PSQP
This optimizer implements a sequential quadratic programming method with a BFGS variable metric update
Options
Name 
Type 
Default value 
Description 


float 
1e+16 
Maximum Stepsize 

float 
1e16 
Variable Change Tolerance 

float 
1e06 
Constraint Violation Tolerance 

float 
1e06 
Lagrangian Gradient Tolerance 

float 
0.0001 
Penalty Coefficient 

int 
1000 
Maximum Number of Iterations 

int 
2000 
Maximum Number of Function Evaluations 

int 
2 
Variable Metric Update (1  BFGS, 2  Hoshino) 

int 
2 
Negative Curvature Correction (1  None, 2  Powell’s Correction) 

int 
2 
Output Level (0  None, 1  Final, 2  Iter) 

int 
6 
Output Unit Number 

str 

Output File Name 
Informs
Code 
Description 


Change in design variable was less than or equal to tolerance 

Change in objective function was less than or equal to tolerance 

Objective function less than or equal to tolerance 

Maximum constraint value is less than or equal to tolerance 

Maximum number of iterations exceeded 

Maximum number of function evaluations exceeded 

Maximum number of gradient evaluations exceeded 

Termination criterion not satisfied, but obtained point is acceptable 

Positive directional derivative in line search 

Interpolation error in line search 

Optimization failed 
API
 class pyoptsparse.pyPSQP.pyPSQP.PSQP(*args, **kwargs)[source]
PSQP 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.
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.
 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 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 PSQP does not match the history, function and gradient evaluations revert back to normal evaluations.
 storeSensbool
Flag sepcifying if sensitivities are to be stored in hist. This is necessay for hotstarting only.