Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms

Seepage with Nonlinear Permeability by Least Square FEM

In seepage problems, the coefficients of permeability in Laplace equation are usually assumed to be constant vs. both space and time; but in reality these coefficients are variable. In this study, the effect of material deformation due to external loads (consolidation) and variation of head in the consolidation process are considered. For the first case, formulation of kx and ky can be defined ...

Multiple solutions of a nonlinear reactive transport model using least square pseudo-spectral collocation method

The recognition and the calculation of all branches of solutions of the nonlinear boundary value problems is difficult obviously. The complexity of this issue goes back to the being nonlinearity of the problem. Regarding this matter, this paper considers steady state reactive transport model which does not have exact closed-form solution and discovers existence of dual or triple solutions in so...

Using an Efficient Penalty Method for Solving Linear Least Square Problem with Nonlinear Constraints

In this paper, we use a penalty method for solving the linear least squares problem with nonlinear constraints. In each iteration of penalty methods for solving the problem, the calculation of projected Hessian matrix is required. Given that the objective function is linear least squares, projected Hessian matrix of the penalty function consists of two parts that the exact amount of a part of i...

Normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output channels

This paper studies normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output (MIMO) channels. Although the MIMO channel is often modeled as sparse, traditional normalized least mean square-based filtering algorithm never takes the advantage of the inherent sparse structure information and thus causes some performance loss. Unlike the t...

Orthogonal Least Square with Boosting for Regression