A myopic MINMPC framework learns a value function offline via inverse optimization from expert data, allowing short horizons with near-optimal performance and strict integer feasibility online for hybrid systems.
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PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.
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Learning myopic mixed-integer nonlinear model predictive control from expert demonstrations
A myopic MINMPC framework learns a value function offline via inverse optimization from expert data, allowing short horizons with near-optimal performance and strict integer feasibility online for hybrid systems.
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Decision-Focused Learning via Tangent-Space Projection of Prediction Error
PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.