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Qian, X., Liao, L., Sun, J. and Zhu, H. (2018) The Convergent Generalized Central Paths for Linearly Constrained Convex Programming. SIAM Journal on Optimization, 28, 1183-1204.
https://doi.org/10.1137/16m1104172

has been cited by the following article:

• TITLE: A Modified Primal-Dual Interior Point Method for Solving Convex Quadratic Optimization Problems

  AUTHORS: John Avoka, Stephen B. Twum, Christian John Etwire

  KEYWORDS: Convex Quadratic Programming, Interior-Point Methods, Central Path, Lagrangian Function, Numerical Optimization

  JOURNAL NAME: Open Journal of Optimization, Vol.15 No.1, March 31, 2026

  ABSTRACT: This study presents a modified primal-dual interior point method (MPD-IPM) for solving convex quadratic optimization problems. The modification is performed through linearization of the central path and the introduction of an improved initialization strategy derived from the derivative structure of the Lagrangian function, unlike the classical approach, whose iterates follow a nonlinear trajectory. The proposed formulation generates linearized subsidiary constraint equations that reduce curvature effects during path-following. Numerical experiments conducted on benchmark and hypothetically generated problems demonstrate improved iteration counts, enhanced convergence reliability, and reduced computational time, particularly for large-scale instances.