A nonmonotone filter method for nonlinear optimization

Chungen Shen, Sven Leyffer, Roger Fletcher

    Research output: Contribution to journalArticlepeer-review

    32 Citations (Scopus)


    We propose a new nonmonotone filter method to promote global and fast local convergence for sequential quadratic programming algorithms. Our method uses two filters: a standard, global g-filter for global convergence, and a local nonmonotone l-filter that allows us to establish fast local convergence. We show how to switch between the two filters efficiently, and we prove global and superlinear local convergence. A special feature of the proposed method is that it does not require second-order correction steps. We present preliminary numerical results comparing our implementation with a classical filter SQP method.
    Original languageEnglish
    Pages (from-to)583-607
    Number of pages25
    JournalComputational Optimization and Applications
    Issue number3
    Publication statusPublished - Jul 2012


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