Современные интервальные подходы к вычислению решений непрерывных игр 

Author(s):  
B.J. Kubica

Computing Nash equilibria in continuous games is a difficult problem, but interval methods have already been applied to its solution quite successfully. The purpose of this paper is to briefly survey previous efforts and achievements of the author related to the topic, and to consider some advanced tools for accelerating the interval branch-and-bound-type methods. In particular, we discuss computing eigenvalues of interval matrices, use of algorithmic (automatic) differentiation, memory management techniques as well as advanced parallelization in both shared-memory and distributedmemory environments. Дан краткий анализ результатов исследований по применению интервальных методов для вычисления равновесия по Нэшу в непрерывных играх. Рассмотрены некоторые современные подходы к ускорению интервальных методов типа ветвей и границ. В частности, обсуждаются такие вопросы, как вычисление собственных значений интервальных матриц, использование алгоритмического(автоматического) дифференцирования, методы управления памятью, инструменты распараллеливания в средах с разделяемой и распределенной памятью.

Author(s):  
K. G. Langendoen ◽  
H. L. Muller ◽  
W. G. Vree

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167351-167373
Author(s):  
Anna Pupykina ◽  
Giovanni Agosta

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Dhiranuch Bunnag

This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen’s algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA.


Sign in / Sign up

Export Citation Format

Share Document