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The Least Squares Approximation is a examples-intensive concept. However, it can be solved using the following concise formulas:
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for system
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:<math>A\bold{x}=\bold{b},</math>
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:<math> {A^T(A\bold{\hat{x}} - \bold{b})} = 0,</math>
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which is equivalent to
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:<math> {A^TA\bold{\hat{x}}} = A^{T}\bold{b}.</math>
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'''NOTE:'''
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Thorough examples are available in the [[MA265]] textbook, Webassign problems, and Past Exam Archive. Most problems only deal with linear and parabolic fits.
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'''Main Reference'''
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----
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Kolman, B., & Hill, D. (2007). ''Elementary linear algebra with applications (9th ed.)''. Prentice Hall.
  
  

Latest revision as of 22:37, 4 March 2015

The Least Squares Solution



The Least Squares Approximation is a examples-intensive concept. However, it can be solved using the following concise formulas:

for system

$ A\bold{x}=\bold{b}, $
$ {A^T(A\bold{\hat{x}} - \bold{b})} = 0, $

which is equivalent to

$ {A^TA\bold{\hat{x}}} = A^{T}\bold{b}. $


NOTE:

Thorough examples are available in the MA265 textbook, Webassign problems, and Past Exam Archive. Most problems only deal with linear and parabolic fits.


Main Reference


Kolman, B., & Hill, D. (2007). Elementary linear algebra with applications (9th ed.). Prentice Hall.


Ryan Jason Tedjasukmana


Back to Inner Product Spaces and Orthogonal Complements

Back to MA265 Fall 2010 Prof Walther

Back to MA265

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva