are the values that will be used in calls to the evaluation methods

get_primals_scaling()

Return the desired scaling factors to use for the for the primals. None indicates no scaling. This indicates potential scaling for the model, but the evaluation methods should return unscaled values

Return type:

array-like or None

get_pyomo_constraints()

Return an ordered list of the Pyomo ConData objects in the order corresponding to the primals

get_pyomo_equality_constraints()

Return an ordered list of the Pyomo ConData objects in the order corresponding to the equality constraints.

get_pyomo_inequality_constraints()

Return an ordered list of the Pyomo ConData objects in the order corresponding to the inequality constraints.

get_pyomo_objective()

Return an instance of the active objective function on the Pyomo model. (there can be only one)

get_pyomo_variables()

Return an ordered list of the Pyomo VarData objects in the order corresponding to the primals

ineq_lb()

Returns vector of lower bounds for inequality constraints

Return type:

vector-like

ineq_ub()

Returns vector of upper bounds for inequality constraints

Return type:

vector-like

inequality_constraint_names()

Return an ordered list of the Pyomo ConData names in the order corresponding to the inequality constraints.

init_duals()

Returns vector with initial values for the dual variables of the constraints

init_duals_eq()

Returns vector with initial values for the dual variables of the equality constraints

init_duals_ineq()

Returns vector with initial values for the dual variables of the inequality constraints

init_primals()

Returns vector with initial values for the primal variables

n_constraints()

Returns number of constraints

n_eq_constraints()

Returns number of equality constraints

n_ineq_constraints()

Returns number of inequality constraints

n_primals()

Returns number of primal variables

nnz_hessian_lag()

Returns number of nonzero values in hessian of the lagrangian function

nnz_jacobian()

Returns number of nonzero values in jacobian of equality constraints

nnz_jacobian_eq()

Returns number of nonzero values in jacobian of equality constraints

nnz_jacobian_ineq()

Returns number of nonzero values in jacobian of inequality constraints

primals_lb()

Returns vector of lower bounds for the primal variables

Return type:

vector-like

primals_names()

Return an ordered list of the Pyomo variable names in the order corresponding to the primals

primals_ub()

Returns vector of upper bounds for the primal variables

Return type:

vector-like

pyomo_model()

Return optimization model

report_solver_status(status_code, status_message)

Report the solver status to NLP class using the values for the primals and duals defined in the set methods

set_duals(duals)

Set the value of the dual variables for the constraints to be used in calls to the evaluation methods (hessian_lag)

Parameters:

duals (vector_like) – Vector with the values of dual variables for the equality constraints

set_duals_eq(duals_eq)

Set the value of the dual variables for the equality constraints to be used in calls to the evaluation methods (hessian_lag)

Parameters:

duals_eq (vector_like) – Vector with the values of dual variables for the equality constraints

set_duals_ineq(duals_ineq)

Set the value of the dual variables for the inequality constraints to be used in calls to the evaluation methods (hessian_lag)

Parameters:

duals_ineq (vector_like) – Vector with the values of dual variables for the inequality constraints

set_obj_factor(obj_factor)

Set the value of the objective function factor to be used in calls to the evaluation of the hessian of the lagrangian (evaluate_hessian_lag)

Parameters:

obj_factor (float) – Value of the objective function factor used in the evaluation of the hessian of the lagrangian

set_primals(primals)

Set the value of the primal variables to be used in calls to the evaluation methods

Parameters:

primals (vector_like) – Vector with the values of primal variables.

variable_names()

DEPRECATED.

Deprecated since version 6.0.0: This method has been replaced with primals_names (will be removed in (or after) 6.0)