# Normal equation

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Related to Linear least squares: Nonlinear least squares

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Also found in: Encyclopedia, Wikipedia.

Related to Linear least squares: Nonlinear least squares

suggested the estimation algorithm based on the linear least squares (LLS) [12].

By fixing two factor matrices alternately at each iteration, three coupled linear least squares subproblems are then formulated to find each factor matrix:

Further, the authors in [3] introduce a range variable instead of subtracting the reference equation, and then exploit a linearization approach to devise two linear least squares (LLS) estimators for RSS-based positioning.

For instance, direct algorithms such as the Linear Least Squares Method in the Time Domain and the Linear Least Squares Method in the Frequency Domain may easily identify unstable poles or anomalous zeros with positive real parts.

After that uses linear least squares estimation to determine each rule's result equations.

Using non-full-rank design matrices and numerous models, Monahan covers the linear least squares problem, estimability and least squares estimators, the Gauss-Markov model, distributional theory, statistical inference, topics in testing (such as orthogonal polynomials and contrasts), variance components and mixed models, and the multivariate linear model.

3) fill length data was replotted against %DF/(100-%DF) and a straight line was fitted to the data for each screw, using the linear least squares regression method.

The firm proudly called it a "stepwise population-weighted linear least squares method" to work out policing.

These linear models include the linear least squares method for regression and the logistic regression method for classification.

Average annual rates mentioned in the text and tables are based on the linear least squares trend of the logarithms of the index numbers.

It is clear that for a fixed value of a the linear parameters c can be found by solving a linear least squares problem.

Covered in the 2004 edition are the calculation of linear least squares, the numerical analysis of functional integral and integro-differential equations of Volterra type, sparse grids, complete search in continuous global optimization and constraint satisfaction, and multiscale computational modeling of the heart.

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