csnlp.nlps#

A module for defining NLPs and its building blocks. From simplest to most complex, and following the inheritance hierarchy, these are:

  • csnlp.nlps.HasParameters: a class for the creation and storage of symbolic parameters for NLP problems

  • csnlp.nlps.HasVariables: a class for the creation and storage of symbolic variables for NLP problems

  • csnlp.nlps.HasConstraints: a class for the creation and storage of symbolic constraints (dependent on variables and parameters) for NLP problems. The constraints can be equality, inequality, lower- or upper-bound.

  • csnlp.nlps.HasObjective: a class for the assignment of a scalar minimization objective function (dependent on variables and parameters).

  • csnlp.Nlp: a class that combines all the above building blocks into a full-fledged NLP problem.

Classes

HasConstraints([sym_type, ...])

Class for the creation and storage symbolic constraints for an NLP problem.

HasObjective([sym_type, ...])

Class for creating an NLP problem with parameters, variables, constraints and an objective.

HasParameters([sym_type])

Class for the creation and storage of symbolic parameters in an NLP problem.

HasVariables([sym_type])

Class for the creation and storage of symbolic variables in an NLP problem.

Nlp([sym_type, remove_redundant_x_bounds, ...])

The generic NLP class is a controller that solves a (possibly, nonlinear) optimization problem to yield a (possibly, sub-) optimal solution.