An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
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Authors
Hou, L.
Qian, X.
Liao, L.Z.
Sun, Jie
Date
2022Type
Journal Article
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Hou, L. and Qian, X. and Liao, L.Z. and Sun, J. 2022. An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming. Journal of Scientific Computing. 90 (3): ARTN 95.
Source Title
Journal of Scientific Computing
ISSN
Faculty
Faculty of Science and Engineering
School
School of Elec Eng, Comp and Math Sci (EECMS)
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Abstract
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.