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ICCS
2007
Springer

Complexity of Monte Carlo Algorithms for a Class of Integral Equations

9 years 6 months ago
Complexity of Monte Carlo Algorithms for a Class of Integral Equations
In this work we study the computational complexity of a class of grid Monte Carlo algorithms for integral equations. The idea of the algorithms consists in an approximation of the integral equation by a system of algebraic equations. Then the Markov chain iterative Monte Carlo is used to solve the system. The assumption here is that the corresponding Neumann series for the iterative matrix does not necessarily converge or converges slowly. We use a special technique to accelerate the convergence. An estimate of the computational complexity of Monte Carlo algorithm using the considered approach is obtained. The estimate of the complexity is compared with the corresponding quantity for the complexity of the grid-free Monte Carlo algorithm. The conditions under which the class of grid Monte Carlo algorithms is more efficient are given.
Ivan Dimov, Rayna Georgieva
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ICCS
Authors Ivan Dimov, Rayna Georgieva
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