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@PHDTHESIS{Jungen:1023990,
      author       = {Jungen, Daniel},
      othercontributors = {Mitsos, Alexander and Muñoz, Diego Alejandro},
      title        = {{D}eterministic global and hierarchical optimization for
                      experimental design},
      volume       = {44},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-10903},
      series       = {Aachener Verfahrenstechnik series - AVT.SVT - Process
                      systems engineering},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2026; Dissertation, Rheinisch-Westfälische
                      Technische Hochschule Aachen, 2025},
      abstract     = {In this dissertation, hierarchical optimization is applied
                      in optimal experimental design for guaranteed parameter
                      estimation, and the applicability and accessibility of
                      hierarchical optimization methods are improved. Furthermore,
                      we analyze the advantages and disadvantages of using global
                      optimization in Bayesian optimization. Bayesian optimization
                      using Gaussian processes as a surrogate model has been
                      effectively used to optimize expensive to evaluate
                      black-box-functions. In this context, we investigate the
                      advantages and disadvantages of using deterministic global
                      optimization for optimizing the acquisition function, by
                      comparing the Bayesian optimization performance when using a
                      deterministic global solver with two conventional solvers
                      across four different test functions. In contrast to
                      Bayesian optimization, optimal experimental design for
                      guaranteed parameter estimation requires knowledge of the
                      underlying model as an equation system. These models usually
                      include parameters that must be estimated, and naturally,
                      the parameter values are inherently uncertain. We extend the
                      formulation of optimal experimental design for guaranteed
                      parameter estimation to systems whose input-output relation
                      is implicitly defined through an optimization problem, which
                      corresponds in our application to the rigorous computation
                      of liquid-liquid equilibria. The resulting optimization
                      problem of the latter approach is a hierarchical program
                      that is generally challenging to solve. Although multiple
                      adaptive discretization-based methods for their solution
                      have been proposed in recent decades, their numerical
                      comparison is lacking. We remedy this by presenting an
                      open-source software for solving multiple hierarchical
                      optimization programs. Our software includes an extensive
                      library of test problems, which we compiled from existing
                      benchmark libraries and other problems in the literature.
                      Utilizing our software and library of test problems, we
                      compare multiple adaptive discretization-based solvers and
                      conduct parameter tuning for the algorithmic parameters
                      associated with each solver. On this basis, we propose an
                      adaptation of an existing adaptive discretization-based
                      method for solving bilevel programs, implement it in our
                      software, and compare it to existing solvers, including the
                      original approach. For the above-mentioned extended
                      formulation for optimal experimental design for guaranteed
                      parameter estimation, we present a specialized solution
                      algorithm and provide a proof-of-concept, demonstrating the
                      feasibility of our method. In many applications, including
                      optimal experimental design for guaranteed parameter
                      estimation, the necessary assumptions of the employed
                      hierarchical optimization solvers may be violated.
                      Therefore, we examine the necessary assumptions of an
                      existing semi-infinite optimization method, and show that
                      slightly weaker assumptions are possible. As many adaptive
                      discretization-based solution algorithms have the examined
                      solution algorithm as their predecessor, it can be presumed
                      that the convergence guarantees shown with the more precise,
                      slightly weaker assumptions are directly transferable from
                      one algorithm to the other.},
      cin          = {416710},
      ddc          = {620},
      cid          = {$I:(DE-82)416710_20140620$},
      pnm          = {DFG project G:(GEPRIS)390919832 - EXC 2186: Das Fuel
                      Science Center – Adaptive Umwandlungssysteme für
                      erneuerbare Energie- und Kohlenstoffquellen (390919832)},
      pid          = {G:(GEPRIS)390919832},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-10903},
      url          = {https://publications.rwth-aachen.de/record/1023990},
}