Nonlinear programming: theory, algorithms, and applications
Nonlinear programming: theory, algorithms, and applications
Integer and combinatorial optimization
Integer and combinatorial optimization
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Mathematical Programming: Series A and B
A Lagrangian-based heuristic for large-scale set covering problems
Mathematical Programming: Series A and B - Special issue on computational integer programming
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
A Heuristic Method for the Set Covering Problem
Operations Research
Modeling and Solving the Train Timetabling Problem
Operations Research
A survey of very large-scale neighborhood search techniques
Discrete Applied Mathematics
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The well-known and established global optimality conditions based on the Lagrangian formulation of an optimization problem are consistent if and only if the duality gap is zero. We develop a set of global optimality conditions that are structurally similar but are consistent for any size of the duality gap. This system characterizes a primaldual optimal solution by means of primal and dual feasibility, primal Lagrangian É聸-optimality, and, in the presence of inequality constraints, a relaxed complementarity condition analogously called -complementarity. The total size É聸 of those two perturbations equals the size of the duality gap at an optimal solution. Further, the characterization is equivalent to a near-saddle point condition which generalizes the classic saddle point characterization of a primaldual optimal solution in convex programming. The system developed can be used to explain, to a large degree, when and why Lagrangian heuristics for discrete optimization are successful in reaching near-optimal solutions. Further, experiments on a set-covering problem illustrate how the new optimality conditions can be utilized as a foundation for the construction of new Lagrangian heuristics. Finally, we outline possible uses of the optimality conditions in column generation algorithms and in the construction of core problems.