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. Dai, Yu-Hong; Liu, Xin-Wei; Sun, Jie: A primal-dual interior-point method capable of rapidly detecting infeasibility for nonlinear programs (2020)
  2. Liu, Xin-Wei; Dai, Yu-Hong: A globally convergent primal-dual interior-point relaxation method for nonlinear programs (2020)
  3. Macêdo, M. Joseane F. G.; Karas, Elizabeth W.; Costa, M. Fernanda P.; Rocha, Ana Maria A. C.: Filter-based stochastic algorithm for global optimization (2020)
  4. Kuhlmann, Renke: Learning to steer nonlinear interior-point methods (2019)
  5. Kuhlmann, Renke; Büskens, Christof: A primal-dual augmented Lagrangian penalty-interior-point filter line search algorithm (2018)
  6. Pei, Yong Gang; Zhu, De Tong: On the global convergence of a projective trust region algorithm for nonlinear equality constrained optimization (2018)
  7. Qiu, Songqiang; Chen, Zhongwen: An interior point method for nonlinear optimization with a quasi-tangential subproblem (2018)
  8. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  9. Curtis, Frank E.; Raghunathan, Arvind U.: Solving nearly-separable quadratic optimization problems as nonsmooth equations (2017)
  10. Esteban-Bravo, Mercedes; Leszkiewicz, Agata; Vidal-Sanz, Jose M.: Exact optimal experimental designs with constraints (2017)
  11. Pang, Lili; Zhu, Detong: A line search filter-SQP method with Lagrangian function for nonlinear inequality constrained optimization (2017)
  12. Kimiaei, Morteza; Esmaeili, Hamid: A trust-region approach with novel filter adaptive radius for system of nonlinear equations (2016)
  13. Shen, Chungen; Zhang, Lei-Hong; Yang, Wei Hong: A filter active-set algorithm for ball/sphere constrained optimization problem (2016)
  14. Gould, Nicholas I. M.; Loh, Yueling; Robinson, Daniel P.: A nonmonotone filter SQP method: local convergence and numerical results (2015)
  15. Huang, Mingxia; Pu, Dingguo: A line search SQP method without a penalty or a filter (2015)
  16. Huang, Mingxia; Pu, Dingguo: Line search SQP method with a flexible step acceptance procedure (2015)
  17. Liu, Weiai; Kong, Feng; Pu, Dingguo: An infeasible QP-free method without a penalty function for nonlinear inequality constrained optimization (2015)
  18. Schmidt, Martin: An interior-point method for nonlinear optimization problems with locatable and separable nonsmoothness (2015)
  19. Pei, Yonggang; Zhu, Detong: A trust-region algorithm combining line search filter method with Lagrange merit function for nonlinear constrained optimization (2014)
  20. 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)

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