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A Primal-Dual Framework for Symmetric Cone Programming

Autor: Zheng, Jiaqi, Varvitsiotis, Antonios, Tan, Tiow-Seng, Lin, Wayne

In this paper, we introduce a primal-dual algorithmic framework for solving Symmetric Cone Programs (SCPs), a versatile optimization model that unifies and extends Linear, Second-Order Cone (SOCP), and Semidefinite Programming (SDP). Our work general

Externí odkaz: http://arxiv.org/abs/2405.09157

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Learning and steering game dynamics towards desirable outcomes

Autor: Canyakmaz, Ilayda, Sakos, Iosif, Lin, Wayne, Varvitsiotis, Antonios, Piliouras, Georgios

Game dynamics, which describe how agents' strategies evolve over time based on past interactions, can exhibit a variety of undesirable behaviours including convergence to suboptimal equilibria, cycling, and chaos. While central planners can employ in

Externí odkaz: http://arxiv.org/abs/2404.01066

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No-Regret Learning and Equilibrium Computation in Quantum Games

Autor: Lin, Wayne, Piliouras, Georgios, Sim, Ryann, Varvitsiotis, Antonios

Publikováno v: Quantum 8, 1569 (2024)

As quantum processors advance, the emergence of large-scale decentralized systems involving interacting quantum-enabled agents is on the horizon. Recent research efforts have explored quantum versions of Nash and correlated equilibria as solution con

Externí odkaz: http://arxiv.org/abs/2310.08473

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Multiplicative Updates for Online Convex Optimization over Symmetric Cones

Autor: Canyakmaz, Ilayda, Lin, Wayne, Piliouras, Georgios, Varvitsiotis, Antonios

We study online convex optimization where the possible actions are trace-one elements in a symmetric cone, generalizing the extensively-studied experts setup and its quantum counterpart. Symmetric cones provide a unifying framework for some of the mo

Externí odkaz: http://arxiv.org/abs/2307.03136

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Quantum Potential Games, Replicator Dynamics, and the Separability Problem

Autor: Lin, Wayne, Piliouras, Georgios, Sim, Ryann, Varvitsiotis, Antonios

Gamification is an emerging trend in the field of machine learning that presents a novel approach to solving optimization problems by transforming them into game-like scenarios. This paradigm shift allows for the development of robust, easily impleme

Externí odkaz: http://arxiv.org/abs/2302.04789

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