The Experts below are selected from a list of 169065 Experts worldwide ranked by ideXlab platform
Gonglin Yuan - One of the best experts on this subject based on the ideXlab platform.
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The PRP conjugate gradient algorithm with a modified WWP line Search and its application in the image restoration problems
Applied Numerical Mathematics, 2020Co-Authors: Gonglin Yuan, Zhan WangAbstract:Abstract It is well known that the conjugate gradient algorithm is one of the most classic and useful methods for solving large-scale optimization problems, where the Polak-Ribiere-Polyak(PRP) method is an important and effective conjugate gradient algorithm. However, the global convergence of the PRP conjugate gradient method can not be achieved when the WWP line Search Technique is used for a nonconvex function. In this paper, we propose a modified WWP line Search Technique for the PRP conjugate gradient algorithm. The new line Search Technique can guarantee the global convergence of the PRP method for general functions. The numerical results show that the PRP method with a new line Search Technique enables a practical computation and is effective in the image restoration problems.
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global convergence of bfgs and prp methods under a modified weak wolfe powell line Search
Applied Mathematical Modelling, 2017Co-Authors: Gonglin Yuan, Zengxin WeiAbstract:Abstract The BFGS method is one of the most effective quasi-Newton algorithms for optimization problems. However, its global convergence for general functions is still open. In this paper, under a new line Search Technique, this problem is solved, and it is shown that other methods in the Broyden class also possess this property. Moreover, the global convergence of the PRP method is established in the case of this new line Search. Numerical results are reported to show that the new line Search Technique is competitive to that of the normal line Search.
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new line Search methods for unconstrained optimization
Journal of The Korean Statistical Society, 2009Co-Authors: Gonglin Yuan, Zengxin WeiAbstract:It is well known that the Search direction plays a main role in the line Search method. In this paper, we propose a new Search direction together with the Wolfe line Search Technique and one nonmonotone line Search Technique for solving unconstrained optimization problems. The given methods possess sufficiently descent property without carrying out any line Search rule. The convergent results are established under suitable conditions. For numerical results, analysis of one probability shows that the new methods are more effective, robust, and stable, than other similar methods. Numerical results of two statistical problems also show that the presented methods are more interesting than other normal methods.
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a modified prp conjugate gradient method
Annals of Operations Research, 2009Co-Authors: Gonglin YuanAbstract:This paper gives a modified PRP method which possesses the global convergence of nonconvex function and the R-linear convergence rate of uniformly convex function. Furthermore, the presented method has sufficiently descent property and characteristic of automatically being in a trust region without carrying out any line Search Technique. Numerical results indicate that the new method is interesting for the given test problems.
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a new backtracking inexact bfgs method for symmetric nonlinear equations
Computers & Mathematics With Applications, 2008Co-Authors: Gonglin YuanAbstract:A BFGS method, in association with a new backtracking line Search Technique, is presented for solving symmetric nonlinear equations. The global and superlinear convergences of the given method are established under mild conditions. Preliminary numerical results show that the proposed method is better than the normal Technique for the given problems.
J A Snyman - One of the best experts on this subject based on the ideXlab platform.
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a gradient only line Search method for the conjugate gradient method applied to constrained optimization problems with severe noise in the objective function
International Journal for Numerical Methods in Engineering, 2005Co-Authors: J A SnymanAbstract:A new implementation of the conjugate gradient method is presented that economically overcomes the problem of severe numerical noise superimposed on an otherwise smooth underlying objective function of a constrained optimization problem. This is done by the use of a novel gradient-only line Search Technique, which requires only two gradient vector evaluations per Search direction and no explicit function evaluations. The use of this line Search Technique is not restricted to the conjugate gradient method but may be applied to any line Search descent method. This method, in which the gradients may be computed by central finite differences with relatively large perturbations, allows for the effective smoothing out of any numerical noise present in the objective function. This new implementation of the conjugate gradient method, referred to as the ETOPC algorithm, is tested using a large number of well-known test problems. For initial tests with no noise introduced in the objective functions, and with high accuracy requirements set, it is found that the proposed new conjugate gradient implementation is as robust and reliable as traditional first-order penalty function methods. With the introduction of severe relative random noise in the objective function, the results are surprisingly good, with accuracies obtained that are more than sufficient compared to that required for engineering design optimization problems with similar noise. Copyright © 2004 John Wiley & Sons, Ltd.
Zengxin Wei - One of the best experts on this subject based on the ideXlab platform.
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global convergence of bfgs and prp methods under a modified weak wolfe powell line Search
Applied Mathematical Modelling, 2017Co-Authors: Gonglin Yuan, Zengxin WeiAbstract:Abstract The BFGS method is one of the most effective quasi-Newton algorithms for optimization problems. However, its global convergence for general functions is still open. In this paper, under a new line Search Technique, this problem is solved, and it is shown that other methods in the Broyden class also possess this property. Moreover, the global convergence of the PRP method is established in the case of this new line Search. Numerical results are reported to show that the new line Search Technique is competitive to that of the normal line Search.
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new line Search methods for unconstrained optimization
Journal of The Korean Statistical Society, 2009Co-Authors: Gonglin Yuan, Zengxin WeiAbstract:It is well known that the Search direction plays a main role in the line Search method. In this paper, we propose a new Search direction together with the Wolfe line Search Technique and one nonmonotone line Search Technique for solving unconstrained optimization problems. The given methods possess sufficiently descent property without carrying out any line Search rule. The convergent results are established under suitable conditions. For numerical results, analysis of one probability shows that the new methods are more effective, robust, and stable, than other similar methods. Numerical results of two statistical problems also show that the presented methods are more interesting than other normal methods.
Magdy Bayoumi - One of the best experts on this subject based on the ideXlab platform.
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fast motion estimation system using dynamic models for h 264 avc video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2012Co-Authors: Yasser Ismail, Jason Mcneely, M Shaaban, H Mahmoud, Magdy BayoumiAbstract:H.264/AVC offers many coding tools for achieving high compression gains of up to 50% more than other standards. These tools dramatically increase the computational complexity of the block based motion estimation (BB-ME) which consumes up to 80% of the entire encoder's computations. In this paper, computationally efficient accurate skipping models are proposed to speed up any BB-ME algorithm. First, an accurate initial Search center (ISC) is decided using a smart prediction Technique. Thereafter, a dynamic early stop Search termination (DESST) is used to decide if the block at the ISC position can be considered as a best match candidate block or not. If the DESST algorithm fails, a less complex style of the motion estimation algorithm which incorporates dynamic padding window size Technique will be used. Further reductions in computations are achieved by combining the following two Techniques. First, a dynamic partial internal stop Search Technique which utilizes an accurate adaptive threshold model is exploited to skip the internal sum of absolute difference operations between the current and the candidate blocks. Second, a dynamic external stop Search Technique greatly reduces the unnecessary operations by skipping all the irrelevant blocks in the Search area. The proposed Techniques can be incorporated in any block matching motion estimation algorithm. Computational complexity reduction is reflected in the amount of savings in the motion estimation encoding time. The novelty of the proposed Techniques comes from their superior saving in computations with an acceptable degradation in both peak signal-to-noise ratio (PSNR) and bit-rate compared to the state of the art and the recent motion estimation Techniques. Simulation results using H.264/AVC reference software (JM 12.4) show up to 98% saving in motion estimation time using the proposed Techniques compared to the conventional full Search algorithm with a negligible degradation in the PSNR by approximately 0.05 dB and a small increase in the required bits per frame by only 2%. Experimental results also prove the effectiveness of the proposed Techniques if they are incorporated with any fast BB-ME Technique such as fast extended diamond enhanced predictive zonal Search and predictive motion vector field adaptive Search Technique.
Hamidreza Zareipour - One of the best experts on this subject based on the ideXlab platform.
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a new stochastic Search Technique combined with scenario approach for dynamic state estimation of power systems
IEEE Transactions on Power Systems, 2012Co-Authors: Maryam Nejati, Nima Amjady, Hamidreza ZareipourAbstract:In this paper, a new closed loop dynamic state estimation (DSE) method is proposed providing state forecasts (before receiving the corresponding measurements) in addition to state estimation (after receiving the measurements). This method comprises a new stochastic Search Technique and scenario generation approach. The proposed stochastic Search Technique, benefiting from high Search capability, is a new hybridization of differential evolution and bacterial foraging methods. The suggested scenario generation approach is composed of bus load prediction, lattice Monte Carlo simulation (LMCS) and optimal power flow (OPF). This approach can model bus load forecast uncertainty. Most of existing state estimation methods (such as weighted least square) can provide state estimations only in cases that the power system is observable. However, the proposed DSE method can solve the state estimation problem for both observable and unobservable power systems with reasonable accuracy. The proposed DSE method is extensively tested on the well-known IEEE 30-bus and IEEE 118-bus test systems with different sets of measurements and its obtained results are compared with the results of some other state estimation methods. These comparisons confirm the validity of the developed approach.
