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pyOpt Quick Reference Guide

Copyright (c) 2008-2014, pyOpt Developers

This is a quick guide to begin solving optimization problems with pyOpt.

Optimization Problem Definition

pyOpt is design to solve general constrained nonlinear optimization problems:

min f(x)
x
s.t. g_j(x) = 0, j = 1, ..., m_e

g_j(x) <= 0, j = m_e + 1, ..., m

x_i_L <= x_i <= x_i_U, i = 1, ..., n

where:

  • x is the vector of design variables
  • f(x) is a nonlinear function
  • g(x) is a linear or nonlinear function
  • n is the number of design variables
  • m_e is the number of equality constraints
  • m is the total number of constraints (number of equality constraints: m_i = m - m_e)

Optimization Class

Instanciating an Optimization Problem:

>>> opt_prob = Optimization('name',obj_fun,var_set={},obj_set={},con_set={})

Notes on Objective Functions:

General Objective Function Template

def obj_fun(x, *args, **kwargs):

  fail = 0
  f = function(x,*args,**kwargs)
  g = function(x,*args,**kwargs)

  return f,g,fail

where:

f - objective value

g - array (or list) of constraint values

fail - 0 for successful function evaluation - 1 for unsuccessful function evaluation (test must be provided by user)

If the Optimization problem is unconstraint g must be returned as an empty

list or array: g = []

Inequality constraints are handled as <=.

Assigning Objective:

>>> opt_prob.addObj('name', value=0.0, optimum=0.0)

Assigning Design Variables:

Single Design variable:

>>> opt_prob.addVar('name', type='c', value=0.0, lower=-inf, upper=inf, choices=listochoices)

A Group of Design Variables:

>>> opt_prob.addVarGroup('name', numerinGroup, type='c', value=value, lower=lb, upper=up,choices=listochoices)

where:

value,lb,ub - (float or int or list or 1Darray).

Supported Types:

'c' - continous design variable.

'i' - integer design variable.

'd' - discrete design variable (based on choices, e.g.: list/dict of materials).

Assigning Constraints:

Single Constraint:

>>> opt_prob.addCon('name', type='i', lower=-inf, upper=inf, equal=0.0)

A Group of Constraints:

>>> opt_prob.addConGroup('name', numberinGroup, type='i', lower=lb, upper=up, equal=eq)

where:

lb,ub,eq - (float or int or list or 1Darray).

Supported Types:

'i' - inequality constraint.

'e' - equality constraint.

Optimizer Class

Instanciating an Optimizer (e.g.: Snopt):

>>> opt = pySNOPT.SNOPT()

Setting Optimizer Options:

either during instanciation:

>>> opt = pySNOPT.SNOPT(options={'name':value,...})

or one by one:

>>> opt.setOption('name',value)

Getting Optimizer Options/Attributes:

>>> opt.getOption('name')
>>> opt.ListAttributes()

Optimizing

Solving the Optimization Problem:

>>> opt(opt_prob, sens_type='FD', disp_opts=False, sens_mode='',*args, **kwargs)

disp_opts - flag for displaying the options in the solution output.

sens_type - sensitivity type. - 'FD' = finite differences. - 'CS' = complex step. - grad_function = user provided function.

format: grad_function(x,f,g) returns g_obj,g_con,fail

sens_mode - parallel sensitivity flag (''-serial,'pgc'-parallel).

Additional arguments and keyword arguments (e.g.: parameters) can be passed to the objective function.

Output:

  • Prompt output of the Optimization problem with inital values:

    >>> print opt_prob

  • Prompt output of specific solution of the Optimization problem:

    >>> print opt_prob._solutions[key]

    key - index in order of optimizer call.

  • File output of the Optimization problem:

    >>> opt_prob.write2file(outfile='', disp_sols=False, solutions=[])

    where:

    outfile - (filename, fileinstance, default=opt_prob name[0].txt).

    disp_sols - True will display all the stored solutions.

    solutions - list of indicies of stored solutions to display.

Output as Input:

The solution can be used directly as a optimization problem for refinement by the same or a new optimizer:

>>> optimizer(opt_prob._solutions[key])

key - index in order of optimzer call.

The new solution will be stored as a sub-solution of the previous solution:

e.g.: print opt_prob._solutions[key]._solutions[nkey]

History and Hot Start:

The history flag stores all fucntion evaluations from an optimizer in binary format in a .bin and .cue file:

>>> optimizer(opt_prob, store_hst=True)

True - uses default print name for the file names str - filename

The binary history file can be used to hot start the optimzer if the optimization was interruped. The flag needs the filename of the history (True will use the default name)

>>> optimizer(opt_prob, store_hst=True, hot_start=True)

If the store history flag is set as the same as the hot start flag a temporary file will be created during the run and teh original file will be overwritten at the end.

For hot start to work properly all options must be the same as when the history was created.