A unified funnel restoration SQP algorithm
提出一个统一的算法框架，整合多种非线性优化求解器，并基于漏斗方法控制约束违反，通过线搜索或信赖域实现全局收敛，在开源求解器中实现并测试。
Abstract 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 , 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 , and we provide extensive test results for the trust-region line-search funnel SQP on small instances.