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13
Global minimization using an Augmented Lagrangian method with variable lowerlevel constraints
, 2007
"... A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration k the method requires the εkglobal minimization of the Augmented Lagrangian with simple constraints, where εk → ε. Global c ..."
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Cited by 21 (1 self)
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A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration k the method requires the εkglobal minimization of the Augmented Lagrangian with simple constraints, where εk → ε. Global convergence to an εglobal minimizer of the original problem is proved. The subproblems are solved using the αBB method. Numerical experiments are presented.
Reformulations in Mathematical Programming: A Computational Approach
"... Summary. Mathematical programming is a language for describing optimization problems; it is based on parameters, decision variables, objective function(s) subject to various types of constraints. The present treatment is concerned with the case when objective(s) and constraints are algebraic mathema ..."
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Cited by 17 (13 self)
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Summary. Mathematical programming is a language for describing optimization problems; it is based on parameters, decision variables, objective function(s) subject to various types of constraints. The present treatment is concerned with the case when objective(s) and constraints are algebraic mathematical expressions of the parameters and decision variables, and therefore excludes optimization of blackbox functions. A reformulation of a mathematical program P is a mathematical program Q obtained from P via symbolic transformations applied to the sets of variables, objectives and constraints. We present a survey of existing reformulations interpreted along these lines, some example applications, and describe the implementation of a software framework for reformulation and optimization. 1
REFORMULATIONS IN MATHEMATICAL PROGRAMMING: DEFINITIONS AND SYSTEMATICS
, 2008
"... A reformulation of a mathematical program is a formulation which shares some properties with, but is in some sense better than, the original program. Reformulations are important with respect to the choice and efficiency of the solution algorithms; furthermore, it is desirable that reformulations c ..."
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Cited by 17 (13 self)
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A reformulation of a mathematical program is a formulation which shares some properties with, but is in some sense better than, the original program. Reformulations are important with respect to the choice and efficiency of the solution algorithms; furthermore, it is desirable that reformulations can be carried out automatically. Reformulation techniques are very common in mathematical programming but interestingly they have never been studied under a common framework. This paper attempts to move some steps in this direction. We define a framework for storing and manipulating mathematical programming formulations, give several fundamental definitions categorizing reformulations in essentially four types (optreformulations, narrowings, relaxations and approximations). We establish some theoretical results and give reformulation examples for each type.
Optimal running and planning of a biomassbased energy production process
, 2008
"... We propose ..."
Reformulation in mathematical programming: an application to quantum chemistry
 DISCRETE APPLIED MATHEMATICS, ACCEPTED FOR PUBLICATION
, 2007
"... ..."
Comparison of Deterministic and Stochastic Approaches to global optimization
"... In this paper we compare two different approaches to nonconvex global optimization. The first one is a deterministic spatial BranchandBound algorithm (sBB), whereas the second approach is a quasi Monte Carlo (QMC) variant of a stochastic multi level single linkage (MLSL) algorithm. Both algorithms ..."
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Cited by 6 (3 self)
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In this paper we compare two different approaches to nonconvex global optimization. The first one is a deterministic spatial BranchandBound algorithm (sBB), whereas the second approach is a quasi Monte Carlo (QMC) variant of a stochastic multi level single linkage (MLSL) algorithm. Both algorithms apply to problems in a very general form and are not dependent on problem structure. The test suite we chose is fairly extensive in scope, in that it includes constrained and unconstrained problems, continuous and mixedinteger problems. The conclusion of the tests is that in general the QMC variant of the MLSL algorithm is more efficient, although in some instances the BranchandBound algorithm is capable of locating the global optimum of hard problems in just one iteration.
ReformulationLinearization Methods for Global Optimization
, 2007
"... Keywords: ReformulationLinearization Technique, liftandproject, tight relaxations, valid inequalities, model reformulation, convex hull, convex envelopes, mixedinteger 01 program, polynomial programs, nonconvex programs, factorable programs, reduced relaxations. Discrete and continuous nonconve ..."
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Cited by 4 (1 self)
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Keywords: ReformulationLinearization Technique, liftandproject, tight relaxations, valid inequalities, model reformulation, convex hull, convex envelopes, mixedinteger 01 program, polynomial programs, nonconvex programs, factorable programs, reduced relaxations. Discrete and continuous nonconvex programming problems arise in a host of practical applications in the context of production planning and control, locationallocation, distribution, economics and game theory, quantum chemistry, and process and engineering design situations. Several recent advances have been made in the development of branchandcut type algorithms for mixedinteger linear and nonlinear programming problems, as well as polyhedral outerapproximation methods for continuous nonconvex programming problems. At the heart of these approaches is a sequence of linear (or convex) programming relaxations that drive the solution process, and the success of such algorithms is strongly tied in with the strength or tightness of these relaxations. The ReformulationLinearizationTechnique (RLT) is a method that generates such tight linear programming relaxations for not only constructing exact solution algorithms, but also to design powerful heuristic procedures for large classes of discrete combinatorial and continuous nonconvex programming problems. Its development originated in [4, 5, 6], initially focusing on 01 and mixed 01 linear and
Mathematical programmingbased approach to scheduling of communicating tasks
, 2004
"... We present a MILP mathematical programming formulation for static scheduling of dependent tasks onto homogeneous multiprocessor system of an arbitrary architecture with communication delays. We reduce the number of constraints by applying a Reduction Constraint reformulation to the model. We solve s ..."
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Cited by 3 (1 self)
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We present a MILP mathematical programming formulation for static scheduling of dependent tasks onto homogeneous multiprocessor system of an arbitrary architecture with communication delays. We reduce the number of constraints by applying a Reduction Constraint reformulation to the model. We solve several smallscale instances of the reformulated problem by using CPLEX 8.1. Upper bounds are computed with the Variable Neighborhood Search metaheuristic applied directly to the graphbased formulation of the problem, whereas lower bounds are obtained by solving linear relaxations of the MILP formulation, further tightened by using load balancing and critical path method arguments. Résumé Nous présentons une formulation sous la forme d’un modèle de programmation linéaire en nombres entiers (PLNE) pour le problème d’ordonnancement avec des tâches dépendantes dans un système de multiprocesseurs homogènes associés à une architecture arbitraire en présence de délais de communications. Pour diminuer le nombre de contraintes nous utilisons une reformulation du modèle à l’aide d’une procédure de Réduction de Contraintes. Nous avons résolu plusieurs exemples de petite taille du modèle reformulé avec CPLEX 8.1. Les bornes supérieures ont été calculées par des techniques VNS appliquées directement à une formulation basée sur la théorie des graphes. Les bornes inférieures ont été obtenues en résolvant des relaxations linéaires de la formulation PLNE et améliorées par des méthodes du chemin critique et par des techniques de balancemment de charges.
Global Nonlinear Programming with possible infeasibility and finite termination
, 2012
"... In a recent paper, Birgin, Floudas and Martínez introduced an augmented Lagrangian method for global optimization. In their approach, augmented Lagrangian subproblems are solved using the αBB method and convergence to global minimizers was obtained assuming feasibility of the original problem. In th ..."
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Cited by 1 (0 self)
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In a recent paper, Birgin, Floudas and Martínez introduced an augmented Lagrangian method for global optimization. In their approach, augmented Lagrangian subproblems are solved using the αBB method and convergence to global minimizers was obtained assuming feasibility of the original problem. In the present research, the algorithm mentioned above will be improved in several crucial aspects. On the one hand, feasibility of the problem will not be required. Possible infeasibility will be detected in finite time by the new algorithms and optimal infeasibility results will be proved. On the other hand, finite termination results thatguaranteeoptimalityand/orfeasibilityuptoanyrequiredprecisionwillbeprovided. An adaptive modification in which subproblem tolerances depend on current feasibility and complementarity will also be given. The adaptive algorithm allows the augmented Lagrangian subproblems to be solved without requiring unnecessary potentially high precisions in the intermediate steps of the method, which improves the overall efficiency. Experiments showing how the new algorithms and results are related to practical computations will be given.