- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Bright optical solitons of Chen-Lee-Liu equation with improved Adomian decomposition method
摘要： Optical solitons form the basic fabric in telecommunication industry. It represents a nonlinear pulse or wave packet and travels without changing its shape and velocity across intercontinental distances [1-25]. The unique property of optical solitons is that they interact like particles [6]. One of the models that stems from nonlinear Schrodinger’s equation and is commonly referred to as one of the forms of derivative nonlinear Schrodinger’s equation. It is the Chen-Lee-Liu (CLL) equation [1] and plays vital role in plasmas and optical fiber communications [3-4]. Recently, there has been a significant increase in interest with this model. The focus however is mainly on the analytical aspect and retrieval of soliton solutions to the model. One can find in [7-16] and the references therein where studies related to W-shaped optical solitons, combined optical solitary waves and conservation laws, chirped W-shaped optical solitons, chirped dark and gray solitons, Laplace-Adomian decomposition method, semi-inverse variational principle and Darboux transformation among others are discussed. However, with the increasing interests on efficient numerical techniques for solving nonlinear evolution equations, we consider an algorithm based on the Improved Adomian Decomposition Method (IADM) that was later investigated in [18-19, 2-23] to numerically treat the CLL equation. The recursive algorithm for the model will be determined coupled with error analyses for some special bright optical soliton solutions that are available in the literature.
关键词： bright solitons,Adomian decomposition,numerics.
更新于2025-09-23 15:23:52
-
On image restoration from random sampling noisy frequency data with regularization
摘要： Consider the image restoration using random sampling noisy frequency data by total variation regularization. By exploring image sparsity property under wavelet expansion, we establish an optimization model with two regularizing terms specifying image sparsity and edge preservation on the restored image. The choice strategy for the regularizing parameters is rigorously set up together with corresponding error estimate on the restored image. The cost functional with data-fitting in the frequency domain is minimized using the Bregman iteration scheme. By deriving the gradient of the cost functional explicitly, the minimizer of the cost functional at each Bregman step is also generated by an inner iteration process with Tikhonov regularization, which is implemented stably and efficiently due to the special structure of the regularizing iterative matrix. Numerical tests are given to show the validity of the proposed scheme.
关键词： Image restoration,iteration,numerics,total variation,wavelet sparsity,error estimate
更新于2025-09-23 15:23:52