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Received March 9, 2021; Accepted September 5, 2021; Published October 4, 2021
Abstract. This paper proposes a new full-Newton step infeasible interior-point algorithm based on a kernel function with linear growth terms for a linear programming. This kernel function determines search directions and the proximity measure between the iterates and the center path. Two types of full-Newton steps are used: feasibility steps and centering steps. By developing a new norm-based proximity measure and some new technical results, we derive the iteration bound that coincides with the currently best known iteration bound for the linear programming.
How to Cite this Article:
Mingwang Zhang, Jie Geng, Shan Wu, A new infeasible interior-point algorithm with full-Newton steps for linear programming based on a kernel function, Journal of Nonlinear Functional Analysis, Vol. 2021 (2021), Article ID 31, pp. 1-17.