An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming

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 un...

Full description

Bibliographic Details
Main Authors: Hou, L., Qian, X., Liao, L.Z., Sun, Jie
Format: Journal Article
Language:English
Published: SPRINGER/PLENUM PUBLISHERS 2022
Subjects:
Online Access:http://purl.org/au-research/grants/arc/DP160102918
http://hdl.handle.net/20.500.11937/91422
_version_ 1848765516244582400
author Hou, L.
Qian, X.
Liao, L.Z.
Sun, Jie
author_facet Hou, L.
Qian, X.
Liao, L.Z.
Sun, Jie
author_sort Hou, L.
building Curtin Institutional Repository
collection Online Access
description 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.
first_indexed 2025-11-14T11:36:29Z
format Journal Article
id curtin-20.500.11937-91422
institution Curtin University Malaysia
institution_category Local University
language English
last_indexed 2025-11-14T11:36:29Z
publishDate 2022
publisher SPRINGER/PLENUM PUBLISHERS
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-914222023-05-04T06:21:25Z An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming Hou, L. Qian, X. Liao, L.Z. Sun, Jie Science & Technology Physical Sciences Mathematics, Applied Mathematics Interior point method Path following Polynomial-time complexity Convex programming POTENTIAL REDUCTION ALGORITHM SCALING CONTINUOUS TRAJECTORIES PREDICTOR-CORRECTOR ALGORITHM CONTINUATION-SMOOTHING METHOD POLYNOMIAL-TIME ALGORITHM PRIMAL-DUAL ALGORITHMS LIMITING BEHAVIOR CONVERGENCE COMPLEXITY 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/91422 10.1007/s10915-022-01765-3 English http://purl.org/au-research/grants/arc/DP160102918 SPRINGER/PLENUM PUBLISHERS restricted
spellingShingle Science & Technology
Physical Sciences
Mathematics, Applied
Mathematics
Interior point method
Path following
Polynomial-time complexity
Convex programming
POTENTIAL REDUCTION ALGORITHM
SCALING CONTINUOUS TRAJECTORIES
PREDICTOR-CORRECTOR ALGORITHM
CONTINUATION-SMOOTHING METHOD
POLYNOMIAL-TIME ALGORITHM
PRIMAL-DUAL ALGORITHMS
LIMITING BEHAVIOR
CONVERGENCE
COMPLEXITY
Hou, L.
Qian, X.
Liao, L.Z.
Sun, Jie
An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
title An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
title_full An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
title_fullStr An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
title_full_unstemmed An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
title_short An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming
title_sort interior point parameterized central path following algorithm for linearly constrained convex programming
topic Science & Technology
Physical Sciences
Mathematics, Applied
Mathematics
Interior point method
Path following
Polynomial-time complexity
Convex programming
POTENTIAL REDUCTION ALGORITHM
SCALING CONTINUOUS TRAJECTORIES
PREDICTOR-CORRECTOR ALGORITHM
CONTINUATION-SMOOTHING METHOD
POLYNOMIAL-TIME ALGORITHM
PRIMAL-DUAL ALGORITHMS
LIMITING BEHAVIOR
CONVERGENCE
COMPLEXITY
url http://purl.org/au-research/grants/arc/DP160102918
http://hdl.handle.net/20.500.11937/91422