A descent algorithm without line search for unconstrained optimization

Guang-ming, Zhou

doi:10.1016/j.amc.2009.08.058

Cited by 5 publications

(8 citation statements)

References 22 publications

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“…As shown in Section 1, there are many feasible largescale optimization algorithms. Take the GD algorithm [27] for instance, the control parameters can be optimized using the following formulas:…”

Section: Numerical Modelmentioning

confidence: 99%

Estimation of Oceanic Eddy Viscosity Profile and Wind Stress Drag Coefficient Using Adjoint Method

Zhang

Gao

2015

Mathematical Problems in Engineering

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Adjoint method is used to assimilate pseudoobservations to simultaneously estimate the OEVP and the WSDC in an oceanic Ekman layer model. Five groups of experiments are designed to investigate the influences that the optimization algorithms, step-length, inverse integral time of the adjoint model, prescribed vertical distribution of eddy viscosity, and regularization parameter exert on the inversion results. Experimental results show that the best estimation results are obtained with the GD algorithm; the best estimation results are obtained when the step-length is equal to 1 in Group 2; in Group 3, 8 days of inverse integral time yields the best estimation results, and good assimilation efficiency is achieved by increasing iteration steps when the inverse integral time is reduced; in Group 4, the OEVP can be estimated for some specific distributions; however, when the VEVCs increase along with the depth at the bottom of water, the estimation results are relatively poor. For this problem, we use extrapolation method to deal with the VEVCs in layers in which the estimation results are poor; the regularization method with appropriate regularization parameter can indeed improve the experiment result to some extent. In all experiments in Groups 2-3, the WSDCs are inverted successfully within 100 iterations.

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Section: Numerical Modelmentioning

confidence: 99%

Estimation of Oceanic Eddy Viscosity Profile and Wind Stress Drag Coefficient Using Adjoint Method

Zhang

Gao

2015

Mathematical Problems in Engineering

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“…Compared with [10], the extended algorithm has more effective numerical perfermance, so it is effective.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we extended the descent algorithm without line search of [10] to more general case, and got its global convergence. Compared with [10], the extended algorithm has more effective numerical perfermance, so it is effective.…”

Section: Resultsmentioning

confidence: 99%

“…Inspired by the above literatures, in this paper we will extend the descent algorithm without line search of [10] to more general case, and discuss its global convergence. The rest of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…In [7] [8], Wang and Zhu put forward to conjugate gradient path methods without line search. Shi, Shen and Zhou proposed descent methods without line search in [9] and [10], respectively. Further, Zhou presented the steepest descent algorithm without line search in [11].…”

Section: Introductionmentioning

confidence: 99%

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