NLP Interface
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class
pyomo.contrib.pynumero.interfaces.nlp.
NLP
[source] Bases:
object
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abstract
constraints_lb
()[source] Returns vector of lower bounds for the constraints
Return type: vector-like
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abstract
constraints_ub
()[source] Returns vector of upper bounds for the constraints
Return type: vector-like
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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. Return type: vector-like
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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. Return type: vector_like
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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. Return type: vector_like
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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 Return type: matrix_like
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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. Return type: matrix_like
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abstract
evaluate_objective
()[source] Returns value of objective function evaluated at the values given for the primal variables in set_primals
Return type: float
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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
Return type: array-like or None
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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.
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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)
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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
Return type: float or None
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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
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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
Return type: array-like or None
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abstract
init_duals
()[source] Returns vector with initial values for the dual variables of the constraints
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abstract
nnz_hessian_lag
()[source] Returns number of nonzero values in hessian of the lagrangian function
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abstract
nnz_jacobian
()[source] Returns number of nonzero values in jacobian of equality constraints
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abstract
primals_lb
()[source] Returns vector of lower bounds for the primal variables
Return type: vector-like
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abstract
primals_ub
()[source] Returns vector of upper bounds for the primal variables
Return type: vector-like
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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
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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
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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
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abstract