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.
Geometric characterizations of lipschitz stability for convex optimization problems.SIAM Journal on Optimization, 35(2):927–958
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.
citing papers explorer
-
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.
-
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.