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A scalable model-free actor--critic algorithm is proposed, establishing finite-sample critic and near-stationarity guarantees for a linear LSTD critic and a more expressive neural TD parameterization is introduced.
Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts'tacit knowledge, making them hard to formalize. As a result, optimization models often contain miscalibrated objectives, missing constraints, or omitted decision variables, leading to solu...
The proposed update incorporates the objective gradient inside the denoising step, yielding an inference-time method that uses only a pretrained denoiser and gradient evaluations and is analyzed as an inexact projected-gradient method for constrained optimization over learned feasible geometries.
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