scholarly journals Exact and approximate discrete optimization algorithms for finding useful disjunctions of categorical predicates in data analysis

2004 ◽  
Vol 144 (1-2) ◽  
pp. 43-58 ◽  
Author(s):  
Endre Boros ◽  
Vladimir Menkov
Keyword(s):  
Data Analysis ◽  
Discrete Optimization ◽  
Optimization Algorithms

scholarly journals The Efficiency of Discrete Optimization Algorithm Portfolios

2021 ◽  
pp. 5-12
Author(s):  
Ivan Sergienko ◽  
Vladimir Shylo ◽  
Valentyna Roshchyn ◽  
Petro Shylo
Keyword(s):  
Discrete Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Stochastic Local Search ◽  
Multiprocessor Computer ◽  
Algorithm Portfolios ◽  
Max Cut Problem ◽  
Discrete Optimization Problems

Introduction. Solving large-scale discrete optimization problems requires the processing of large-scale data in a reasonable time. Efficient solving is only possible by using multiprocessor computer systems. However, it is a daunting challenge to adapt existing optimization algorithms to get all the benefits of these parallel computing systems. The available computational resources are ineffective without efficient and scalable parallel methods. In this connection, the algorithm unions (portfolios and teams) play a crucial role in the parallel processing of discrete optimization problems. The purpose. The purpose of this paper is to research the efficiency of the algorithm portfolios by solving the weighted max-cut problem. The research is carried out in two stages using stochastic local search algorithms. Results. In this paper, we investigate homogeneous and non-homogeneous algorithm portfolios. We developed the homogeneous portfolios of two stochastic local optimization algorithms for the weighted max-cut problem, which has numerous applications. The results confirm the advantages of the proposed methods. Conclusions. Algorithm portfolios could be used to solve well-known discrete optimization problems of unprecedented scale and significantly improve their solving time. Further, we propose using communication between algorithms, namely teams and portfolios of algorithm teams. The algorithms in a team communicate with each other to boost overall performance. It is supposed that algorithm communication allows enhancing the best features of the developed algorithms and would improve the computational times and solution quality. The underlying algorithms should be able to utilize relevant data that is being communicated effectively to achieve any computational benefit from communication. Keywords: Discrete optimization, algorithm portfolios, computational experiment.

GARBLED TEXT CORRECTION USING EVOLUTIONARY DISCRETE OPTIMIZATION ALGORITHMS

2018 ◽  
pp. 3-10
Author(s):  
A. A. Belozerov ◽  
D. V. Vakhlakov ◽  
V. A. Peresypkin ◽  
S. Yu. Melnikov ◽  
D. V. Skavinskaya
Keyword(s):  
Discrete Optimization ◽  
Optimization Algorithms ◽  
Text Correction

DISCRETE OPTIMIZATION ALGORITHMS IN REAL-TIME VISUAL TRACKING

2009 ◽  
Vol 23 (9) ◽  
pp. 805-827 ◽  
Author(s):  
Miguel A. Patricio ◽  
Ivan Dotu ◽  
Jesús García ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Discrete Optimization ◽  
Optimization Algorithms
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