Least-Squares and Triangulated Vortex Methods
Total Least Squares Methods
... namely into scalar minimization problems, where each function evaluation requires the solution of a quadrat- ically constrained linear least squares problem. 26 The constrained formulation (10) has been ...
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Overview of total least squares methods
... total least squares ...solution methods. In the case of the basic total least squares problem and the generalized total least squares problem, presented in Section ...
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Least-Squares Methods for Policy Iteration
... reviews least- squares methods for policy iteration, an important class of algorithms for approxi- mate reinforcement ...called: least-squares temporal differ- ence, ...
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Valuing American style derivatives by least squares methods
... Carlo methods with variance reduction techniques since, in this way, we can reduce the bias in the estimation of the early exercise value and achieve a more accurate price of the ...(2004b) methods to price ...
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Valuing American Style Options by Least Squares Methods
... In the last few years, the LSM and the WME estimators have raised great interest amongst practitioners working in the finance industry. The main reason for this, as mentioned above, is their suitability for pricing very ...
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Non-parametric and least squares Langley plot methods
... and least squares methods of straight-line fitting to identify Langley plots useful for ...the methods on the estimated calibration ...
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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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SELECTION OF REFERENCE PLANE BY THE LEAST SQUARES FITTING METHODS
... paper least square fitting methods (cylinder, polynomial) and commercial filters (Gaussian filter, Gaussian regression filter and ro- bust Gaussian regression filter) for areal form removal were compared ...
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Regularization methods for the solution of a nonlinear least-squares problem in tomography
... 3.2.4 Comparisons between methods In this section we are going to compare the several methods we analysed and highlight pros and cons. From what we said in the previous sections, the Gauss-Newton method ...
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Finite element least squares methods for a compressible stokes system
... KEEHWAN KIM, SANGDONG KIM, and SANGSIK SHIN Received 25 April 2002 The least-squares functional related to a vorticity variable or a velocity flux variable is consid- ered for two-dimensional compressible ...
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Robust Least Squares Methods Under Bounded Data Uncertainties
... estimation methods and the direct LS estimation method tuned to the estimates of the data matrix and output ...estimation methods we propose can be cast as SDP problems, hence our methods can be ...
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Numerical methods for least squares problems with application to data assimilation
... Probabilistic methods for 4DVAR problems (ensemble based methods) The aim of this chapter is to present the application of the approach developed in the previ- ous chapter to data assimilation problems ...
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Numerical investigations of linear least squares methods for derivative estimation
... for least squares estimates of function gradients are ...a least squares problem using a truncated Taylor ...the least squares ...
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Comparison of Two Time Series Decomposition Methods: Least Squares and Buys Ballot Methods
... estimation methods (LSE and BBE) are equal both in magnitude and direction all the ...two methods are equally effective in estimat- ing the trend parameters and seasonal indices when the model for ...
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Least Squares Temporal Difference Methods: An Analysis Under General Conditions
... λ→1 T (λ) (J ), ∀J ∈ < n . (5) (T (0) = T in particular.) It appears in the projected Bellman equation (2), J = ΠT (λ) (J ), associated with the TD(λ) methods. We approximate J ∗ by a vector in a subspace of < n , ...
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Reducing Communication in Proximal Newton Methods for Sparse Least Squares Problems
... Newton methods are iterative algorithms that solve l1- regularized least squares ...these methods have become popular since they enable the analysis of large-scale machine learning ...these ...
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V -invariant methods, generalised least squares problems, and the Kalman filter
... These methods are summarised briefly here, and the ability to han- dle multiple scales indicated. An application to a class of Kalman filter problems derived from generalised smoothing splines is consid- ered. ...
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Two-stage least squares and indirect least squares algorithms for simultaneous equations models
... of methods (indirect least squares (ILS), two-stage least squares (2SLS), three-stage least squares (3SLS), ...limited-information methods which consider one ...
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Notes on Regularized Least Squares
... regularized least squares, where we work with matrix factorizations of the data or kernel ...iterative methods such as conjugate gradient, and this is frequently the method of choice for large data ...
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