tutorial2.py

Functions
	tutorial2.TutModelData.buildModel (self)
	adding the variables and constraints to the model
	tutorial2.TutModelData.tapeFunction (self, x, rowno)
	evaluates the nonlinear function and records a tape is necessary
	tutorial2.TutModelData.initialiseAutoDiff (self)
	initialises the automatic differentiation
	tutorial2.TutModelData.evaluateNonlinearTerm (self, x, rowno, ignerr, thread)
	tutorial2.TutModelData.evaluateNonlinearJacobian (self, x, rowno, jacnum, ignerr, thread)
	tutorial2.TutModelData.evaluateSDLagrangian (self, x, u, hessianrow, hessiancol)

Detailed Description

this extends the tutorial by including the computation of the Lagrangian of the Hessian.

Function Documentation

buildModel()

tutorial2.TutModelData.buildModel

(

self

)

adding the variables and constraints to the model

Definition at line 26 of file tutorial2.py.

tapeFunction()

tutorial2.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 58 of file tutorial2.py.

initialiseAutoDiff()

tutorial2.TutModelData.initialiseAutoDiff

(

self

)

initialises the automatic differentiation

Definition at line 91 of file tutorial2.py.

evaluateNonlinearTerm()

tutorial2.TutModelData.evaluateNonlinearTerm	(		self,
			x,
			rowno,
			ignerr,
			thread )

Definition at line 104 of file tutorial2.py.

evaluateNonlinearJacobian()

tutorial2.TutModelData.evaluateNonlinearJacobian	(		self,
			x,
			rowno,
			jacnum,
			ignerr,
			thread )

Definition at line 117 of file tutorial2.py.

evaluateSDLagrangian()

tutorial2.TutModelData.evaluateSDLagrangian	(		self,
			x,
			u,
			hessianrow,
			hessiancol )

Definition at line 134 of file tutorial2.py.