RLSCG uses reinforcement learning to adaptively select stabilization parameters in column generation, substantially reducing iterations and runtime on cutting stock problems compared to fixed rules and other learning baselines.
Weak sharp minima in mathematical programming.SIAM Journal on Control and Optimization, 31(5):1340–1359, 1993
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Heat kernel regularization ensures the regularized Hessian stays asymptotically nondegenerate near nonsmooth minimizers of the form |x|^a, making the continuation equation locally solvable for small t.
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Learning to Control Stabilization in Column Generation
RLSCG uses reinforcement learning to adaptively select stabilization parameters in column generation, substantially reducing iterations and runtime on cutting stock problems compared to fixed rules and other learning baselines.
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From Nonsmooth Minima to Smooth Branches via Heat Kernel Regularization
Heat kernel regularization ensures the regularized Hessian stays asymptotically nondegenerate near nonsmooth minimizers of the form |x|^a, making the continuation equation locally solvable for small t.