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.
Options
Name 
Type 
Default value 
Description 


float 
1e06 
Convergence accuracy 

float 
1e14 
Convergence accuracy for QP 

float 
1e06 
Minimum step length 

int 
20 
Maximum Number of Function Calls During Line Search 

int 
500 
Maximum Number of Iterations 

int 
1 
Maximum stack size for nonmonotone line search 

float 
1.0 
Factor scaling identify for IFAIL=2 

int 
2 
Output Level


int 
0 
Mode (0  Normal Execution, 1 to 18  See Manual) 

int 
6 
Output Unit Number 

bool 
True 
Merit Function Type (True  L2 Augmented Penalty, False  L1 Penalty) 

bool 
False 
QP Subproblem Solver (True  QuasiNewton, False  Cholesky) 

str 

Output File Name 
Informs
Code 
Description 


Compute gradient values w.r.t. the variables stored in first column of X, and store them in DF and DG. Only derivatives for active constraints ACTIVE(J)=.TRUE. need to be computed. 

Compute objective fn and all constraint values subjectthe variables found in the first L columns of X, and store them in F and G. 

The optimality conditions are satisfied. 

The algorithm has been stopped after MAXIT iterations. 

The algorithm computed an uphill search direction. 

Underflow occurred when determining a new approximation matrix for the Hessian of the Lagrangian. 

The line search could not be terminated successfully. 

Length of a working array is too short. More detailed error information is obtained with IPRINT>0 

There are false dimensions, for example M>MMAX, N>=NMAX, or MNN2<>M+N+N+2. 

The search direction is close to zero, but the current iterate is still infeasible. 

The starting point violates a lower or upper bound. 

Wrong input parameter, i.e., MODE, LDL decomposition in D and C (in case of MODE=1), IPRINT, IOUT 

Internal inconsistency of the quadratic subproblem, division by zero. 

More than MAXFUN successive nonevaluable function calls. 

The solution of the quadratic programming subproblem has been terminated with an error message and IFAIL is set to IFQL+100, where IFQL denotes the index of an inconsistent constraint. 
API
 class pyoptsparse.pyNLPQLP.pyNLPQLP.NLPQLP(*args, **kwargs)[source]
NLPQL Optimizer Class  Inherited from Optimizer Abstract Class
This is the base optimizer class that all optimizers inherit from. We define common methods here to avoid code duplication.
 Parameters
 namestr
Optimizer name
 categorystr
Typically local or global
 defaultOptionsdictionary
A dictionary containing the default options
 informsdict
Dictionary of the inform codes
 __call__(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
 optProbOptimization or Solution class instance
This is the complete description of the optimization problem to be solved by the optimizer
 sensstr or python Function.
Specify method to compute sensitivities. To explicitly use pyOptSparse gradient class to do the derivatives with finite differences 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.
 sensStepfloat
Set the step size to use for design variables. Defaults to 1e6 when sens is ‘FD’ and 1e40j when sens is ‘CS’.
 sensModestr
Use ‘pgc’ for parallel gradient computations. Only available with mpi4py and each objective evaluation is otherwise serial
 storeHistorystr
File name of the history file into which the history of this optimization will be stored
 hotStartstr
File name of the history file to “replay” for the optimization. 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.
 storeSensbool
Flag specifying if sensitivities are to be stored in hist. This is necessary for hotstarting only.