A globally convergent primal-dual interior-point filter method for nonlinear programming The paper proposes an algorithm which uses the filter technique of Fletcher and Leyffer to globalize the primal-dual interior-point method for nonlinear optimization, avoiding the use of merit functions and the updating of penalty parameters. This algorithm decomposes the primal-dual step obtained from the perturbed first-order necessary conditions into a normal and a tangential step, whose sizes are controlled by a trust-region type parameter. Each entry in the filter is a pair of coordinates: one resulting from feasibility and centrality, and associated with the normal step, the other resulting from optimality and related with the tangential step.

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  1. Kimiaei, Morteza; Esmaeili, Hamid: A trust-region approach with novel filter adaptive radius for system of nonlinear equations (2016)
  2. Shen, Chungen; Zhang, Lei-Hong; Yang, Wei Hong: A filter active-set algorithm for ball/sphere constrained optimization problem (2016)
  3. Gould, Nicholas I.M.; Loh, Yueling; Robinson, Daniel P.: A nonmonotone filter SQP method: local convergence and numerical results (2015)
  4. Huang, Mingxia; Pu, Dingguo: A line search SQP method without a penalty or a filter (2015)
  5. Huang, Mingxia; Pu, Dingguo: Line search SQP method with a flexible step acceptance procedure (2015)
  6. Liu, Weiai; Kong, Feng; Pu, Dingguo: An infeasible QP-free method without a penalty function for nonlinear inequality constrained optimization (2015)
  7. Schmidt, Martin: An interior-point method for nonlinear optimization problems with locatable and separable nonsmoothness (2015)
  8. Pei, Yonggang; Zhu, Detong: A trust-region algorithm combining line search filter method with Lagrange merit function for nonlinear constrained optimization (2014)
  9. Rocha, Ana Maria A.C.; Costa, M.Fernanda P.; Fernandes, Edite M.G.P.: A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues (2014)
  10. Agarwal, Anshul; Biegler, Lorenz T.: A trust-region framework for constrained optimization using reduced order modeling (2013)
  11. Gu, Chao: A dwindling filter inexact projected Hessian algorithm for large scale nonlinear constrained optimization (2013)
  12. Jin, Zhong: An improved nonmonotone filter trust region method for equality constrained optimization (2013)
  13. Periçaro, Gislaine A.; Ribeiro, Ademir A.; Karas, Elizabeth W.: Global convergence of a general filter algorithm based on an efficiency condition of the step (2013)
  14. Su, Ke; Liu, Wei; Lu, Xiaoli: Global convergence of a new nonmonotone filter method for equality constrained optimization (2013)
  15. Cai, Li; Zhu, Detong: A line search filter inexact SQP method for nonlinear equality constrained optimization (2012)
  16. Fatemi, M.; Mahdavi-Amiri, N.: A filter trust-region algorithm for unconstrained optimization with strong global convergence properties (2012)
  17. Jin, Zhong; Wang, Yuqing: A nonmonotone line search filter algorithm for the system of nonlinear equations (2012)
  18. Liu, Meiling; Li, Xueqian; Pu, Dingguo: A feasible filter SQP algorithm with global and local convergence (2012)
  19. Liu, Meiling; Li, Xueqian; Wu, Qinmin: A filter algorithm with inexact line search (2012)
  20. Qiu, Songqiang; Chen, Zhongwen: A new penalty-free-type algorithm based on trust region techniques (2012)

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