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12
Interior methods for mathematical programs with complementarity constraints
 SIAM J. Optim
, 2004
"... This paper studies theoretical and practical properties of interiorpenalty methods for mathematical programs with complementarity constraints. A framework for implementing these methods is presented, and the need for adaptive penalty update strategies is motivated with examples. The algorithm is sh ..."
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Cited by 22 (8 self)
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This paper studies theoretical and practical properties of interiorpenalty methods for mathematical programs with complementarity constraints. A framework for implementing these methods is presented, and the need for adaptive penalty update strategies is motivated with examples. The algorithm is shown to be globally convergent to strongly stationary points, under standard assumptions. These results are then extended to an interiorrelaxation approach. Superlinear convergence to strongly stationary points is also established. Two strategies for updating the penalty parameter are proposed, and their efficiency and robustness are studied on an extensive collection of test problems.
Elasticmode algorithms for mathematical programs with equilibrium constraints: global convergence and stationarity properties
 Math. Program
, 2005
"... Abstract. The elasticmode formulation of the problem of minimizing a nonlinear function subject to equilibrium constraints has appealing local properties in that, for a finite value of the penalty parameter, local solutions satisfying first and secondorder necessary optimality conditions for the ..."
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Cited by 11 (1 self)
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Abstract. The elasticmode formulation of the problem of minimizing a nonlinear function subject to equilibrium constraints has appealing local properties in that, for a finite value of the penalty parameter, local solutions satisfying first and secondorder necessary optimality conditions for the original problem are also first and secondorder points of the elasticmode formulation. Here we study global convergence properties of methods based on this formulation, which involve generating an (exact or inexact) first or secondorder point of the formulation, for nondecreasing values of the penalty parameter. Under certain regularity conditions on the active constraints, we establish finite or asymptotic convergence to points having a certain stationarity property (such as strong stationarity, Mstationarity, or Cstationarity). Numerical experience with these approaches is discussed. In particular, our analysis and the numerical evidence show that exact complementarity can be achieved finitely even when the elasticmode formulation is solved inexactly. Key words. Nonlinear programming, equilibrium constraints, complementarity constraints, elasticmode formulation, strong stationarity, Cstationarity, Mstationarity. AMS subject classifications 49M30, 49M37, 65K05, 90C30, 90C33 1.
Complementarity constraints as nonlinear equations: Theory and numerical experience
 Preprint ANL/MCSP10540603, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne
, 2003
"... Recently, it has been shown that mathematical programs with complementarity constraints (MPCCs) can be solved efficiently and reliably as nonlinear programs. This paper examines various nonlinear formulations of the complementarity constraints. Several nonlinear complementarity functions are conside ..."
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Cited by 10 (3 self)
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Recently, it has been shown that mathematical programs with complementarity constraints (MPCCs) can be solved efficiently and reliably as nonlinear programs. This paper examines various nonlinear formulations of the complementarity constraints. Several nonlinear complementarity functions are considered for use in MPCC. Unlike standard smoothing techniques, however, the reformulations do not require the control of a smoothing parameter. Thus they have the advantage that the smoothing is exact in the sense that KarushKuhnTucker points of the reformulation correspond to strongly stationary points of the MPCC. A new exact smoothing of the wellknown min function is also introduced and shown to possess desirable theoretical properties. It is shown how the new formulations can be integrated into a sequential quadratic programming solver, and their practical performance is compared on a range of test problems.
A twosided relaxation scheme for mathematical programs with equilibrium constraints
 SIAM J. Optim
, 2005
"... Abstract. We propose a relaxation scheme for mathematical programs with equilibrium constraints (MPECs). In contrast to previous approaches, our relaxation is twosided: both the complementarity and the nonnegativity constraints are relaxed. The proposed relaxation update rule guarantees (under cert ..."
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Cited by 7 (0 self)
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Abstract. We propose a relaxation scheme for mathematical programs with equilibrium constraints (MPECs). In contrast to previous approaches, our relaxation is twosided: both the complementarity and the nonnegativity constraints are relaxed. The proposed relaxation update rule guarantees (under certain conditions) that the sequence of relaxed subproblems will maintain a strictly feasible interior—even in the limit. We show how the relaxation scheme can be used in combination with a standard interiorpoint method to achieve superlinear convergence. Numerical results on the MacMPEC test problem set demonstrate the fast local convergence properties of the approach. Key words. nonlinear programming, mathematical programs with equilibrium constraints, complementarity constraints, constrained minimization, interiorpoint methods, primaldual methods,
An interiorpoint method for MPECs based on strictly feasible relaxations
 Preprint ANL/MCSP11500404, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL
, 2004
"... Abstract. An interiorpoint method for solving mathematical programs with equilibrium constraints (MPECs) is proposed. At each iteration of the algorithm, a single primaldual step is computed from each subproblem of a sequence. Each subproblem is defined as a relaxation of the MPEC with a nonempty ..."
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Cited by 6 (0 self)
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Abstract. An interiorpoint method for solving mathematical programs with equilibrium constraints (MPECs) is proposed. At each iteration of the algorithm, a single primaldual step is computed from each subproblem of a sequence. Each subproblem is defined as a relaxation of the MPEC with a nonempty strictly feasible region. In contrast to previous approaches, the proposed relaxation scheme preserves the nonempty strict feasibility of each subproblem even in the limit. Local and superlinear convergence of the algorithm is proved even with a less restrictive strict complementarity condition than the standard one. Moreover, mechanisms for inducing global convergence in practice are proposed. Numerical results on the MacMPEC test problem set demonstrate the fastlocal convergence properties of the algorithm. Key words. nonlinear programming, mathematical programs with equilibrium constraints, constrained minimization, interiorpoint methods, primaldual methods, barrier methods
The Penalty Interior Point Method fails to converge for mathematical programs with equilibrium constraints
 University of Dundee
, 2002
"... Equilibrium equations in the form of complementarity conditions often appear as constraints in optimization problems. Problems of this type are commonly referred to as mathematical programs with complementarity constraints (MPCCs). A popular method for solving MPCCs is the penalty interiorpoint alg ..."
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Cited by 6 (1 self)
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Equilibrium equations in the form of complementarity conditions often appear as constraints in optimization problems. Problems of this type are commonly referred to as mathematical programs with complementarity constraints (MPCCs). A popular method for solving MPCCs is the penalty interiorpoint algorithm (PIPA). This paper presents a small example for which PIPA converges to a nonstationary point, providing a counterexample to the established theory. The reasons for this adverse behavior are discussed.
On the global minimization of the valueatrisk
 Optimization Methods and Software
"... In this paper, we consider the nonconvex minimization problem of the valueatrisk (VaR) that arises from financial risk analysis. By considering this problem as a special linear program with linear complementarity constraints (a bilevel linear program to be more precise), we develop upper and lower ..."
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Cited by 6 (4 self)
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In this paper, we consider the nonconvex minimization problem of the valueatrisk (VaR) that arises from financial risk analysis. By considering this problem as a special linear program with linear complementarity constraints (a bilevel linear program to be more precise), we develop upper and lower bounds for the minimum VaR and show how the combined bounding procedures can be used to compute the latter value to global optimality. A numerical example is provided to illustrate the methodology. Dedication. With great pleasure we dedicate this paper to a respected pioneer of our field, Professor Olvi L. Mangasarian, on the occasion of his 70th birthday. The two topics of this paper, LPECs and smoothing methods, are examples of the vast contributions that Olvi has made in optimization, which have benefited us in many ways and which will continue to benefit us in the future. Happy 70th birthday, Olvi! 1
A Line Search Exact Penalty Method Using Steering Rules
, 2009
"... Line search algorithms for nonlinear programming must include safeguards to enjoy global convergence properties. This paper describes an exact penalization approach that extends the class of problems that can be solved with line search SQP methods. In the new algorithm, the penalty parameter is adju ..."
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Cited by 3 (1 self)
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Line search algorithms for nonlinear programming must include safeguards to enjoy global convergence properties. This paper describes an exact penalization approach that extends the class of problems that can be solved with line search SQP methods. In the new algorithm, the penalty parameter is adjusted at every iteration to ensure sufficient progress in linear feasibility and to promote acceptance of the step. A trust region is used to assist in the determination of the penalty parameter (but not in the step computation). It is shown that the algorithm enjoys favorable global convergence properties. Numerical experiments illustrate the behavior of the algorithm on various difficult situations. 1
Payment Cost Minimization Auction for Deregulated Electricity Markets With Transmission Capacity Constraints
"... Abstract—Deregulated electricity markets in the U.S. currently use an auction mechanism that minimizes total supply bid costs to select bids and their levels. Payments are then settled based on marketclearingprices. Under this setup, the consumer payments could be significantly higher than the mini ..."
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Abstract—Deregulated electricity markets in the U.S. currently use an auction mechanism that minimizes total supply bid costs to select bids and their levels. Payments are then settled based on marketclearingprices. Under this setup, the consumer payments could be significantly higher than the minimized bid costs obtained from auctions. This gives rise to “payment cost minimization,” an alternative auction mechanism that minimizes consumer payments. We previously presented an augmented Lagrangian and surrogate optimization framework to solve payment cost minimization problems without considering transmission. This paper extends that approach to incorporate transmission capacity constraints. The consideration of transmission constraints complicates the problem by entailing power flow and introducing locational marginal orices (LMPs). DC power flow is used for simplicity and LMPs are defined by “economic dispatch ” for the selected supply bids. To characterize LMPs that appear in the payment cost objective function, Karush–Kuhn–Tucker (KKT) conditions of economic dispatch are established and embedded as constraints. The reformulated problem is difficult in view of the complex role of LMPs and the violation of constraint qualifications caused by the complementarity constraints of KKT conditions. Our key idea is to extend the surrogate optimization framework and use a regularization technique. Specific methods to satisfy the “surrogate optimization condition ” in the presence of transmission capacity constraints are highlighted. Numerical testing results of small examples and the IEEE Reliability Test System with randomly generated supply bids demonstrate the quality, effectiveness, and scalability of the method. Index Terms—Deregulated electricity markets, electricity auctions, locational marginal price (LMP), mathematical programs with equilibrium constraints, payment cost minimization, surrogate optimization, transmission constraints. I.
AN ACTIVE SET METHOD FOR MATHEMATICAL PROGRAMS WITH LINEAR COMPLEMENTARITY CONSTRAINTS
, 2007
"... Abstract. We study mathematical programs with linear complementarity constraints (MPLCC) for which the objective function is smooth. Current nonlinear programming (NLP) based algorithms including regularization methods and decomposition methods generate only weak (e.g., C or M) stationary points t ..."
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Abstract. We study mathematical programs with linear complementarity constraints (MPLCC) for which the objective function is smooth. Current nonlinear programming (NLP) based algorithms including regularization methods and decomposition methods generate only weak (e.g., C or M) stationary points that may not be firstorder solutions to the MPLCC. Piecewise sequential quadratic programming methods enjoy stronger convergence properties, but need to solve expensive subproblems. Here we propose a primaldual active set projected Newton method for MPLCCs, that maintains the feasibility of all iterates. At every iteration the method generates a working set for predicting the active set. The projected step direction on the subspace associated with this working set is determined by the current dual iterate, while other elements in the step direction are computed by a Newton system. The major cost of a subproblem involves one matrix factorization and is comparable to that of NLP based algorithms. Our method has strong convergence properties. In particular, under the MPLCClinear independence constraint qualification, any accumulation point of the generated iterates is a Bstationary solution (i.e., a firstorder solution) to the MPLCC. The asymptotic rate of convergence is quadratic under additional MPLCCsecondorder sufficient conditions and strict complementarity. Key words. mathematical programs with linear complementarity constraints, equilibrium constraints, active set method, Bstationary point, global convergence, local convergence