An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.
Fsnet: Feasibility-seeking neural network for constrained optimization with guarantees.arXiv preprint arXiv:2506.00362
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SnareNet introduces a repair layer that navigates the range space of constraints plus adaptive relaxation training to enforce hard non-convex constraints on neural network outputs more reliably than prior methods.
Action aliasing from safety projections harms policy-gradient estimates more severely when the projection is inside the policy than when it is outside, but a penalty term restores competitiveness.
HardNet++ enforces general nonlinear constraints on neural network outputs through differentiable iterative damped linearizations, with convergence guarantees under regularity conditions.
LG-ND algorithm finds neural networks with up to 10x fewer neurons per layer that match baseline performance on ACOPF approximation for IEEE systems.
A transformer predicts unit commitment schedules over 72 hours, heuristics fix infeasibilities, and the output warm-starts a MILP solver to achieve 100% feasibility, faster runtimes, and lower costs in 20% of single-bus test cases.
A survey synthesizing stochastic, robust, and distributionally robust optimization methods for energy infrastructure planning under uncertainty while identifying gaps and machine learning opportunities.
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