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.
Lecture note for bounded controls in continuous-time and control of several variables.arXiv e-prints, pages arXiv–2604
2 Pith papers cite this work. Polarity classification is still indexing.
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Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.
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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.
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Optimization Workshop Notes for Mathematical Programming with Equilibrium Constraints (MPECs): Verification of MPEC Hypotheses
Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.