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Theoretical considerations or problem descriptions
often represent linear transformations by matrices.
In scientific and engineering applications,
as in the convolution example above,
these matrices are often large and sparse.
An implementation of such a transformation as an explicit matrix
multiplication is inefficient.
Instead Jest utilizes the more general concept of operator interfaces
and leaves implementation details to each application.
The operator concept,
of course,
includes the specific implementation of a linear operator as a matrix
but also generalizes to nonlinear transformations.
Table 4:
Some Jest operator methods (name, syntax, effect).
image 
y = f.image(x) 

image 
f.image(x,y) 

addImage 
f.addImage(x,y) 

getDomain 
f.getDomain() 
returns vector space 
The convolution in our example is a linear operator
that implements Jest's operator interface.
A few operator methods are listed in Table 4.
The interface for a linear operator with adjoint is called hasAdjoint
and extends the operator interface by a method adjoint() that returns
the adjoint operator of the original operator. Many solvers require
a linear operator's adjoint.
The deconvolution example uses a simple convolution class Tcai
and its adjoint; both implement the hasAdjoint interface.
An auxiliary class TcaiFactory
creates the needed transient convolution object.
Next: Deconvolution
Up: A deconvolution example
Previous: Vectors
Stanford Exploration Project
3/8/1999