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Copyright 2003, Landmark Graphics and others.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
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package wsh.opt;
/**
* Define methods applying a linear transform and its transpose
*
* @author W.S. Harlan
*/
public interface LinearTransform {
/**
* Apply the linear transform data = F model
* Zero the current data, and do not add.
*
* @param data Output after linear transform
* @param model Input for linear transform
*/
void forward(Vect data, VectConst model);
/**
* Apply the transpose of a linear transform model = F' data
* Add to existing data.
*
* @param data Input for transpose.
* @param model Output after linear transform.
*/
void addTranspose(VectConst data, Vect model);
/**
* To speed convergence multiple a model by an approximate inverse
* Hessian. An empty implementation is equivalent to an identity
* and is also okay.
* The Hessian is equivalent to multiplying once by the
* forward operation and then by the transpose. Your approximate
* inverse can greatly speed convergence by trying to diagonalize
* this Hessian, or at least balancing the diagonal.
*
* @param model The model to be multiplied.
*/
void inverseHessian(Vect model);
/**
* Apply any robust trimming of outliers, or
* scale all errors for an approximate L1 norm when squared.
* This method should do nothing if you want a standard
* least-squares solution.
* Do not change the overall variance of the errors more than necessary.
*
* @param dataError This is the original data minus the modeled data.
*/
void adjustRobustErrors(Vect dataError);
}