# Lecture 5 - 5 Least Squares Problems Consider the solution...

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5 Least Squares Problems Consider the solution of A x = b , where A C m × n with m > n . In general, this system is overdetermined and no exact solution is possible. Example Fit a straight line to 10 measurements. If we represent the line by f ( x ) = mx + c and the 10 pieces of data are { ( x 1 , y 1 ) , . . . , ( x 10 , y 10 ) } , then the constraints can be sumamrized in the linear system x 1 1 x 2 1 . . . . . . x 10 1 A m c x = y 1 y 2 . . . y 10 b . This type of problem is known as linear regression or (linear) least squares fitting . The basic idea (due to Gauss) is to minimize the 2-norm of the residual vector , i.e., b - A x 2 . In other words, we want to find x C n such that m i =1 [ b i - ( A x ) i ] 2 is minimized. For the notation used in the example above we want to find m and c such that 10 i =1 [ y i - ( mx i + c ))] 2 is minimized. Example We can generalize the previous example to polynomial least squares fitting of arbitrary degree. To this end we assume that p ( x ) = n i =0 c i x i , where n is the degree of the polynomial. We can fit a polynomial of degree n to m > n data points ( x i , y i ), i = 1 , . . . , m , using the least squares approach, i.e., min m i =1 [ y i - p ( x i )] 2 is used as constraint for the overdetermined linear system A x = b with A = 1 x 1 x 2 1 . . . x n 1 1 x 2 x 2 2 . . . x n 2 . . . . . . . . . . . . 1 x m x 2 m . . . x n m , x = c 0 c 1 c 2 . . . c n , b = y 1 y 2 . . . y m . Remark The special case n = m - 1 is called interpolation and is known to have a unique solutions if the conditions are independent, i.e., the points x i are distinct. How- ever, for large degrees n we frequently observe severe oscillations which is undesirable.

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