4 papers
An Adaptive Cubic Regularisation Algorithm Based on Interior-Point Methods for Optimization with General Inequality Constraints
Yonggang Pei, Jingyi Guo, Detong Zhu
Nonlinear constrained optimization has a wide range of practical applications. The interior-point method is considered to be one of the most powerful algorithms for solving nonline…
A Second-Order Algorithm Based on Affine Scaling Interior-Point Methods for nonlinear Optimisation with bound constraints
Yonggang Pei, Yubing Lin, Mauricio Silva Louzeiro +1
The homogeneous second-order descent method (Zhang et al. 2025, Mathematics of Operations Research) was initially proposed for unconstrained optimisation problems. HSODM shows exce…
A scalable sequential adaptive cubic regularization algorithm for optimization with general equality constraints
Yonggang Pei, Yubing Lin, Shuai Shao +2
The scalable adaptive cubic regularization method (: Dussault et al. in Math. Program. Ser. A 207(1-2): 191-225, 2024) has been recently proposed for unconstrain…
A line search filter sequential adaptive cubic regularisation algorithm for nonlinearly constrained optimization
Yonggang Pei, Jingyi Wang, Shaofang Song +2
In this paper, a sequential adaptive regularization algorithm using cubics (ARC) is presented to solve nonlinear equality constrained optimization. It is motivated by the idea of h…