The notes explain the failure of classical NLP optimality conditions for MPECs and outline multiplier-based, implicit-programming, and piecewise-programming viewpoints with emphasis on critical cones and strong regularity.
A branch-and-cut algorithm for mixed-integer bi- linear programming.European Journal of Operational Research, 282(2):506–514, 2020
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Optimization Workshop Notes for Mathematical Programming with Equilibrium Constraints (MPECs): Second-Order Optimality Conditions
The notes explain the failure of classical NLP optimality conditions for MPECs and outline multiplier-based, implicit-programming, and piecewise-programming viewpoints with emphasis on critical cones and strong regularity.