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Mirror Frameworks for Relatively Lipschitz and Monotone-Like Variational Inequalities

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Abstract

Nonconvex–nonconcave saddle-point optimization in machine learning has triggered lots of research for studying non-monotone variational inequalities (VIs). In this work, we introduce two mirror frameworks, called mirror extragradient method and mirror extrapolation method, for approximating solutions to relatively Lipschitz and monotone-like VIs. The former covers the well-known Nemirovski’s mirror prox method and Nesterov’s dual extrapolation method, and the recently proposed Bregman extragradient method; all of them can be reformulated into a scheme that is very similar to the original form of extragradient method. The latter includes the operator extrapolation method and the Bregman extrapolation method as its special cases. The proposed mirror frameworks allow us to present a unified and improved convergence analysis for all these existing methods under relative Lipschitzness and monotone-like conditions that may be the currently weakest assumptions guaranteeing (sub)linear convergence.

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Acknowledgements

The authors wish to express their thanks to the anonymous referees and the associate editor for several helpful comments, which allowed us to improve the original presentation.

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Authors and Affiliations

Authors

Contributions

H. Zhang contributed to methodology and writing-original draft; Y.-H. Dai contributed to conceptualization, methodology, supervision, writing-review and editing

Corresponding author

Correspondence to Hui Zhang.

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The authors declare that they have no conflict of interest.

Additional information

The first author was supported by the National Natural Science Foundation of China (No. 11971480), the Natural Science Fund of Hunan for Excellent Youth (No. 2020JJ3038), and the Fund for NUDT Young Innovator Awards (No. 20190105). The second author was supported by the National Natural Science Foundation of China (Nos. 11991020, 11631013, 12021001, 11971372 and 11991021) and the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA27000000).

Appendix

Appendix

Proof of Lemma 3

Fix \(u, v, z\in {\mathbb {R}}^d\). By Cauchy-Schwartz inequality, Lipschitzness of F, and strong convexity of \(\omega \), we derive that for any \(u^*\in \partial \omega (u), v^*\in \partial \omega (v)\),

$$\begin{aligned}{} & {} \qquad \langle F(v)-F(u),v-z\rangle \\{} & {} \quad \leqslant \Vert F(v)-F(u)\Vert _*\Vert z-v\Vert \leqslant L\Vert v-u\Vert \Vert z-v\Vert \\{} & {} \quad \leqslant L(\frac{1}{2}\Vert v-u\Vert ^2 +\frac{1}{2}\Vert z-v\Vert ^2) \leqslant \frac{L}{\mu }(D_\omega ^{u^*}(v,u)+ D_\omega ^{v^*}(z,v)) \end{aligned}$$

from which the result follows.

Proof of Lemma 4

Fix \(u, v, z\in {\mathbb {R}}^d\). By the definition Bregman distance and relative smoothness of \(\phi \), we derive that for any \(u^*\in \partial \omega (u), v^*\in \partial \omega (v)\),

$$\begin{aligned}{} & {} \,\quad L(D_\omega ^{u^*}(v,u)+ D_\omega ^{v^*}(z,v))) \\{} & {} \geqslant \phi (v)-[\phi (u)+\langle \nabla \phi (u),v-u\rangle ]+ \phi (z)-[\phi (v)+\langle \nabla \phi (v), z-v\rangle ]\\{} & {} = \phi (z)-\phi (u)-\langle \nabla \phi (u),v-u\rangle - \langle \nabla \phi (v), z-v\rangle \\{} & {} = D_\phi (z,u)+\langle \nabla \phi (u),z-u\rangle -\langle \nabla \phi (u),v-u\rangle - \langle \nabla \phi (v),z-v\rangle \\{} & {} = D_\phi (z,u)+\langle \nabla \phi (v)-\nabla \phi (u),v-z\rangle . \end{aligned}$$

Note that \(F=\nabla \phi \) and \(D_\phi (z,u)\geqslant 0\) due to the convexity of \(\phi \). The conclusion follows.

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Zhang, H., Dai, YH. Mirror Frameworks for Relatively Lipschitz and Monotone-Like Variational Inequalities. J. Oper. Res. Soc. China (2023). https://doi.org/10.1007/s40305-023-00458-4

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