A bi-level algorithm adapts scalarization weights online in vector-valued repeated games to obtain sublinear regret bounds and raise convergence to a preferred equilibrium from roughly 50% to 80%.
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Nonlinearly preconditioned gradient flows admit global solutions with sublinear or exponential convergence and are dual to mirror descent, solving an infinite-horizon optimal control problem whose value function is the Bregman divergence of the cost.
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Online Scalarization in Vector-Valued Games
A bi-level algorithm adapts scalarization weights online in vector-valued repeated games to obtain sublinear regret bounds and raise convergence to a preferred equilibrium from roughly 50% to 80%.
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Nonlinearly preconditioned gradient flows
Nonlinearly preconditioned gradient flows admit global solutions with sublinear or exponential convergence and are dual to mirror descent, solving an infinite-horizon optimal control problem whose value function is the Bregman divergence of the cost.