Adaptive interval wavelet precise integration method for partial differential equations

2005 ◽  
Vol 26 (3) ◽  
pp. 364-371 ◽  
Author(s):  
Mei Shu-li ◽  
Lu Qi-shao ◽  
Zhang Sen-wen ◽  
Jin Li
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Integration Method ◽  
Partial Differential ◽  
Precise Integration ◽  
Precise Integration Method

scholarly journals Adaptive Wavelet Precise Integration Method for Nonlinear Black-Scholes Model Based on Variational Iteration Method

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Huahong Yan
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Iteration Method ◽  
Integration Method ◽  
Variational Iteration Method ◽  
Precise Integration ◽  
Adaptive Wavelet ◽  
Precise Integration Method ◽  
Black Scholes Model ◽  
Black Scholes

An adaptive wavelet precise integration method (WPIM) based on the variational iteration method (VIM) for Black-Scholes model is proposed. Black-Scholes model is a very useful tool on pricing options. First, an adaptive wavelet interpolation operator is constructed which can transform the nonlinear partial differential equations into a matrix ordinary differential equations. Next, VIM is developed to solve the nonlinear matrix differential equation, which is a new asymptotic analytical method for the nonlinear differential equations. Third, an adaptive precise integration method (PIM) for the system of ordinary differential equations is constructed, with which the almost exact numerical solution can be obtained. At last, the famous Black-Scholes model is taken as an example to test this new method. The numerical result shows the method's higher numerical stability and precision.

scholarly journals Finite integration method for partial differential equations

2013 ◽  
Vol 37 (24) ◽  
pp. 10092-10106 ◽  
Author(s):  
P.H. Wen ◽  
Y.C. Hon ◽  
M. Li ◽  
T. Korakianitis
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Integration Method ◽  
Partial Differential

scholarly journals Shannon-Cosine Wavelet Precise Integration Method for Locust Slice Image Mixed Denoising

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Haihua Wang ◽  
Xinxin Zhang ◽  
Shuli Mei
Keyword(s):  
Wavelet Transform ◽  
Differential Equations ◽  
Sparse Representation ◽  
Integration Method ◽  
Nonlinear Differential Equations ◽  
Variational Model ◽  
Precise Integration ◽  
Edge Preserving ◽  
Precise Integration Method ◽  
Salt And Pepper

A novel denoising method for removing mixed noise from locust slice images is proposed by means of Shannon-cosine wavelet and the nonlinear variational model for the image processing. This method includes two parts that are the sparse representation of the slice images and the novel numerical algorithm for solving the variation model on image denoising based on the sparse representation. In the first part, a parametric Shannon-cosine wavelet function is introduced to construct the multiscale wavelet transform matrix, which is applied to represent the slice images sparsely by adjusting the parameters adaptively based on the texture of the locust slice images. By multiplying the matrix with the signal, the multiscale wavelet transform coefficients of the signal can be obtained at one time, which can be used to identify the salt-and-pepper noises in the slice images. This ensures that the salt-and-pepper noise points are kept away from the sparse representation of the slice images. In the second part, a semianalytical method on solving the system of the nonlinear differential equations is constructed based on the sparse representation of the slice images, which is named the sparse wavelet precise integration method (SWPIM). Substituting the sparse representation of the slice images into the Perona–Malik model which is the famous edge-preserving smoothing model for removing the Gaussian noises of the biomedical images, a system of nonlinear differential equations is obtained, whose scale is far smaller than the one obtained by the difference method. The numerical experiments show that both the values of SSIM and PSNR of the denoised locust slice images are better than the classical methods besides the algorithm efficiency.

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