Mathematical programming with equilibrium constraints
Author: Alexandra Rodriguez (ChE 345 Spring 2015) and Brandon Muncy (ChE345 Spring 2015)
Stewards: Dajun Yue and Fengqi You
Mathematical programming with equilibrium constraints (MPEC) is a type of nonlinear programming with constrained optimization. Constraints must satisfy an equilibrium condition, which can be an equilibrium inequality or a complimentarity condition, of which the simplest form is given by the critical point:
Therefore, an equilibrium constrained optimization model is given by:
MPEC plays a central role in the modeling of transportation problems, economics, and engineering design.
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.
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:
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:
As mentioned above, there are several applications of MPEC problems. Two published examples deal with economics and mathematical physics.
For this application, consider that n companies product the same product. We will introduce an integer variable y to denote the number of units a company will sell. We will further denote yi as the number of items that company i decides to sell. The total price of the product on the market will be notated P(T), where T = . The total cost of production for a company will be given by fi(yi). With this notation, the profit can then be given by the expression:
In this case, if company 1 were to product a product, their profitability would be based on the amount of other products in the market. Therefore, the portion of the profit that is a summation, is based on another optimization problem. This case is known as a bi-level problem.
 G.B. Allende. Mathematical programs with equilibrium constraints: solution techniques from parametric optimization (1977).
 M.C. Ferris, S.P. Dirkse, A. Meeraus. Mathematical programs with equilibrium constraints: automatic reformation and solution via constrained optimization. Northwestern University (2002).
 H. Pieper. Algorithms for mathematical programs with equilibrium constraints with applications to deregulated electricity markets. Stanford University (2001).
 R. Andreani, J.M. Martinez. On the solution of mathematical programming problems with equilibrium constraints (2008).