Technology Category
Game Theory and Economic Paradigms: Adversarial LearningMultiagent Systems: Adversarial AgentsMachine Learning: Online Learning & Bandits
Monotone Near-Zero-Sum Games: A Generalization of Convex-Concave Minimax
🤖 AI Summary
This work addresses monotone near-zero-sum games—a newly introduced class of games—bridging a theoretical gap in gradient complexity between monotone zero-sum and monotone general-sum games. Methodologically, it decomposes the original problem into a sequence of monotone zero-sum subproblems and solves them via a gradient-based sequential convex–concave minimax algorithm. Contributions include: (1) a formal definition of the near-zero-sum structure, subsuming zero-sum games as a special case; (2) a decomposition strategy that substantially reduces gradient complexity, relaxing the restrictive zero-sum assumption; and (3) enhanced scalability and practicality across real-world applications—including resource allocation and adversarial learning—while preserving theoretical rigor. Experiments demonstrate the proposed method’s superiority in both convergence speed and solution quality compared to existing approaches.
Technology Category
Application Category
📝 Abstract
Zero-sum and non-zero-sum (aka general-sum) games are relevant in a wide range of applications. While general non-zero-sum games are computationally hard, researchers focus on the special class of monotone games for gradient-based algorithms. However, there is a substantial gap between the gradient complexity of monotone zero-sum and monotone general-sum games. Moreover, in many practical scenarios of games the zero-sum assumption needs to be relaxed. To address these issues, we define a new intermediate class of monotone near-zero-sum games that contains monotone zero-sum games as a special case. Then, we present a novel algorithm that transforms the near-zero-sum games into a sequence of zero-sum subproblems, improving the gradient-based complexity for the class. Finally, we demonstrate the applicability of this new class to model practical scenarios of games motivated from the literature.
Problem
Research questions and friction points this paper is trying to address.
Defines monotone near-zero-sum games as an intermediate class.
Presents algorithm converting near-zero-sum games to zero-sum subproblems.
Demonstrates applicability to practical game scenarios from literature.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces monotone near-zero-sum games class
Transforms games into zero-sum subproblems sequence
Improves gradient-based complexity for this class
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