Результаты поиска по 'Gauss–Newton method':
Найдено статей: 3
  1. Sviridenko A.B.
    The correction to Newton's methods of optimization
    Computer Research and Modeling, 2015, v. 7, no. 4, pp. 835-863

    An approach to the decrease of norm of the correction in Newton’s methods of optimization, based on the Cholesky’s factorization is presented, which is based on the integration with the technique of the choice of leading element of algorithm of linear programming as a method of solving the system of equations. We investigate the issues of increasing of the numerical stability of the Cholesky’s decomposition and the Gauss’ method of exception.

    Keywords: correction, algorithm, Newton’s methods of optimization, Cholesky’s decomposition, Gauss’ method of exception, linear programming, numerical stability, integration.
    Views (last year): 1. Citations: 6 (RSCI).
  2. Dunyushkin D.Y.
    Test-signals forming method for correlation identification of nonlinear systems
    Computer Research and Modeling, 2012, v. 4, no. 4, pp. 721-733

    Тhe new test-signals forming method for correlation identification of a nonlinear system based on Lee–Shetzen cross-correlation approach is developed and tested. Numerical Gauss–Newton algorithm is applied to correct autocorrelation functions of test signals. The achieved test-signals have length less than 40 000 points and allow to measure the 2nd order Wiener kernels with a linear resolution up to 32 points, the 3rd order Wiener kernels with a linear resolution up to 12 points and the 4th order Wiener kernels with a linear resolution up to 8 points.

    Keywords: nonlinear dynamic systems, Volterra–Wiener approach, system identification, cross-correlation approach, Lee–Shetzen method, test-signals, white noise.
    Views (last year): 1. Citations: 3 (RSCI).
  3. Yudin N.E.
    Modified Gauss–Newton method for solving a smooth system of nonlinear equations
    Computer Research and Modeling, 2021, v. 13, no. 4, pp. 697-723

    In this paper, we introduce a new version of Gauss–Newton method for solving a system of nonlinear equations based on ideas of the residual upper bound for a system of nonlinear equations and a quadratic regularization term. The introduced Gauss–Newton method in practice virtually forms the whole parameterized family of the methods solving systems of nonlinear equations and regression problems. The developed family of Gauss–Newton methods completely consists of iterative methods with generalization for cases of non-euclidean normed spaces, including special forms of Levenberg–Marquardt algorithms. The developed methods use the local model based on a parameterized proximal mapping allowing us to use an inexact oracle of «black–box» form with restrictions for the computational precision and computational complexity. We perform an efficiency analysis including global and local convergence for the developed family of methods with an arbitrary oracle in terms of iteration complexity, precision and complexity of both local model and oracle, problem dimensionality. We present global sublinear convergence rates for methods of the proposed family for solving a system of nonlinear equations, consisting of Lipschitz smooth functions. We prove local superlinear convergence under extra natural non-degeneracy assumptions for system of nonlinear functions. We prove both local and global linear convergence for a system of nonlinear equations under Polyak–Lojasiewicz condition for proposed Gauss– Newton methods. Besides theoretical justifications of methods we also consider practical implementation issues. In particular, for conducted experiments we present effective computational schemes for the exact oracle regarding to the dimensionality of a problem. The proposed family of methods unites several existing and frequent in practice Gauss–Newton method modifications, allowing us to construct a flexible and convenient method implementable using standard convex optimization and computational linear algebra techniques.

    Keywords: systems of nonlinear equations, nonlinear regression, Gauss–Newton method, Levenberg– Marquardt algorithm, trust rergion methods, nonconvex optimization, inexact proximal mapping, inexact oracle, Polyak–Lojasiewicz condition, complexity estimate.

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