AMPL NLP Interface
- class pyomo.contrib.pynumero.interfaces.ampl_nlp.AmplNLP(nl_file, row_filename=None, col_filename=None)[source]
Bases:
AslNLP- constraint_idx(con_name)[source]
Returns the index of the constraint named con_name (corresponding to the order returned by evaluate_constraints)
- constraint_names()[source]
Returns an ordered list with the names of all the constraints (corresponding to evaluate_constraints)
- 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:
- eq_constraint_idx(con_name)[source]
Returns the index of the equality constraint named con_name (corresponding to the order returned by evaluate_eq_constraints)
- eq_constraint_names()[source]
Returns ordered list with names of equality constraints only (corresponding to evaluate_eq_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:
- 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_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_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_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
- ineq_constraint_idx(con_name)[source]
Returns the index of the inequality constraint named con_name (corresponding to the order returned by evaluate_ineq_constraints)
- ineq_constraint_names()[source]
Returns ordered list with names of inequality constraints only (corresponding to evaluate_ineq_constraints)
- 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
- 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_ub()
Returns vector of upper bounds for the primal variables
- Return type:
vector-like
- 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.