Complexity of Minimizing Projected-Gradient-Dominated Functions with Stochastic First-order Oracles

• URL: http://arxiv.org/abs/2408.01839v1
• Date: Sat, 3 Aug 2024 18:34:23 GMT
• Title: Complexity of Minimizing Projected-Gradient-Dominated Functions with Stochastic First-order Oracles
• Authors: Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran,
• Abstract summary: This work investigates the performance limits of projected first-order methods for minimizing functions under the $(alpha,tau,mathcal)$-projected-dominance property.
• Score: 38.45952947660789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates the performance limits of projected stochastic first-order methods for minimizing functions under the $(\alpha,\tau,\mathcal{X})$-projected-gradient-dominance property, that asserts the sub-optimality gap $F(\mathbf{x})-\min_{\mathbf{x}'\in \mathcal{X}}F(\mathbf{x}')$ is upper-bounded by $\tau\cdot\|\mathcal{G}_{\eta,\mathcal{X}}(\mathbf{x})\|^{\alpha}$ for some $\alpha\in[1,2)$ and $\tau>0$ and $\mathcal{G}_{\eta,\mathcal{X}}(\mathbf{x})$ is the projected-gradient mapping with $\eta>0$ as a parameter. For non-convex functions, we show that the complexity lower bound of querying a batch smooth first-order stochastic oracle to obtain an $\epsilon$-global-optimum point is $\Omega(\epsilon^{-{2}/{\alpha}})$. Furthermore, we show that a projected variance-reduced first-order algorithm can obtain the upper complexity bound of $\mathcal{O}(\epsilon^{-{2}/{\alpha}})$, matching the lower bound. For convex functions, we establish a complexity lower bound of $\Omega(\log(1/\epsilon)\cdot\epsilon^{-{2}/{\alpha}})$ for minimizing functions under a local version of gradient-dominance property, which also matches the upper complexity bound of accelerated stochastic subgradient methods.

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