Anytime-Feasible First-Order Optimization via Safe Sequential QCQP

Abstract:This paper presents the Safe Sequential Quadratically Constrained Quadratic Programming (SS-QCQP) algorithm, a first-order method for smooth inequality-constrained nonconvex optimization that guarantees feasibility at every iteration. The method is derived from a continuous-time dynamical system whose vector field is obtained by solving a convex QCQP that enforces monotonic descent of the objective and forward invariance of the feasible set. The resulting continuous-time dynamics achieve an $O(1/t)$ ergodic convergence rate for the stationarity measure under standard constraint qualification conditions. We then propose a safeguarded Euler discretization with adaptive step-size selection that preserves this convergence rate while maintaining both descent and feasibility in discrete time. To enhance scalability, we develop an active-set variant (SS-QCQP-AS) that selectively enforces constraints near the boundary, substantially reducing computational cost without compromising theoretical guarantees. Numerical experiments on a multi-agent nonlinear optimal control problem demonstrate that SS-QCQP and SS-QCQP-AS maintain feasibility, exhibit the predicted convergence behavior, and deliver solution quality comparable to second-order solvers such as SQP and IPOPT.

Optimization and Control (math.OC); Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2511.19675 [math.OC]

(or arXiv:2511.19675v2 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2511.19675 Focus to learn more arXiv-issued DOI via DataCite

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