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  <doc>
    <id>10151</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-10-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Unified Funnel Restoration SQP Algorithm</title>
    <abstract language="eng">We consider nonlinearly constrained optimization problems and discuss a generic double-loop framework consisting of basic algorithmic ingredients that unifies a broad range of nonlinear optimization solvers. This framework has been implemented in the open-source solver Uno, a Swiss Army knife-like C++ optimization framework that unifies many nonlinearly constrained nonconvex optimization solvers.&#13;
We illustrate the framework with a sequential quadratic programming (SQP) algorithm that maintains an acceptable upper bound on the constraint violation, called a funnel, that is monotonically decreased to control the feasibility of the iterates. Infeasible quadratic subproblems are handled by a feasibility restoration strategy. Globalization is controlled by a line search or a trust-region method.&#13;
We prove global convergence of the trust-region funnel SQP method, building on known results from filter methods. We implement the algorithm in Uno, and we provide extensive test results for the trust-region line-search funnel SQP on small CUTEst instances.</abstract>
    <parentTitle language="eng">Mathematical Programming B</parentTitle>
    <identifier type="doi">10.1007/s10107-025-02284-3</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2025-09-13</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>David Kießling</author>
    <submitter>Charlie Vanaret</submitter>
    <author>Sven Leyffer</author>
    <author>Charlie Vanaret</author>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="persons" number="vanaret">Vanaret, Charlie William</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
