SNOPT is a sparse nonlinear optimizer that is particularly useful for solving large-scale constrained problems with smooth objective functions and constraints. The algorithm consists of a sequential quadratic programming (SQP) algorithm that uses a smooth augmented Lagrangian merit function, while making explicit provision for infeasibility in the original problem and in the quadratic programming subproblems. The Hessian of the Lagrangian is approximated using the BFGS quasi-Newton update.
SNOPT is available for purchase here. Upon purchase, you should receive a zip file. Within the zip file, there is a folder called
src. To use SNOPT with pyoptsparse, paste all files from
src except snopth.f into
From v2.0 onwards, only SNOPT v7.7 is officially supported. To use pyOptSparse with previous versions of SNOPT, please checkout release v1.2.
SNOPT Optimizer Class - Inherited from Optimizer Abstract Class
__call__(self, optProb, sens=None, sensStep=None, sensMode=None, storeHistory=None, hotStart=None, storeSens=True, timeLimit=None)¶
This is the main routine used to solve the optimization problem.
- 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. The default is None which will use SNOPT’s own finite differences which are vastly superiour to the pyOptSparse implementation. 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 1e-6 when sens is ‘FD’ and 1e-40j 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 SNOPT 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 hot-starting only.
- timeLimit : float
Specify the maximum amount of time for optimizer to run. Must be in seconds. This can be useful on queue systems when you want an optimization to cleanly finish before the job runs out of time.