A combined Lagrangian, linear programming, and implication heuristic for large-scale set partitioning problems

1996 ◽  
Vol 1 (2) ◽  
pp. 247-259 ◽  
Author(s):  
A. Atamt�rk ◽  
G. L. Nemhauser ◽  
M. W. P. Savelsbergh
Keyword(s):  
Linear Programming ◽  
Large Scale ◽  
Set Partitioning ◽  
Partitioning Problems

A Parallel, Linear Programming-based Heuristic for Large-Scale Set Partitioning Problems

2001 ◽  
Vol 13 (3) ◽  
pp. 191-209 ◽  
Author(s):  
Jeff T. Linderoth ◽  
Eva K. Lee ◽  
Martin W. P. Savelsbergh
Keyword(s):  
Linear Programming ◽  
Large Scale ◽  
Set Partitioning ◽  
Partitioning Problems

scholarly journals LARGE-SCALE SET PARTITIONING PROBLEMS: SOME REAL‐WORLD INSTANCES HIDE A BENEFICIAL STRUCTURE

2006 ◽  
Vol 12 (1) ◽  
pp. 18-22
Author(s):  
Luca Coslovich ◽  
Raffaele Pesenti ◽  
Walter Ukovich
Keyword(s):  
Exact Solution ◽  
Heuristic Algorithm ◽  
Real World ◽  
Large Scale ◽  
Set Partitioning ◽  
Trucking Industry ◽  
The Real ◽  
Benchmark Instances ◽  
Partitioning Problems ◽  
Insight Into

In this paper we consider large‐scale set partitioning problems. Our main purpose is to show that real‐world set partitioning problems originating from the container‐trucking industry are easier to tackle in respect to general ones. We show such different behavior through computational experiments: in particular, we have applied both a heuristic algorithm and some exact solution approaches to real‐world instances as well as to benchmark instances from Beasley OR‐library. Moreover, in order to gain an insight into the structure of the real-world instances, we have performed and evaluated various instance perturbations.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals Large Scale Non-Linear Programming for PDE Constrained Optimization

2002 ◽  
Author(s):  
BART G VAN BLOEMEN WAANDERS ◽  
ROSCOE A BARTLETT ◽  
KEVIN R LONG ◽  
PAUL T BOGGS ◽  
ANDREW G SALINGER
Keyword(s):  
Linear Programming ◽  
Constrained Optimization ◽  
Large Scale ◽  
Pde Constrained Optimization ◽  
Non Linear Programming ◽  
Non Linear
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