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A privacy-preserving distributed computational approach for distributed locational marginal prices
An important issue in today's electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles.
Training Generative Adversarial Networks via stochastic Nash games
We also show convergence of an averaged variant of the SRFB algorithm to a neighborhood of the solution when only few samples are available.
A game-theoretic approach for Generative Adversarial Networks
Since the training can be cast as a stochastic Nash equilibrium problem, we rewrite it as a variational inequality and introduce an algorithm to compute an approximate solution.
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