This work proposes a new algorithm – the Single-timescale Double-momentum
Stochastic Approximation (SUSTAIN) –for tackling stochastic unconstrained
bilevel optimization problems. We focus on bilevel problems where the lower level
subproblem is strongly-convex and the upper level objective function is smooth.
Unlike prior works which rely on two-timescale or double loop techniques, we
design a stochastic momentum-assisted gradient estimator for both the upper and
lower level updates. The latter allows us to control the error in the stochastic gradient updates due to inaccurate solution to both subproblems. If the upper objective function is smooth but possibly non-convex, we show that SUSTAIN requires ${O}(\epsilon^{-3/2})$
iterations (each using $O(1)$ samples) to find an $\epsilon$-stationary solution. The $\epsilon$-stationary solution is defined as the point whose squared norm of the gradient of the outer function is less than or equal to $\epsilon$. The total number of stochastic gradient samples required for the upper and lower level objective functions match the best-known complexity for single-level stochastic gradient algorithms. We also analyze the case when the upper level objective function is strongly-convex.
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This content will become publicly available on February 13, 2025
Achieving O(\epsilon^{-1.5}) Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
In this paper, we revisit the bilevel optimization problem, in which the upper-level objective function is generally nonconvex and the lower-level objective function is strongly convex. Although this type of problem has been studied extensively, it still remains an open question how to achieve an $\mathcal{O}(\epsilon^{-1.5})$ sample complexity in Hessian/Jacobian-free stochastic bilevel optimization without any second-order derivative computation. To fill this gap, we propose a novel Hessian/Jacobian-free bilevel optimizer named FdeHBO, which features a simple fully single-loop structure, a projection-aided finite-difference Hessian/Jacobian-vector approximation, and momentum-based updates. Theoretically, we show that FdeHBO requires $\mathcal{O}(\epsilon^{-1.5})$ iterations (each using $\mathcal{O}(1)$ samples and only first-order gradient information) to find an $\epsilon$-accurate stationary point. As far as we know, this is the first Hessian/Jacobian-free method with an $\mathcal{O}(\epsilon^{-1.5})$ sample complexity for nonconvex-strongly-convex stochastic bilevel optimization.
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- Award ID(s):
- 2311274
- PAR ID:
- 10505562
- Publisher / Repository:
- Advances in Neural Information Processing Systems
- Date Published:
- Format(s):
- Medium: X
- Location:
- Advances in Neural Information Processing Systems
- Sponsoring Org:
- National Science Foundation
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