"Linear least squares"의 두 판 사이의 차이
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* ID : [https://www.wikidata.org/wiki/Q17086259 Q17086259] | * ID : [https://www.wikidata.org/wiki/Q17086259 Q17086259] | ||
+ | ===Spacy 패턴 목록=== | ||
+ | * [{'LOWER': 'linear'}, {'LOWER': 'least'}, {'LEMMA': 'square'}] |
2021년 2월 17일 (수) 00:52 기준 최신판
노트
위키데이터
- ID : Q17086259
말뭉치
- Transform first the nonlinear function y(T) to a linear one and then solve a linear least squares problem.[1]
- *} , 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.[1]
- Modeling Workhorse Linear least squares regression is by far the most widely used modeling method.[2]
- 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.[2]
- Linear least squares regression also gets its name from the way the estimates of the unknown parameters are computed.[2]
- As a result, nonlinear least squares regression could be used to fit this model, but linear least squares cannot be used.[2]
- The approach is called linear least squares since the assumed function is linear in the parameters to be estimated.[3]
- 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.[3]
- 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.[3]
- Importantly, in "linear least squares", we are not restricted to using a line as the model as in the above example.[3]
- There is, in some cases, a closed-form solution to a non-linear least squares problem – but in general there is not.[4]
- In contrast, linear least squares tries to minimize the distance in the y {\displaystyle y} direction only.[4]
- Table 5.11 summarizes performance results obtained for the ScaLAPACK routine PSGELS /PDGELS that solves full-rank linear least squares problems.[5]
- (2018) Condition numbers for a linear function of the solution of the linear least squares problem with equality constraints.[6]
- Partial condition number for the equality constrained linear least squares problem.[6]
- (2017) Updating QR factorization procedure for solution of linear least squares problem with equality constraints.[6]
- Statistical Estimates for the Conditioning of Linear Least Squares Problems.[6]
- This topic describes LAPACK driver routines used for solving linear least squares problems.[7]
- Computes the minimum norm solution to a linear least squares problem using the singular value decomposition of A and a divide and conquer method.[8]
- Madsen K, Nielsen HB, Tingleff O. Methods for Non-Linear Least Squares Problems.[9]
소스
- ↑ 1.0 1.1 Numerical Solution of Linear Least Squares Problems
- ↑ 2.0 2.1 2.2 2.3 4.1.4.1. Linear Least Squares Regression
- ↑ 3.0 3.1 3.2 3.3 Linear least squares
- ↑ 4.0 4.1 Least squares
- ↑ Solving Linear Least Squares Problems
- ↑ 6.0 6.1 6.2 6.3 Perturbation Theory for the Least Squares Problem with Linear Equality Constraints
- ↑ Linear Least Squares (LLS) Problems
- ↑ Linear Least Squares (LLS) Problems
- ↑ Non-linear Least-Squares Optimization of Rational Filters for the Solution of Interior Hermitian Eigenvalue Problems
메타데이터
위키데이터
- ID : Q17086259
Spacy 패턴 목록
- [{'LOWER': 'linear'}, {'LOWER': 'least'}, {'LEMMA': 'square'}]