A Restart-Free Accelerated Algorithm for Non-Convex Minimization: Continuous and Discrete Analysis

We propose two novel first-order methods for minimizing nonconvex functions with Lipschitz-continuous gradients and Hessians.

These algorithms attain an $\varepsilon$-approximate first-order stationary point in $\mathrm{O}(\varepsilon^{-7/4})$ function and gradient evaluations, without using $\varepsilon$ as an input parameter.

While existing methods rely on restart mechanisms to achieve this complexity, our methods do not.

Consequently, the first algorithm enjoys a simple implementation, making its last iterate differentiable with respect to the initial point.

By estimating the Lipschitz constants adaptively, we develop the second algorithm that does not require prior knowledge of the constants.

This algorithm exhibits better numerical performance than existing parameter-free methods for certain problems, which can be attributed to its restart-free design.

Both algorithms are derived by discretizing a newly introduced continuous-time model represented by an ordinary differential equation, and their continuous- and discrete-time convergence analyses proceed in a parallel manner under the Performance Estimation Problem framework.