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. The current version of SNOPT being tested is v7.7.5, although v7.7.1 is also expected to work.

Options

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

• The SNOPT option Proximal iterations limit has its default value changed to 10000, in order to fully solve the proximal point problem to optimality

• The option Save major iteration variables is unique to the Python wrapper, and takes a list of values which can be saved at each iteration to the History file. Possible values are

  • step

  • merit

  • feasibility

  • optimality

  • penalty

  • Hessian

  • slack

  • lambda

  • condZHZ

• The option Start corresponds to the value directly passed to the SNOPT kernel, and will be overwritten if another option, e.g. Cold start is supplied.

• The default value for Total character workspace is set internally to the minimum work array length of 500. The default values for Total integer workspace and Total real workspace depend on the number of design variables and constraints. They are computed based on recommendations in the SNOPT manual.

• If SNOPT determines that the default values for Total character workspace, Total integer workspace, or Total real workspace are 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.

Option name	Type	Default value
iPrint	int	18
iSumm	int	19
Print file	str	SNOPT_print.out
Summary file	str	SNOPT_summary.out
Problem Type	str	[‘Minimize’, ‘Maximize’, ‘Feasible point’]
Start	str	[‘Cold’, ‘Warm’]
Derivative level	int	3
Proximal iterations limit	int	10000
Total character workspace	int	None
Total integer workspace	int	None
Total real workspace	int	None
Save major iteration variables	list	[‘step’, ‘merit’, ‘feasibility’, ‘optimality’, ‘penalty’]

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: Any, **kwargs: Any)

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.