SLSQP

SLSQP optimizer is a sequential least squares programming algorithm which uses the Han-Powell quasi-Newton method with a BFGS update of the B-matrix and an L1-test function in the step-length algorithm. The optimizer uses a slightly modified version of Lawson and Hanson’s NNLS nonlinear least-squares solver.

The version provided is the original source code from 1991 by Dieter Kraft.

Options

SLSQP Default Options

Name	Type	Default value	Description
ACC	float	1e-06	Convergence Accurancy
MAXIT	int	500	Maximum Iterations
IPRINT	int	1	Output Level (<0 - None, 0 - Screen, 1 - File)
IOUT	int	60	Output Unit Number
IFILE	str	SLSQP.out	Output File Name

Informs

SLSQP Informs

Code	Description
-1	Gradient evaluation required (g & a)
0	Optimization terminated successfully.
1	Function evaluation required (f & c)
2	More equality constraints than independent variables
3	More than 3*n iterations in LSQ subproblem
4	Inequality constraints incompatible
5	Singular matrix E in LSQ subproblem
6	Singular matrix C in LSQ subproblem
7	Rank-deficient equality constraint subproblem HFTI
8	Positive directional derivative for linesearch
9	Iteration limit exceeded

API

class pyoptsparse.pySLSQP.pySLSQP.SLSQP(*args, **kwargs)

SLSQP 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)

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 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 SLSQP 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.