A geometric characterization of the tangent cone to feasible points in MPECs yields stationarity concepts and constraint qualifications that avoid the strong nondegeneracy and smoothness assumptions required by classical nonlinear programming approaches.
In: 2023 62nd IEEE Conference on Decision and Control (CDC), pp
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
math.OC 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Exact penalization for MPECs is enabled under broader conditions by fractional-order penalties derived from Lojasiewicz error bounds on KKT residual mappings.
An MPEC is an optimization problem whose feasible set is partly defined by another optimization, variational inequality, complementarity system, or equilibrium model.
citing papers explorer
-
First-Order Optimality Conditions for Mathematical Programming with Equilibrium Constraints
A geometric characterization of the tangent cone to feasible points in MPECs yields stationarity concepts and constraint qualifications that avoid the strong nondegeneracy and smoothness assumptions required by classical nonlinear programming approaches.
-
Introduction to Exact Penalization for Mathematical Programming with Equilibrium Constraints
Exact penalization for MPECs is enabled under broader conditions by fractional-order penalties derived from Lojasiewicz error bounds on KKT residual mappings.
-
Introduction to Mathematical Programming with Equilibrium Constraints (MPECs) and Bilevel Optimization
An MPEC is an optimization problem whose feasible set is partly defined by another optimization, variational inequality, complementarity system, or equilibrium model.