Tootfinder

Complexity guarantees for risk-neutral generalized Nash equilibrium problems
Haochen Tao, Andrea Iannelli, Meggie Marschner, Mathias Staudigl, Uday V. Shanbhag, Shisheng Cui
https://arxiv.org/abs/2506.11409

Complexity guarantees for risk-neutral generalized Nash equilibrium problems
In this paper, we address \ac{SGNEP} seeking with risk-neutral agents. Our main contribution lies the development of a stochastic variance-reduced gradient (SVRG) technique, modified to contend with general sample spaces, within a stochastic forward-backward-forward splitting scheme for resolving structured monotone inclusion problems. This stochastic scheme is a double-loop method, in which the mini-batch gradient estimator is computed periodically in the outer loop, while only cheap sampling …

ASMOP: Additional sampling stochastic trust region method for multi-objective problems
Nata\v{s}a Krklec Jerinki\'c, Luka Rute\v{s}i\'c
https://arxiv.org/abs/2506.10976

ASMOP: Additional sampling stochastic trust region method for multi-objective problems
We consider an unconstrained multi-criteria optimization problem with finite sum objective functions. The proposed algorithm belongs to a non-monotone trust-region framework where additional sampling approach is used to govern the sample size and the acceptance of a candidate point. Depending on the problem, the method can result in a mini-batch or an increasing sample size approach. Therefore, this work can be viewed as an extension of additional sampling trust region method for scalar finite …

This https://arxiv.org/abs/2401.06738 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum
Stochastic heavy ball momentum (SHB) is commonly used to train machine learning models, and often provides empirical improvements over stochastic gradient descent. By primarily focusing on strongly-convex quadratics, we aim to better understand the theoretical advantage of SHB and subsequently improve the method. For strongly-convex quadratics, Kidambi et al. (2018) show that SHB (with a mini-batch of size $1$) cannot attain accelerated convergence, and hence has no theoretical benefit over SGD…