COMPUTATIONAL GRIDS TO SOLVE LARGE SCALE OPTIMIZATION PROBLEMS WITH UNCERTAIN DATA | Zendy

Chefi Triki | Zendy; Lucio Grandinetti | Zendy

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COMPUTATIONAL GRIDS TO SOLVE LARGE SCALE OPTIMIZATION PROBLEMS WITH UNCERTAIN DATA

Author(s) -

Chefi Triki,

Lucio Grandinetti

Publication year - 2014

Publication title -

international journal of computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.184

H-Index - 11

eISSN - 2312-5381

pISSN - 1727-6209

DOI - 10.47839/ijc.1.1.78

Subject(s) - computer science , mathematical optimization , grid , scale (ratio) , path (computing) , optimization problem , stochastic optimization , algorithm , mathematics , physics , geometry , quantum mechanics , programming language

In this paper we discuss the use computational grids to solve stochastic optimization problems. These problems are generally difficult to solve and are often characterized by a high number of variables and constraints. Furthermore, for some applications it is required to achieve a real-time solution. Obtaining reasonable results is a difficult objective without the use of high performance computing. Here we present a grid-enabled path-following algorithm and we discuss some experimental results.

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