Extending algebraic modelling languages to support algorithm development for solving stochastic programming models

2007 ◽  
Vol 19 (4) ◽  
pp. 325-345 ◽  
Author(s):  
S. Karabuk
Keyword(s):  
Stochastic Programming ◽  
Programming Models ◽  
Algorithm Development ◽  
Modelling Languages ◽  
Algebraic Modelling

scholarly journals Extending Algebraic Modelling Languages for Stochastic Programming

2009 ◽  
Vol 21 (1) ◽  
pp. 107-122 ◽  
Author(s):  
Christian Valente ◽  
Gautam Mitra ◽  
Mustapha Sadki ◽  
Robert Fourer
Keyword(s):  
Stochastic Programming ◽  
Modelling Languages ◽  
Algebraic Modelling

MINIMIZING THE COMPLETE INFLUENCE TIME IN A SOCIAL NETWORK WITH STOCHASTIC COSTS FOR INFLUENCING NODES

2013 ◽  
Vol 21 (supp01) ◽  
pp. 63-74 ◽  
Author(s):  
YAODONG NI ◽  
QIAONI SHI
Keyword(s):  
Social Network ◽  
Computational Complexity ◽  
Stochastic Programming ◽  
Greedy Algorithm ◽  
Random Graphs ◽  
Minimum Time ◽  
Programming Models ◽  
Decision Criteria ◽  
Trade Off ◽  
The Cost

In this paper, we study the problem of targeting a set of individuals to trigger a cascade of influence in a social network such that the influence diffuses to all individuals with the minimum time, given that the cost of initially influencing each individual is with randomness and that the budget available is limited. We adopt the incremental chance model to characterize the diffusion of influence, and propose three stochastic programming models that correspond to three different decision criteria respectively. A modified greedy algorithm is presented to solve the proposed models, which can flexibly trade off between solution performance and computational complexity. Experiments are performed on random graphs, by which we show that the algorithm we present is effective.

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