Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Varvitsiotis, Antonios"'
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
The dynamic behavior of agents in games, which captures how their strategies evolve over time based on past interactions, can lead to a spectrum of undesirable behaviors, ranging from non-convergence to Nash equilibria to the emergence of limit cycle
Externí odkaz:
http://arxiv.org/abs/2404.01066
Network games are an important class of games that model agent interactions in networked systems, where players are situated at the nodes of a graph and their payoffs depend on the actions taken by their neighbors. We extend the classical framework b
Externí odkaz:
http://arxiv.org/abs/2310.20333
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
The problems of computing graph colorings and clique covers are central challenges in combinatorial optimization. Both of these are known to be NP-hard, and thus computationally intractable in the worst-case instance. A prominent approach for computi
Externí odkaz:
http://arxiv.org/abs/2310.00257
We consider a class of games between two competing players that take turns acting on the same many-body quantum register. Each player can perform unitary operations on the register, and after each one of them acts on the register the energy is measur
Externí odkaz:
http://arxiv.org/abs/2308.09673
Decentralized learning algorithms are an essential tool for designing multi-agent systems, as they enable agents to autonomously learn from their experience and past interactions. In this work, we propose a theoretical and algorithmic framework for r
Externí odkaz:
http://arxiv.org/abs/2307.06640
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
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
Given a matrix $X\in \mathbb{R}^{m\times n}_+$ with non-negative entries, the cone factorization problem over a cone $\mathcal{K}\subseteq \mathbb{R}^k$ concerns computing $\{ a_1,\ldots, a_{m} \} \subseteq \mathcal{K}$ and $\{ b_1,\ldots, b_{n} \} \
Externí odkaz:
http://arxiv.org/abs/2108.00740