A Unified Funnel Restoration SQP Algorithm
- 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. 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. 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.
| Author: | David KießlingORCiD, Sven LeyfferORCiD, Charlie VanaretORCiD |
|---|---|
| Document Type: | Article |
| Parent Title (English): | Mathematical Programming B |
| Date of first Publication: | 2025/10/22 |
| DOI: | https://doi.org/10.1007/s10107-025-02284-3 |

