A priori information (surface topography and the optical properties) and regularization approaches (mainly Tikhonov method [75]) are usually required to solve the ill-posed inverse problem [70, 76].
Further, the optimization algorithm exhibited better convergence properties with regularization, although no significant improvements in the model predictions was observed.
This exact regularization property of (1.7) was studied in [19, 20].
Toeplitz summability theorems proved that there exists a class of matrices including the Cesaro means with the same regularization property.
The regularization property and escape from the sub-optimal values have been demonstrated through computer simulations on two examples.
This work takes advantages of high-speed property of recursive regularization and improves the performance by imposing the proposed online scheme.
Hence, the proposed method is capable of enhancing the regularization property without getting stuck at sub-optimal values in search space.
The gradient of the performance function is evaluated by means of the improved adjoint variable method, which resolves the difficulty associated with the singularity of the adjoint equation through its inherent regularization property.
Supported by site surveys, this paper examines this state-dominated property formalization and regularization process, and the ways in which informalities have been replaced by a sharp value uplift shared between local governments and local peasants.
The same granulation mechanism delivers some highly advantageous regularization properties.
