least-squares optimization problem
Off -Grid Direction-of-Arrival Estimation Using a Sparse Array Covariance Matrix
... Abstract—An off-grid direction-of-arrival (DOA) estimation method that utilizes a sparse array covariance matrix is proposed. In this method, the array covariance matrix is sparsely represented in the form of a vector and ...
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A Simulation-based Portfolio Optimization Approach with Least Squares Learning
... Abstract—This paper introduces a simulation-based numeri- cal method for solving dynamic portfolio optimization problem. We describe a recursive numerical approach that is based on the Least ...
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Overview of total least squares methods
... alternating least squares ...total least squares problems and is globally convergent, with linear convergence ...specialized optimization methods on a Grassman manifold. The ...
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Solving Nonlinear Least Squares Problem Using Gauss-Newton Method
... An optimization problem occurs when an objective function is, either minimized or maximized, over a set of ...nonlinear least squares problem is formulated as an optimization ...
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On weighted structured total least squares
... equivalent optimization problem (1) is a nonlinear least squares prob- lem, so that classical optimization methods can be used for its ...The optimization methods require a cost ...
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Optimal Dictionary for Least Squares Representation
... of problem (7) is bounded be- low by the optimal value, if it exists, of the one given in ...that optimization problem (21) admits a solution, and we shall furnish a feasible solution of (7) that ...
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Particle Swarm Optimization and Differentiation Evolution –Based Weighted Least Squares State Estimation
... Swarm Optimization (PSO) is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence, is known to effectively solve large-scale nonlinear ...
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Least Median of Squares Estimation by Optimization Heuristics with an Application to the CAPM and Multi Factor Models
... Price et al. (2005) report that, although the scale factor F has no upper limit and the crossover parameter CR is a fine tuning element, both are problem specific. In an attempt to improve the tuning of the ...
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Image magnification by least squares surfaces
... In recent years, nonlinear interpolation methods have been used to reform linear methods for improving image quality and solving the blur problem. The change of non-linear methods depends on the interpolation ...
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Least squares estimation of joint production functions by the Differential Evolution method of global optimization
... Mundlak (1963) approached estimation of joint production function through aggregation. His method lies in specifying the individual micro production function for each (joint) product as well as the manner of aggregating ...
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The element wise weighted total least squares problem
... estimation problem is typically defined as an optimization problem: an appropriate cost function depending on the data is minimized over the estimated param- ...the least-squares (LS) ...
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Sparse least trimmed squares regression.
... Even though the resulting estimates are not sparse, prediction accuracy is improved by shrinking the coefficients, and the computational issues with high-dimensional robust estimators are overcome due to the ...
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Updating QR factorization procedure for solution of linear least squares problem with equality constraints
... original problem without solving it ...in optimization and signal processing [], statistics [], network and structural anal- ysis [, ] and discretization of differential equations ...LLS ...
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Solution of the Nonlinear Least Squares problem using a new Gradient Based Genetic Algorithm
... Two hybrid genetic algorithms are discussed in [14]. One uses GA with hill climbing and the other uses GA with Quasi Newton [14]. Whereas in [17] a hybrid simulated annealing technique is used for estimating the ...
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Least Squares Fitting of Chacón Gielis Curves by the Particle Swarm Method of Optimization
... global optimization method can ensure its immunity to entrapment by local optima, especially in the vicinity of the true global ...global optimization of complicated functions is an extremely difficult ...
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Particle swarm optimization and least squares estimation of NARMAX
... processing [21], biomedical engineering [22], communication and others. Typically, the parameter estimation step is performed using various types LLS estimation algorithms because of the excellent characteristics where ...
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Deformation analysis with Total Least Squares
... a Least Squares (LS) technique is used for the transforma- tion ...Total Least Squares (TLS) that is considerably a new approach in geodetic ...the Least Squares (LS) and the ...
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Least squares Hermitian problem of complex matrix equation \((AXB,CXD)=(E,F)\)
... It is should be noticed that () is always consistent. Therefore, solving Ax = b is usually translated into solving the corresponding consistent equations (). In the following, we will extend the conclusion to a more ...
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Distributed Learning with Regularized Least Squares
... output function of this distributed learning is a good approximation to the algorithm processing the whole data in one single machine. Our derived learning rates in expectation are optimal and stated in a general setting ...
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An Introduction to Partial Least Squares Regression
... An experimental SAS/STAT software procedure, PROC PLS, is available with Release 6.11 of the SAS System for performing various factor-extraction methods of modeling, including partial least squares. Other ...
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