Anytime-Feasible First-Order Optimization via Safe Sequential QCQP

TL;DR

This paper introduces a safe, first-order optimization algorithm for constrained nonconvex problems that guarantees feasibility at each step, achieves a convergence rate of O(1/t), and scales efficiently with an active-set variant, demonstrated on control problems.

Contribution

The paper develops the SS-QCQP algorithm, a novel safe first-order method with convergence guarantees and an active-set variant for improved scalability in constrained nonconvex optimization.

Findings

Maintains feasibility throughout optimization process.

Achieves O(1/t) convergence rate to stationary points.

Performs comparably to second-order solvers in experiments.

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 $O(1/t)$ convergence rate to first-order stationary points 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.