In this work, we develop a fast hierarchical solver for solving large, sparse least squares problems. We build upon the algorithm, spaQR (sparsified QR Gnanasekaran and Darve in SIAM J Matrix Anal Appl 43(1):94–123, 2022), that was developed by authors to solve large linear systems. Our algorithm is built on top of Nested Dissection based multifrontal approach. use low-rank approximations front...

the main purpose of this article is to increase the efficiency of the least squares method in numerical solution of ill-posed functional and physical equations. determining the least squares of a given function in an arbitrary set is often an ill-posed problem. in this article, by defining artificial constraint and using lagrange multipliers method, the attempt is to turn -dimensional least squ...

When acquiring the surface data of the sample part, it is not easy to get the full data on one scan due to the configuration or topology of the part. In addition, some special parts have special assembly request, for example the joint surface smooth and the boundary share. The process usually requires multiple scans for an assembly model. Some identical points have different coordinate data bec...