Application of gradient descent method to the sedimentary grain-size distribution fitting

Chen, Haibo; Lv, Xianqing; Qiao, Yansong

doi:10.1016/j.cam.2009.09.005

Cited by 10 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where the denominator ‖g ‖ 2 denotes the 2 norm of the gradient g and 0 is an empirical constant. This method has been used in a relatively small number of studies (see [13,14,23,52]) showing that this method is indeed feasible and effective in practical application. distributed and is prescribed with a spatial distribution which is constructed by the trigonometric function (see Figure 1(c)).…”

Section: Simplified Gradient Descent Methods (Gdm-s)mentioning

confidence: 99%

Estimation of Open Boundary Conditions for an Internal Tidal Model with Adjoint Method: A Comparative Study on Optimization Methods

Chen

Miao

2013

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Based on an internal tidal model, the practical performances of the limited-memory BFGS (L-BFGS) method and two gradient descent (GD) methods (the normal one with Wolfe’s line search and the simplified one) are investigated computationally through a series of ideal experiments in which the open boundary conditions (OBCs) are inverted by assimilating the interior observations with the adjoint method. In the case that the observations closer to the unknown boundary are included for assimilation, the L-BFGS method performs the best. As compared with the simplified GD method, the normal one really uses less iteration to reach a satisfactory solution, but its advantage over the simplified one is much smaller than expected. In the case that only the observations that are further from the unknown boundary are assimilated, the simplified GD method performs the best instead, whereas the performances of the other two methods are not satisfactory. The advanced L-BFGS algorithm and Wolfe’s line search still need to be improved when applied to the practical cases. The simplified GD method, which is controllable and easy to implement, should be regarded seriously as a choice, especially when the classical advanced optimization techniques fail or perform poorly.

show abstract

Section: Simplified Gradient Descent Methods (Gdm-s)mentioning

confidence: 99%

Estimation of Open Boundary Conditions for an Internal Tidal Model with Adjoint Method: A Comparative Study on Optimization Methods

Chen

Miao

2013

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The stochastic gradient (SG) algorithm has less computational burden and slower convergence rate than the recursive least squares algorithm [15][16][17]. Some new algorithms were presented to improve the convergence rate of the SG algorithm [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Gradient-Based Iterative Identification for Wiener Nonlinear Dynamic Systems with Moving Average Noises

Zhou

et al. 2015

Algorithms

View full text Add to dashboard Cite

This paper focuses on the parameter identification problem for Wiener nonlinear dynamic systems with moving average noises. In order to improve the convergence rate, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates, and to compute iteratively the noise estimates based on the obtained parameter estimates. The simulation results show that the proposed algorithm can effectively estimate the parameters of Wiener systems with moving average noises.

show abstract

“…In [51] the algorithm of gradient descent is used in particle physics to find the minimum energy of a system. In [52] a weighted lognormal function is used to fit the data obtained from a distribution of sediments. This function has parameters that need to be optimized to better fit the data.…”

Section: Figure 2: Algorithm For Gradient Descentmentioning

confidence: 99%

Methods and Techniques for Inter-cloud Data Transfers

Contreras¹

View full text Add to dashboard Cite

This thesis focuses on methods and techniques for transferring data from a compute cloud to a storage cloud by using a buffer at the compute cloud. A space driven, a time driven, and a hybrid buffering technique are discussed. It was found that using buffering at the compute cloud is only beneficial when the load on the system is high. To improve performance a threshold-based and a machine learning based adaptive method are introduced at the buffer to dynamically change between buffering techniques at runtime based on the load on the system state. Using the adaptive methods leads to a significant improvement in performance. A proof-of-concept prototype was built for the different data transfer methods and techniques researched. Experiments performed on the prototype provide insights into system behaviour and performance.

show abstract

Application of gradient descent method to the sedimentary grain-size distribution fitting

Cited by 10 publications

References 19 publications

Estimation of Open Boundary Conditions for an Internal Tidal Model with Adjoint Method: A Comparative Study on Optimization Methods

Estimation of Open Boundary Conditions for an Internal Tidal Model with Adjoint Method: A Comparative Study on Optimization Methods

Gradient-Based Iterative Identification for Wiener Nonlinear Dynamic Systems with Moving Average Noises

Methods and Techniques for Inter-cloud Data Transfers

Contact Info

Product

Resources

About