NLP Interface

class pyomo.contrib.pynumero.interfaces.nlp.NLP[source]

Bases: object

constraint_names()[source]

Override this to provide string names for the constraints

abstract constraints_lb()[source]

Returns vector of lower bounds for the constraints

Returns:
Return type:vector-like
abstract constraints_ub()[source]

Returns vector of upper bounds for the constraints

Returns:
Return type:vector-like
abstract create_new_vector(vector_type)[source]

Creates a vector of the appropriate length and structure as requested

Parameters:vector_type ({'primals', 'constraints', 'duals'}) – String identifying the appropriate vector to create.
Returns:
Return type:vector-like
abstract evaluate_constraints(out=None)[source]

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.
Returns:
Return type:vector_like
abstract evaluate_grad_objective(out=None)[source]

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.
Returns:
Return type:vector_like
abstract evaluate_hessian_lag(out=None)[source]

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
Returns:
Return type:matrix_like
abstract evaluate_jacobian(out=None)[source]

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.
Returns:
Return type:matrix_like
abstract evaluate_objective()[source]

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

Returns:
Return type:float
abstract get_constraints_scaling()[source]

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

Returns:
Return type:array-like or None
abstract get_duals()[source]

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.

abstract get_obj_factor()[source]

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)

abstract get_obj_scaling()[source]

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

Returns:
Return type:float or None
abstract get_primals()[source]

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

abstract get_primals_scaling()[source]

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

Returns:
Return type:array-like or None
abstract init_duals()[source]

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

abstract init_primals()[source]

Returns vector with initial values for the primal variables

abstract n_constraints()[source]

Returns number of constraints

abstract n_primals()[source]

Returns number of primal variables

abstract nnz_hessian_lag()[source]

Returns number of nonzero values in hessian of the lagrangian function

abstract nnz_jacobian()[source]

Returns number of nonzero values in jacobian of equality constraints

abstract primals_lb()[source]

Returns vector of lower bounds for the primal variables

Returns:
Return type:vector-like
primals_names()[source]

Override this to provide string names for the primal variables

abstract primals_ub()[source]

Returns vector of upper bounds for the primal variables

Returns:
Return type:vector-like
abstract report_solver_status(status_code, status_message)[source]

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

abstract set_duals(duals)[source]

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
abstract set_obj_factor(obj_factor)[source]

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
abstract set_primals(primals)[source]

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.