Titlebook: Computational Combinatorial Optimization; Optimal or Provably Michael Jünger,Denis Naddef Textbook 2001 Springer-Verlag Berlin Heidelberg

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Stahl und Eisenbeton im Gescho?gro?bauIn this paper we survey the basic features of state-of-the-art branch-and-cut algorithms for the solution of general mixed integer programming problems. In particular we focus on preprocessing techniques, branch-and-bound issues and cutting plane generation.

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https://doi.org/10.1007/978-3-642-94618-9Lagrangian relaxation is a tool to find upper bounds on a given (arbitrary) maximization problem. Sometimes, the bound is exact and an optimal solution is found. Our aim in this paper is to review this technique, the theory behind it, its numerical aspects, its relation with other techniques such as column generation.

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General Mixed Integer Programming: Computational Issues for Branch-and-Cut Algorithms,In this paper we survey the basic features of state-of-the-art branch-and-cut algorithms for the solution of general mixed integer programming problems. In particular we focus on preprocessing techniques, branch-and-bound issues and cutting plane generation.

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Lagrangian Relaxation,Lagrangian relaxation is a tool to find upper bounds on a given (arbitrary) maximization problem. Sometimes, the bound is exact and an optimal solution is found. Our aim in this paper is to review this technique, the theory behind it, its numerical aspects, its relation with other techniques such as column generation.

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Textbook 2001munity. The lectures introduce modern combinatorial optimization techniques, with an emphasis on branch and cut algorithms and Lagrangian relaxation approaches. Polyhedral combinatorics as the mathematical backbone of successful algorithms are covered from many perspectives, in particular, polyhedra

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0302-9743 by leading members of the optimization community. The lectures introduce modern combinatorial optimization techniques, with an emphasis on branch and cut algorithms and Lagrangian relaxation approaches. Polyhedral combinatorics as the mathematical backbone of successful algorithms are covered from

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Projection and Lifting in Combinatorial Optimization,sections deal with those basic properties of projection that make it such an effiective and useful bridge between problem formulations in different spaces, i.e. different sets of variables. They discuss topics like the integrality-preserving property of projection, the dimension of projected polyhed

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Mathematical Programming Models and Formulations for Deterministic Production Planning Problems,tion planning problems. The objective is to present the classical optimization approaches used, and the known models, for dealing with such management problems..We describe first production planning models in the general context of manufacturing planning and control systems, and explain in which sen

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Branch-and-Cut Algorithms for Combinatorial Optimization and Their Implementation in ABACUS,on problems to optimality (or, at least, with certified quality). In this unit, we concentrate on sequential branch-and-cut for hard combinatorial optimization problems, while branch-and-cut for general mixed integer linear programming is treated in [→ Martin] and parallel branch-and-cut is treated