600. 445 Fall 1999 Copyright © R. H. Taylor Given a linear systemAx -b = e, Linear Least Squares (sometimes written Ax  b) We want to minimize the sum.

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Presentation transcript:

Fall 1999 Copyright © R. H. Taylor Given a linear systemAx -b = e, Linear Least Squares (sometimes written Ax  b) We want to minimize the sum of squares of the errors

Fall 1999 Copyright © R. H. Taylor Linear Least Squares Many methods for Ax  b One simple one is to compute Better methods based on orthogonal transformations exist These methods are available in standard math libraries A short review follows

Fall 1999 Copyright © R. H. Taylor Orthogonal Transformations The key property is: Some implications of this are as follows

Fall 1999 Copyright © R. H. Taylor Singular Value Decomposition Developed by Golub, et al in late 1960’s (I think) Commonly available in mathematical libraries E.g., –MATLAB –IMSL –Numerical Recipes (Wm. Press, et. al., Cambridge Press) –CISST ERC Math Library

Fall 1999 Copyright © R. H. Taylor Singular Value Decomposition Given an arbitrary m  n matrix A, there exist orthogonal matrices U, V and a diagonal matrix S that:

Fall 1999 Copyright © R. H. Taylor SVD Least Squares Solve this for y (trivial, since S is diagonal), then compute