Constrained least squares problems arise in many applications. Their memory and computation costs are expensive practice involving high-dimensional input data. We employ the so-called "sketching" strategy to project problem onto a space of much lower "sketching dimension" via random sketching matrix. The key idea is reduce dimension as possible while maintaining approximation accuracy. Tensor s...

Several extensions of the total least squares (TIS) method that are able to calculate a structured rank deficient approximation of a data matrix have been developed recently. The main result of this correspondence is the demonstration of the equivalence of two of these approaches, namely, the constrained total least squares (CTLS) approach and the structured total least squares (STLS) approach....

Based on constrained least squares approximation and techniques from computational geometry, this paper shall provide a new approximation scheme which achieves to combine well-known features from radial basis function interpolation with recent developments concerning scattered data filtering. The suggested method is shown to be well-posed, and moreover by using local error estimates from radial...

We presented the basic concepts for peakconstrained least-squares (PCLS) optimization in previous papers. We present advanced PCLS optimization concepts in this paper.