A Unified Funnel Restoration SQP Algorithm
<|reference_start|>A Unified Funnel Restoration SQP Algorithm: We consider nonlinearly constrained optimization problems and discuss a generic double-loop framework consisting of four 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.<|reference_end|>
@article{kiessling2024a,
    title={A Unified Funnel Restoration SQP Algorithm},
    author={David Kiessling, Sven Leyffer, Charlie Vanaret},
    journal={arXiv preprint arXiv:2409.09208},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09208},
    primaryClass={math.OC cs.MS}
}