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4.3 Least Squares Approximations

218 Chapter 4. Least Squares ApproximationsIt often happens thatAxDbhas no solution. The usual reason is:too many matrix has more rows than columns. There are more equations than unknowns(mis greater thann). Thencolumns span a small part ofm-dimensional space. Unless allmeasurements are perfect,bis outside that column space. Elimination reaches animpossible equation and stops. But we can t stop just because measurements include repeat: We cannot always get the erroreDb Axdown to zero. Wheneis zero,xis an exact solution the length ofeis as small as possible,bxis aleast Squares goal in this section is to computebxand use it. These are realproblems and they need an previous section emphasizedp(the projection).

least squares solution. Our goal in this section is to computebx and use it. These are real problems and they need an answer. The previous section emphasized p (the projection). This section emphasizes bx (the least squares solution). They are connected by p DAbx. The fundamental equation is still A TAbx DA b. Here is a short unofficial way to ...

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