Optimization methods

Development of nonlinear optimization algorithms.

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Overview

We design and analyze algorithms for nonlinear and/or decomposed optimization for control and verification of complex cyber-physical systems. In particular, we focus on nonlinear sum-of-squares problems and bilevel optimization schemes.

Sequential sum-of-squares programming outperforms previous, iterative methods in computation time and cost. ©2022 by Cunis & Legat.

Publikationen aus dem Bereich Optimization methods

  1. T. Cunis and I. Kolmanovsky, “Input-to-State Stability of Newton Methods for Generalized Equations in Nonlinear Optimization⋆,” in 2024 IEEE 63rd Conference on Decision and Control (CDC), 2024, pp. 5950–5956. doi: 10.1109/CDC56724.2024.10885904.
  2. T. Cunis, “Decomposed quasiconvex optimization with application to generalized cone problems,” Optimization Letters, 2024, doi: 10.1007/s11590-024-02174-1.
  3. T. Cunis, “Source code and Numerical Examples for Decomposed Quasiconvex Optimization with Application to Generalized Cone Problems,” 2024, doi: 10.18419/darus-4024.
  4. T. Cunis and B. Legat, “Sequential sum-of-squares programming for analysis of nonlinear systems,” in 2023 American Control Conference, San Diego, CA, 2023. doi: 10.23919/ACC55779.2023.10156153.
  5. T. Cunis and I. Kolmanovsky, “Input-to-State Stability of a Bilevel Proximal Gradient Descent Algorithm,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.633.
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