Anderson Acceleration for Linearly Converging SQP-Type Methods
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918450557878272 |
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| author | Frey, Jonathan Kiessling, David Baumgärtner, Katrin Diehl, Moritz |
| author_facet | Frey, Jonathan Kiessling, David Baumgärtner, Katrin Diehl, Moritz |
| contents | Although Anderson acceleration (AA) is known to speed up fixed-point iterations, it is rarely applied in constrained optimization, in particular sequential quadratic programming (SQP). We show that the local convergence behavior of a general family of (inexact) SQP-type methods can benefit from AA and introduce a simple heuristic to alleviate slower convergence farther from the solution. The method is implemented in the software framework acados. Numerical examples from optimal control illustrate consistent improvements in convergence of different SQP-type methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14803 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Anderson Acceleration for Linearly Converging SQP-Type Methods Frey, Jonathan Kiessling, David Baumgärtner, Katrin Diehl, Moritz Optimization and Control Although Anderson acceleration (AA) is known to speed up fixed-point iterations, it is rarely applied in constrained optimization, in particular sequential quadratic programming (SQP). We show that the local convergence behavior of a general family of (inexact) SQP-type methods can benefit from AA and introduce a simple heuristic to alleviate slower convergence farther from the solution. The method is implemented in the software framework acados. Numerical examples from optimal control illustrate consistent improvements in convergence of different SQP-type methods. |
| title | Anderson Acceleration for Linearly Converging SQP-Type Methods |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2604.14803 |