On the Global Convergence of a Projective Trust Region Algorithm for Nonlinear Equality Constrained Optimization

Pei, Yong Gang; Zhu, De Tong

doi:10.1007/s10114-018-7063-4

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2022

2023

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Cited by 4 publications

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“…We know that there have been many algorithms embedding filter techniques in line search or trust region framework to solve constrained optimization (e.g., [24,[30][31][32][33][34][35][36][37]). So one natural question is that whether filter techniques can be combined into ARC framework and how if it can be .…”

Section: Introductionmentioning

confidence: 99%

A filter sequential adaptive cubic regularisation algorithm for nonlinear constrained optimization

Pei

Song

Zhu

2022

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In this paper, we propose a filter sequential adaptive regularisation algorithm using cubics (ARC) for solving nonlinear equality constrained optimization. Similar to sequential quadratic programming methods, an ARC subproblem with linearized constraints is considered to obtain a trial step in each iteration. Composite step methods and reduced Hessian methods are employed to tackle the linearized constraints. As a result, a trial step is decomposed into the sum of a normal step and a tangential step which is computed by a standard ARC subproblem. Then, the new iteration is determined by filter methods and ARC framework. The global convergence of the algorithm is proved under some reasonable assumptions. Preliminary numerical experiments are reported.

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