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An analysis of approximations for maximizing submodular set functions—I
TLDR
We analyze greedy and local improvement heuristics and a linear programming relaxation for this problem. Expand
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Integer and Combinatorial Optimization
FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for LinearExpand
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Integer Programming
The principles of integer programming are directed toward finding solutions to problems from the fields of economic planning, engineering design, and combinatorial optimization. This highly respectedExpand
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An analysis of the greedy algorithm for the submodular set covering problem
  • L. Wolsey
  • Mathematics, Computer Science
  • Comb.
  • 1982
TLDR
This generalises earlier results of Dobson and others on the applications of the greedy algorithm to the integer covering problem: min {fy: Ay ≧b, y ε {0, 1}} whereaij,bi} ≧ 0 are integer, and also includes the problem of finding a minimum weight basis in a matroid. Expand
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Production Planning by Mixed Integer Programming
TLDR
This textbook provides a comprehensive modeling, reformulation and optimization approach for solving production planning and supply chain planning problems, covering topics from a basic introduction to planning systems, mixed integer programming (MIP) models and algorithms through the advanced description of mathematical results in polyhedral combinatorics. Expand
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Best Algorithms for Approximating the Maximum of a Submodular Set Function
TLDR
We present a family of algorithms that involve the partial enumeration of all sets of cardinality q and then a greedy selection of the remaining elements, q = 0,..., K-1. Expand
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Integer and Combinatorial Optimization
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Uncapacitated lot-sizing: The convex hull of solutions
TLDR
In this paper, the convex hull of the solutions of the economic lot-sizing model is given. Expand
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Modelling Practical Lot-Sizing Problems as Mixed-Integer Programs
TLDR
In spite of the remarkable improvements in the quality of general purpose mixed-integer programming software, the effective solution of a variety of lot-sizing problems depends crucially on the development of tight formulations for the special problem features occurring in practice. Expand
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