TY - JOUR
T1 - An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
AU - Hou, Liangshao
AU - Qian, Xun
AU - Liao, Li Zhi
AU - Sun, Jie
N1 - KAUST Repository Item: Exported on 2022-04-26
Acknowledgements: The authors would like to thank the Associate Editor and one anonymous referee for their constructive comments and suggestions on the earlier version of this paper.
PY - 2022/2/8
Y1 - 2022/2/8
N2 - An interior point algorithm is proposed for linearly constrained convex programming following a parameterized central path, which is a generalization of the central path and requires weaker convergence conditions. The convergence and polynomial-time complexity of the proposed algorithm are proved under the assumption that the Hessian of the objective function is locally Lipschitz continuous. In addition, an initialization strategy is proposed and some numerical results are provided to show the efficiency and attractiveness of the proposed algorithm.
AB - An interior point algorithm is proposed for linearly constrained convex programming following a parameterized central path, which is a generalization of the central path and requires weaker convergence conditions. The convergence and polynomial-time complexity of the proposed algorithm are proved under the assumption that the Hessian of the objective function is locally Lipschitz continuous. In addition, an initialization strategy is proposed and some numerical results are provided to show the efficiency and attractiveness of the proposed algorithm.
UR - http://hdl.handle.net/10754/676538
UR - https://link.springer.com/10.1007/s10915-022-01765-3
UR - http://www.scopus.com/inward/record.url?scp=85124460907&partnerID=8YFLogxK
U2 - 10.1007/s10915-022-01765-3
DO - 10.1007/s10915-022-01765-3
M3 - Article
SN - 1573-7691
VL - 90
JO - Journal of Scientific Computing
JF - Journal of Scientific Computing
IS - 3
ER -