[PDF] Top 20 A Retrospective Filter Trust Region Algorithm for Unconstrained Optimization

... The trust region method for unconstrained optimiza- tion is first presented by Powell [1], which, in some sense, is equivalent to the Levenberg-Marquardt method which is used to solve the least ... See full document

... It is well known that the line search methods play a very important role for optimization problems. In this paper a new line search method is proposed for solving unconstrained optimization. Under ... See full document

... approach is equipped with the nonmonotone technique as proposed in [2, 3], and uses a slight modification of the secant condition in [7] for constructing an approximation of the Hessian at the current point. Moreover, a ... See full document

... large-scale unconstrained optimization problems and nonlinear equations, we propose a new three-term conjugate gradient algorithm under the Yuan–Wei–Lu line search ...gradient algorithm, which ... See full document

... non-monotone trust region method with line search based on conic model in Section ...new algorithm and the global convergence property are given in Section ... See full document

... constrained optimization problem (NLP), there are many practical methods to solve it, such as augmented Lagrangian function method [1-6], Trust-region filter method [7,8], QP-free feasible ... See full document

... adaptive trust region method for unconstrained optimization ...new algorithm and proved the global convergence theory under some mild ... See full document

... the algorithm of non-monotone trust region method with non-monotone Wolfe-type line search strategy for unconstrained optimization problems based on (5), (6) and ...new algorithm ... See full document

... Newton algorithm with correc- tion by trust region technique, and then prove the global convergence of the new algorithm under some suitable ...Newton algorithm with correction and ... See full document

... These methods above only involve single directions, so that if problems occur, the algorithm discontinues. Aiming at these problems, we present corresponding improved methods, using a curve-linear searching ... See full document

... To further reduce the ripple and overshoot near the band edges, a transition region will be defined with a linear transfer function. Then the L frequency samples are taken 2 / using which the first N samples of ... See full document

... in which m(0) := 0 and 0 ≤ m(k) ≤ min { m(k − 1) + 1, N } with N ≥ 0. The theoretical and numerical results have shown that the proposed technique has some remarkable effects and improves both the possibility of finding ... See full document

... the trust network clearly becomes difficult to control and we can’t traverse all ...initial trust network will be created based on acquaintance circle, and then all the trust relationships from START ... See full document

... In mathematics, Newton's method is an iterative method for finding roots of equations. In optimization, Newton's method is specialized to find stationary points of differentiable functions, which are the zeros of ... See full document

... genetic algorithm is natural coding technique used for coding in search space solution when tracking optimization problem ...In optimization process, the function is calculated by mean-square error ... See full document

... colony optimization (ACO) [30], and GA have been extensively applied by majority of ...bat algorithm (BA) [35], firefly algorithm (FA) [36], and fireworks algorithm (FWA) [37] have shown ... See full document

... of optimization is to improve performance of meta-heuristic algorithms [24] by integrating with chaos theory, Levy flights strategy, Adaptive randomization technique, Evolutionary boundary handling scheme, and ... See full document

... x x H x (3) The initial point is   0.4 0.6  T . The termination condition value is 1.0 10   6 . The initial step size of one-dimensional blind pathfinding optimization is 0.1. For the negative gradient ... See full document

... solving large-scale nonlinear equations (see [–]). In fact, spectral gradient, BFGS quasi- Newton, and conjugate gradient methods can solve large-scale optimization problems and systems of nonlinear equations ... See full document

... The paper is organized as follows. A new nonmonotone hybrid conjugate gradient al- gorithm is presented and the global convergence of the algorithm is proved in Section . The line convergence rate of the ... See full document