An interior-point algorithm for semidefinite least-squares problems
We propose a feasible primal-dual path-following interior-point algorithm for semidefinite least squares problems (SDLS). At each iteration, the algorithm uses only full Nesterov-Todd steps with the advantage that no line search is required. Under new appropriate choices of the parameter which defines the size of the neighborhood of the central-path and of the parameter which determines the rate of decrease of the barrier parameter, we show that the proposed algorithm is well defined and converges...