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Newton-Type Methods for Optimization and Variational Problems pp 367–437Cite as

Constrained Optimization: Globalization of Convergence

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Abstract

In this chapter we discuss approaches to globalizing the fundamental sequential quadratic programming (SQP) algorithm. This can be done in a number of ways. First, candidate points can be produced either using linesearch in the computed SQP direction or solving SQP subproblems with an additional trust-region constraint. Second, the generated candidate points can be evaluated either by computing the value of a suitable merit function or by a filter dominance criterion. The so-called elastic mode modification is also considered, in the context of the sequential quadratically constrained quadratic programming method.

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Authors and Affiliations

  1. Moscow State University, Moscow, Russia

    Alexey F. Izmailov

  2. Instituto de Matemática Pura e Aplicada, Rio de Janeiro, Brazil

    Mikhail V. Solodov

Authors
  1. Alexey F. Izmailov
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  2. Mikhail V. Solodov
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Izmailov, A.F., Solodov, M.V. (2014). Constrained Optimization: Globalization of Convergence. In: Newton-Type Methods for Optimization and Variational Problems. Springer Series in Operations Research and Financial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-04247-3_6

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