Semantic Scholar Open Access 2023 22 sitasi

Optimization Algorithm Synthesis Based on Integral Quadratic Constraints: A Tutorial

C. Scherer C. Ebenbauer Tobias Holicki

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Abstrak

We expose in a tutorial fashion the mechanisms which underlie the synthesis of optimization algorithms based on dynamic integral quadratic constraints. We reveal how these tools from robust control allow to design accelerated gradient descent algorithms with optimal guaranteed convergence rates by solving small-sized convex semi-definite programs. It is shown that this extends to the design of extremum controllers, with the goal to regulate the output of a general linear closed-loop system to the minimum of an objective function. Numerical experiments illustrate that we can not only recover gradient decent and the triple momentum variant of Nesterov's accelerated first order algorithm, but also automatically syn-thesize optimal algorithms even if the gradient information is passed through non-trivial dynamics, such as time-delays.

Topik & Kata Kunci

Computer Science Mathematics Engineering

Penulis (3)

C. Scherer

C. Ebenbauer

Tobias Holicki

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APA MLA BibTeX

Scherer, C., Ebenbauer, C., Holicki, T. (2023). Optimization Algorithm Synthesis Based on Integral Quadratic Constraints: A Tutorial. https://doi.org/10.1109/CDC49753.2023.10384198

Akses Cepat

Lihat di Sumber doi.org/10.1109/CDC49753.2023.10384198

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 22×
Sumber Database: Semantic Scholar
DOI: 10.1109/CDC49753.2023.10384198
Akses: Open Access ✓