Improved Least-Squares Progressive Iterative Approximation for Tensor Product Surfaces

Geometric iterative methods, including progressive approximation and geometric interpolation are efficient for fitting a given data set. With the development of big technology, number points has become massive, least-squares (LSPIA) is generally applied to fit mass data. Combining Schulz method calculating Moore–Penrose generalized inverse matrix with traditional LSPIA method, this paper presents an accelerated tensor product surfaces shows that corresponding surface sequence converged The format non-stationary convergence rate increased rapidly as iteration increased. Some numerical examples provided illustrate proposed faster rate.