TNT: A solver for large dense least-squares problems that takes conjugate gradient from bad in theory, to good in practice

Since its inception by Gauss, the least-squares problem has frequently arisen in science, mathematics, and engineering. Iterative methods, such as Conjugate Gradient Normal Residual (CGNR), have been popular for solving sparse least-squares problems, but have historically been regarded as undesirable for dense applications due to poor convergence. We contend that this traditional 'common knowledge' should be reexamined. Preconditioned CGNR, and perhaps other iterative methods, should be considered alongside standard methods when addressing large dense least-squares problems. In this paper we present TNT, a dynamite method for solving large dense least-squares problems. TNT implements a Cholesky preconditioner for the CGNR fast iterative method. The Cholesky factorization provides a preconditioner that, in the absence of round-off error, would yield convergence in a single iteration. Through this preconditioner and good parallel scaling, TNT provides improved performance over traditional least-squares solvers allowing for accelerated investigations of scientific and engineering problems. We compare a parallel implementation of TNT to parallel implementations of other conventional methods, including the normal equations and the QR method. For the small systems tested (15000 15000 or smaller), it is shown that TNT is capable of producing smaller solution errors and executing up to 16 faster than the other tested methods. We then apply TNT to a representative rock magnetism inversion problem where it yields the best solution accuracy and execution time of all tested methods.