An elimination-based rejection-sampling algorithm with optimistic evaluators identifies target-feasible antichains in monotone co-design problems and propagates bounds compositionally through multigraphs.
Ehrgott,Multicriteria Optimization
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Compositional Online Learning for Multi-Objective System Co-Design
An elimination-based rejection-sampling algorithm with optimistic evaluators identifies target-feasible antichains in monotone co-design problems and propagates bounds compositionally through multigraphs.
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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%.