SNOPT

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.

Installation

Building from source

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 pyoptsparse/pySNOPT/source.

From v2.0 onwards, only SNOPT v7.7.x is officially supported. To use pyOptSparse with previous versions of SNOPT, please checkout release v1.2. We currently test v7.7.7 and v7.7.1.

Installation by conda

When installing via conda, all pyoptsparse binaries are pre-compiled and installed as part of the package. However, the snopt binding module cannot be included as part of the package due to license restrictions.

If you are installing via conda and would like to use SNOPT, you will need to build the snopt binding module on your own, and inform pyoptsparse that it should use that library.

Suppose you have built the binding file, producing snopt.cpython-310.so, living in the folder ~/snopt-bind.

To use this module, set the environment variable, PYOPTSPARSE_IMPORT_SNOPT_FROM, e.g.:

PYOPTSPARSE_IMPORT_SNOPT_FROM=~/snopt-bind/

This will attempt to load the snopt binding module from ~/snopt-bind. If the module cannot be loaded from this path, a warning will be raised at import time, and an error will be raised if attempting to run the SNOPT optimizer.

Options

Please refer to the SNOPT user manual for a complete listing of options and their default values. The following are a list of

• options which have values changed from the defaults within SNOPT

• options unique to pyOptSparse, implemented in the Python wrapper and not found in SNOPT

SNOPT Default Options

Name	Type	Default value	Description
iPrint	int	18	Print File Output Unit (override internally in snopt?)
iSumm	int	19	Summary File Output Unit (override internally in snopt?)
Print file	str	SNOPT_print.out	Print file name
Summary file	str	SNOPT_summary.out	Summary file name
Minor print level	int	0	Minor iterations print level
Problem Type	str	Minimize	This specifies the problem type for SNOPT. Minimize: minimization problem. Maximize: maximization problem. Feasible point: compute a feasible point only.
Start	str	Cold	This value is directly passed to the SNOPT kernel, and will be overwritten if another option, e.g. Cold start is supplied, in accordance with SNOPT options precedence. Cold: Cold start Hot: Hot start
Derivative level	int	3	The SNOPT derivative level. Only “3” is tested, where all derivatives are provided to SNOPT.
Iterations limit	int	10000000	The limit on the total number of minor iterations, summed over all major iterations. This option is set to a very large number to prevent premature termination of SNOPT.
Minor iterations limit	int	10000	The limit on the number of minor iterations for each major iteration. This option is set to a very large number to prevent premature termination of SNOPT.
Proximal iterations limit	int	10000	The iterations limit for solving the proximal point problem. We set this by default to a very large value in order to fully solve the proximal point problem to optimality
Total character workspace	int	None	The total character workspace length. If None, a default value of 500 is used, as recommended by SNOPT. If SNOPT determines that the default value is too small, the Python wrapper will overwrite the defaults with estimates for the required workspace lengths from SNOPT and initialize the optimizer for a second time. SNOPT might still exit with 82, 83, or 84, but this should automate the storage allocation for most cases. User-specified values are not overwritten.
Total integer workspace	int	None	The total integer workspace length. If None, a default value of 500 + 100 * (ncon + nvar) is used, as recommended by SNOPT. If SNOPT determines that the default value is too small, the Python wrapper will overwrite the defaults with estimates for the required workspace lengths from SNOPT and initialize the optimizer for a second time. SNOPT might still exit with 82, 83, or 84, but this should automate the storage allocation for most cases. User-specified values are not overwritten.
Total real workspace	int	None	The total real workspace length. If None, a default value of 500 + 200 * (ncon + nvar) is used, as recommended by SNOPT. If SNOPT determines that the default value is too small, the Python wrapper will overwrite the defaults with estimates for the required workspace lengths from SNOPT and initialize the optimizer for a second time. SNOPT might still exit with 82, 83, or 84, but this should automate the storage allocation for most cases. User-specified values are not overwritten.
Save major iteration variables	list	[]	This option is unique to the Python wrapper, and takes a list of values which can be saved at each major iteration to the History file. The possible values are Hessian slack lambda nS BSwap maxVi penalty_vector In addition, a set of default parameters are saved to the history file and cannot be changed. These are nMajor nMinor step feasibility optimality merit condZHZ penalty
Return work arrays	bool	False	This option is unique to the Python wrapper. If True, internal SNOPT work arrays are also returned at the end of the optimization. These arrays can be used to hot start a subsequent optimization. The SNOPT option ‘Sticky parameters’ will also be automatically set to ‘Yes’ to facilitate the hot start.
Work arrays save file	NoneType or str	None	This option is unique to the Python wrapper. The SNOPT work arrays will be pickled and saved to this file after each major iteration. This file is useful if you want to restart an optimization that did not exit cleanly. If None, the work arrays are not saved.
snSTOP function handle	NoneType or function	None	This option is unique to the Python wrapper. A function handle can be supplied which is called at the end of each major iteration. The following is an example of a callback function that saves the restart dictionary to a different file after each major iteration. def snstopCallback(iterDict, restartDict): # Get the major iteration number nMajor = iterDict["nMajor"] # Save the restart dictionary writePickle(f"restart_{nMajor}.pickle", restartDict) return 0
snSTOP arguments	list	[]	This option is unique to the Python wrapper. It specifies a list of arguments that will be passed to the snSTOP function handle. iterDict is always passed as an argument. Additional arguments are passed in the same order as this list. The possible values are restartDict

Informs

SNOPT Informs

Code	Description
0	finished successfully
1	optimality conditions satisfied
2	feasible point found
3	requested accuracy could not be achieved
5	elastic objective minimized
6	elastic infeasibilities minimized
10	the problem appears to be infeasible
11	infeasible linear constraints
12	infeasible linear equalities
13	nonlinear infeasibilities minimized
14	infeasibilities minimized
15	infeasible linear constraints in QP subproblem
16	infeasible nonelastic constraints
20	the problem appears to be unbounded
21	unbounded objective
22	constraint violation limit reached
30	resource limit error
31	iteration limit reached
32	major iteration limit reached
33	the superbasics limit is too small
34	time limit reached
40	terminated after numerical difficulties
41	current point cannot be improved
42	singular basis
43	cannot satisfy the general constraints
44	ill-conditioned null-space basis
45	unable to compute acceptable LU factors
50	error in the user-supplied functions
51	incorrect objective derivatives
52	incorrect constraint derivatives
56	irregular or badly scaled problem functions
60	undefined user-supplied functions
61	undefined function at the first feasible point
62	undefined function at the initial point
63	unable to proceed into undefined region
70	user requested termination
71	terminated during function evaluation
74	terminated from monitor routine
80	insufficient storage allocated
81	work arrays must have at least 500 elements
82	not enough character storage
83	not enough integer storage
84	not enough real storage
90	input arguments out of range
91	invalid input argument
92	basis file dimensions do not match this problem
140	system error
141	wrong no of basic variables
142	error in basis package

API

class pyoptsparse.pySNOPT.pySNOPT.SNOPT(*args, **kwargs)

SNOPT Optimizer 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, timeLimit=None, restartDict=None)

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. The default is None which will use SNOPT’s own finite differences which are vastly superior to the pyOptSparse implementation. 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 1e-6 when sens is FD and 1e-40j 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 SNOPT 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 hot-starting only.

timeLimitfloat

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. From SNOPT 7.7, use the “Time limit” option instead.

restartDictdict

A dictionary containing the necessary information for hot-starting SNOPT. This is typically the same dictionary returned by this function on a previous invocation.

Returns:

solSolution object

The optimization solution

restartDictdict

If Return work arrays is True, a dictionary of arrays is also returned