Mirror Frameworks for Relatively Lipschitz and Monotone-Like Variational Inequalities

نویسندگان

چکیده

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 extragradient method and extrapolation method, approximating solutions to relatively Lipschitz monotone-like VIs. The former covers the well-known Nemirovski’s prox Nesterov’s dual recently proposed Bregman method; all them can be reformulated into a scheme that is very similar original form method. latter includes operator as its special cases. frameworks allow us present unified improved convergence analysis these existing methods under relative Lipschitzness conditions may currently weakest assumptions guaranteeing (sub)linear convergence.

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ژورنال

عنوان ژورنال: Journal of the Operations Research Society of China

سال: 2023

ISSN: ['2194-668X', '2194-6698']

DOI: https://doi.org/10.1007/s40305-023-00458-4