NLPQLP¶
NLPQLP is a sequential quadratic programming (SQP) method which solves problems with smooth continuously differentiable objective function and constraints. The algorithm uses a quadratic approximation of the Lagrangian function and a linearization of the constraints. To generate a search direction a quadratic subproblem is formulated and solved. The line search can be performed with respect to two alternative merit functions, and the Hessian approximation is updated by a modified BFGS formula.
NLPQLP is a proprietary software, which can be obtained here. The latest version supported is v4.2.2.
API¶

class
pyoptsparse.pyNLPQLP.pyNLPQLP.
NLPQLP
(*args, **kwargs)[source]¶ NLPQL Optimizer Class  Inherited from Optimizer Abstract Class

__call__
(self, optProb, sens=None, sensStep=None, sensMode=None, storeHistory=None, hotStart=None, storeSens=True)[source]¶ This is the main routine used to solve the optimization problem.
Parameters:  optProb : Optimization or Solution class instance
This is the complete description of the optimization problem to be solved by the optimizer
 sens : str or python Function.
Specifiy method to compute sensitivities. To explictly use pyOptSparse gradient class to do the derivatives with finite differenes use ‘FD’. ‘sens’ may also be ‘CS’ which will cause pyOptSpare to compute the derivatives using the complex step method. Finally, ‘sens’ may be a python function handle which is expected to compute the sensitivities directly. For expensive function evaluations and/or problems with large numbers of design variables this is the preferred method.
 sensStep : float
Set the step size to use for design variables. Defaults to 1e6 when sens is ‘FD’ and 1e40j when sens is ‘CS’.
 sensMode : str
Use ‘pgc’ for parallel gradient computations. Only available with mpi4py and each objective evaluation is otherwise serial
 storeHistory : str
File name of the history file into which the history of this optimization will be stored
 hotStart : str
File name of the history file to “replay” for the optimziation. The optimization problem used to generate the history file specified in ‘hotStart’ must be IDENTICAL to the currently supplied ‘optProb’. By identical we mean, EVERY SINGLE PARAMETER MUST BE IDENTICAL. As soon as he requested evaluation point from NLPQL does not match the history, function and gradient evaluations revert back to normal evaluations.
 storeSens : bool
Flag sepcifying if sensitivities are to be stored in hist. This is necessay for hotstarting only.
