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

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.

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
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 Warm: Warm start
Derivative level	int	3	The SNOPT derivative level. Only “3” is tested, where all derivatives are provided to 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	[‘step’, ‘merit’, ‘feasibility’, ‘optimality’, ‘penalty’]	This option is unique to the Python wrapper, and takes a list of values which can be saved at each iteration to the History file. This specifies the list of major iteration variables to be stored in the history file. Hessian, slack, lambda and condZHZ are also supported.

Informs

SNOPT Informs

Code	Description
0	finished successfully
1	optimality conditions satisfied
2	feasible point found
3	requested accuracy could not be achieved
4	weak QP minimizer
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
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
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
50	error in the user-supplied functions
51	incorrect objective derivatives
52	incorrect constraint derivatives
53	the QP Hessian is indefinite
54	incorrect second derivatives
55	incorrect 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
72	terminated during constraint evaluation
73	terminated during objective 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
93	the QP Hessian is indefinite
100	finished successfully
101	SPECS file read
102	Jacobian structure estimated
103	MPS file read
104	memory requirements estimated
105	user-supplied derivatives appear to be correct
106	no derivatives were checked
107	some SPECS keywords were not recognized
110	errors while processing MPS data
111	no MPS file specified
112	problem-size estimates too small
113	fatal error in the MPS file
120	errors while estimating Jacobian structure
121	cannot find Jacobian structure at given point
130	fatal errors while reading the SP
131	no SPECS file (iSpecs le 0 or iSpecs gt 99)
132	End-of-file while looking for a BEGIN
133	End-of-file while reading SPECS file
134	ENDRUN found before any valid SPECS
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 - Inherited from Optimizer Abstract Class

SNOPT Optimizer Class Initialization

__call__(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.

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. 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.

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 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.

storeSensbool

Flag sepcifying if sensitivities are to be stored in hist. This is necessay 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.