# Mathematical programming with equilibrium constraints

## Contents |

# Introduction

Mathematical programming with equilibrium constraints (MPEC) is a type of nonlinear programming with constrained optimization. Constraints must satisfy an equilibrium condition, of which the simplest form is given by the critical point:

φ

Therefore, an equilibrium constrained optimization model is given by:

φ

φ

MPEC plays a central role in the modeling of transportation problems, economics, and engineering design.

# Problem formulation

φ

φ

## Feasible set

For a feasible set, the conditions for convexity and closedness are as follows. If Y(x) is convex, and functions f, g, and ∅ are concave, then the mathematical program is convex. Furthermore, if the Mangasarian-Fromovitz constraint qualification holds at all z ∈ Y(x), then Y(x) is the lower semi-continuous bound, and the mathematical program is closed.

## KKT transformation

### Complementarity constrained optimization

By applying the Karush-Kuhn-Tucker (KKT) approach to solving an equilibrium constraint problem (EC), a program with complementarity constraints can be obtained (CC):

The complementarity constraints can be written equivalently as:

perp-to

A mathematical program with complementarity constraints (MPCC) is a relaxed MPEC.

### Linear constrained optimization

The KKT approach may also lead to an MPCC with only linear functions:

&lamda;

λ

# Conclusion

# References

[1] G.B. Allende. Mathematical programs with equilibrium constraints: solution techniques from parametric optimization (1977).

[2] M.C. Ferris, S.P. Dirkse, A. Meeraus. Mathematical programs with equilibrium constraints: automatic reformation and solution via constrained optimization. Northwestern University (2002).

[3] H. Pieper. Algorithms for mathematical programs with equilibrium constraints with applications to deregulated electricity markets. Stanford University (2001).

[4] R. Andreani, J.M. Martinez. On the solution of mathematical programming problems with equilibrium constraints (2008).