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.
Generalized equations and their solutions, part ii: Applications to nonlinear programming
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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.