CONMIN
CONstrained function MINimization (CONMIN) is a gradientbased optimizer that uses the methods of feasible directions.
Options
Name 
Type 
Default value 
Description 


int 
10000 
Maximum Number of Iterations 

float 
1e06 
Objective Relative Tolerance 

float 
1e06 
Objective Absolute Tolerance 

int 
5 
None 

int 
20 
None 

int 
4 
Print Control (0  None, 1  Final, 2,3,4  Debug) 

int 
6 
Output Unit Number 

str 

Output File Name 
API
 class pyoptsparse.pyCONMIN.pyCONMIN.CONMIN(*args, **kwargs)[source]
CONMIN 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 CONMIN 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.