Linear least squares

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위키데이터

• ID : Q17086259

말뭉치

1. Transform first the nonlinear function y(T) to a linear one and then solve a linear least squares problem.
2. *} , T_0^{*}\)) and solve the linear least squares problem using the method of normal equations (optionally QR decomposition) in order to recover these exact parameters.
3. Modeling Workhorse Linear least squares regression is by far the most widely used modeling method.
4. Not only is linear least squares regression the most widely used modeling method, but it has been adapted to a broad range of situations that are outside its direct scope.
5. Linear least squares regression also gets its name from the way the estimates of the unknown parameters are computed.
6. As a result, nonlinear least squares regression could be used to fit this model, but linear least squares cannot be used.
7. The approach is called linear least squares since the assumed function is linear in the parameters to be estimated.
8. In contrast, non-linear least squares problems generally must be solved by an iterative procedure, and the problems can be non-convex with multiple optima for the objective function.
9. In statistics, linear least squares problems correspond to a particularly important type of statistical model called linear regression which arises as a particular form of regression analysis.
10. Importantly, in "linear least squares", we are not restricted to using a line as the model as in the above example.
11. There is, in some cases, a closed-form solution to a non-linear least squares problem – but in general there is not.
12. In contrast, linear least squares tries to minimize the distance in the y {\displaystyle y} direction only.
13. Table 5.11 summarizes performance results obtained for the ScaLAPACK routine PSGELS /PDGELS that solves full-rank linear least squares problems.
14. (2018) Condition numbers for a linear function of the solution of the linear least squares problem with equality constraints.
15. Partial condition number for the equality constrained linear least squares problem.
16. (2017) Updating QR factorization procedure for solution of linear least squares problem with equality constraints.
17. Statistical Estimates for the Conditioning of Linear Least Squares Problems.
18. This topic describes LAPACK driver routines used for solving linear least squares problems.
19. Computes the minimum norm solution to a linear least squares problem using the singular value decomposition of A and a divide and conquer method.
20. Madsen K, Nielsen HB, Tingleff O. Methods for Non-Linear Least Squares Problems.

메타데이터

위키데이터

• ID : Q17086259

Spacy 패턴 목록

• [{'LOWER': 'linear'}, {'LOWER': 'least'}, {'LEMMA': 'square'}]