Nonlinear Least-squares Problem
Regularization methods for the solution of a nonlinear least-squares problem in tomography
... ill-posed problem, that needs a ...high-dimensional nonlinear least-squares problem, that is much more complex than a linear least-squares one and needs for its resolution ...
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Solving Nonlinear Least Squares Problem Using Gauss-Newton Method
... optimization problem occurs when an objective function is, either minimized or maximized, over a set of ...the nonlinear least squares problem is formulated as an optimization ...
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Selime GÜROL. Solving regularized nonlinear least-squares problem in dual space with application to variational data assimilation
... In this thesis, a further step is taken in making the RPCG dual solver relevant to practice for large scale, nonlinear problems. A general LMP is first introduced for the case when the system matrix of a sequence ...
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Solution of the Nonlinear Least Squares problem using a new Gradient Based Genetic Algorithm
... and nonlinear least squares method for material characterization using elastic waves" [8] make use of a material characterization problem in which parameters are determined by minimizing ...
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Bounded-variable least-squares methods for linear and nonlinear model predictive control
... optimization problem can be for- mulated based on a linear I/O model in order to achieve a faster performance as compared to the conventional state-space based ...the problem in a way that grants the ...
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Nonlinear Least Squares Estimation of the Shifted Gompertz Distribution
... In this paper, we consider the nonlinear weighted LS parameter estimation problem for the shifted Gompertz distribution. Our focus is on the existence of the corresponding best LS estimate. To the best of ...
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Numerical Analysis in Nonlinear Least Squares Methods and Applications
... Suppose that H.x/ is positive definite, then it is nonsingular so that equation ( 2.10 ) has a unique solution. It is obvious that p N k is a descent direction of F .x/ by multiply- ing p N k T on both sides of equation ...
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Least Squares Solution for Discrete Time Nonlinear Stochastic Optimal Control Problem with Model Reality Differences
... time nonlinear stochastic optimal control ...control problem is reformulated into the input-output equa- ...sum squares of output error is defined. In these point of views, the least ...
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Estimation and Inference in Unstable Nonlinear Least Squares Models (Final)
... Chapter 9 Conclusions We considered a univariate NLS models with multiple parameter changes that occur at un- known dates. If the number of breaks is known, we showed that, similar to the linear model of Bai and Perron ...
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Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems
... Several authors have studied inexact Newton’s methods for solving NLS problems 11. Xiaofang et al. have introduced stable factorized quassi-Newton methods for solving large-scale NLS 12. Dennis et al. proposed a ...
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Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
... robust Nonlinear Least Trimmed Squares es- timator has been proposed in this study by adding an Elastic net penalty to the NLTS loss ...possible problem which needs to be ...
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NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program
... 4. “No. of parameters to be estimated” : It is the number m in p1, p2, … , Pm. For the Hougen problem it is 5. Input 5 and Enter. 5. “Would you specify any limits … “: If the user has to specify some bounds ...
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Identification of some nonlinear systems by using least-squares support vector machines
... 2.1.1 Types of Models A system can be modeled as a box with an input and output. Then the problem is how to model the box. In literature, more emphasis is given on mainly three types of modeling, namely white, ...
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NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program
... 4. “No. of parameters to be estimated” : It is the number m in p1, p2, … , Pm. For the Hougen problem it is 5. Input 5 and Enter. 5. “Would you specify any limits … “: If the user has to specify some bounds ...
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On limited memory SQP methods for large scale constrained nonlinear least squares
... Contents C901 nonlinear programming problem with a simple objective. This is then solved by a limited memory variation of sqp methods. This overcomes one of the major drawbacks of the traditional sqp ...
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Nonlinear partial least squares
... These two techniques can handle both underdetermined (fewer observations than variables) data sets and collinearity amongst the variables, by capturing the underlying structure in[r] ...
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A note on the scaled total least squares problem
... total least squares (STLS) problem is a generalization of the total least squares (TLS) ...TLS problem is to find E ∈ R m×n and r ∈ R m solving the problem 聻 The first and ...
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Estimation and inference in unstable nonlinear least squares models
... to nonlinear models. To that end, we consider a nonlinear model that can be estimated via nonlinear least squares (NLS) and features a limited number of parameter shifts occur- ring at ...
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THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
... LINEAR REGRESSION LINEAR REGRESSION is a powerfull tool for studying fundamental relationships between two (or more) RVs Y and X. The method is based on the method of least squares. Let’s discuss the ...
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A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems
... For nonlinear rational systems, how- ever, the resulting noise is a function of the ...linear least squares compen- sation (Stoica & S¨oderstr¨om, ...are nonlinear systems, both noise variance ...
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