Functions
	tutorial.TutModelData.buildModel (self)
	adding the variables and constraints to the model

	tutorial.TutModelData.tapeFunction (self, x, rowno)
	evaluates the nonlinear function and records a tape is necessary

	tutorial.TutModelData.initialiseAutoDiff (self)
	initialises the automatic differentiation

	tutorial.TutModelData.evaluateNonlinearTerm (self, x, rowno, ignerr, thread)

	tutorial.TutModelData.evaluateNonlinearJacobian (self, x, rowno, jacnum, ignerr, thread)

Detailed Description

the tutorial example using the automatic differentiation library ADOL-C.

a tutorial providing an introduction to the CONOPT API

For more information about the individual callbacks, please have a look at the source code.

Function Documentation

tutorial.TutModelData.buildModel ( self )

adding the variables and constraints to the model

Definition at line 27 of file tutorial.py.

tutorial.TutModelData.tapeFunction	(	self,
		x,
		rowno )

evaluates the nonlinear function and records a tape is necessary

Parameters

x	current point to be evaluated
rowno	the index of the constraint. This is also used for the trace tag.

Definition at line 56 of file tutorial.py.

tutorial.TutModelData.initialiseAutoDiff ( self )

initialises the automatic differentiation

Definition at line 91 of file tutorial.py.

tutorial.TutModelData.evaluateNonlinearTerm	(	self,
		x,
		rowno,
		ignerr,
		thread )

Definition at line 104 of file tutorial.py.

tutorial.TutModelData.evaluateNonlinearJacobian	(	self,
		x,
		rowno,
		jacnum,
		ignerr,
		thread )

Definition at line 117 of file tutorial.py.