An inexact infeasible arc-search interior-point method for linear optimization problems

Abstract We propose an inexact infeasible arc-search interior-point method (IPM) for solving linear optimization problems. The method combines an arc-search strategy with inexact solutions to Newton systems and admits an iteration complexity bound that is polynomial in the problem size. Compared with an existing inexact infeasible line-search IPM, the proposed IPM achieves a tighter worst-case iteration bound. Numerical experiments on NETLIB benchmark problems demonstrate that the proposed IPM reduces both the number of iterations and the computation time.