Monte-Carlo Methods from the Point of View of Algorithmic Complexity

Author(s):  
Ivan Kramosil
2021 ◽  
Vol 2 (2) ◽  
pp. 132-151
Author(s):  
Vito Vitali ◽  
Florent Chevallier ◽  
Alexis Jinaphanh ◽  
Andrea Zoia ◽  
Patrick Blaise

Modal expansions based on k-eigenvalues and α-eigenvalues are commonly used in order to investigate the reactor behaviour, each with a distinct point of view: the former is related to fission generations, whereas the latter is related to time. Well-known Monte Carlo methods exist to compute the direct k or α fundamental eigenmodes, based on variants of the power iteration. The possibility of computing adjoint eigenfunctions in continuous-energy transport has been recently implemented and tested in the development version of TRIPOLI-4®, using a modified version of the Iterated Fission Probability (IFP) method for the adjoint α calculation. In this work we present a preliminary comparison of direct and adjoint k and α eigenmodes by Monte Carlo methods, for small deviations from criticality. When the reactor is exactly critical, i.e., for k0 = 1 or equivalently α0 = 0, the fundamental modes of both eigenfunction bases coincide, as expected on physical grounds. However, for non-critical systems the fundamental k and α eigenmodes show significant discrepancies.


2019 ◽  
Vol 65 ◽  
pp. 294-308x ◽  
Author(s):  
Bruno Bouchard ◽  
Ki Wai Chau ◽  
Arij Manai ◽  
Ahmed Sid-Ali

We extend the viscosity solution characterization proved in [5] for call/put American option prices to the case of a general payoff function in a multi-dimensional setting: the price satisfies a semilinear reaction/diffusion type equation. Based on this, we propose two new numerical schemes inspired by the branching processes based algorithm of [8]. Our numerical experiments show that approximating the discontinuous driver of the associated reaction/diffusion PDE by local polynomials is not efficient, while a simple randomization procedure provides very good results.


1982 ◽  
Vol 5 (3-4) ◽  
pp. 301-312
Author(s):  
Ivan Kramosil

In this paper we investigate the Monte-Carlo method for estimation of the unknown probability of a random event on the ground of relative frequencies and under the condition that random sampling is replaced by a deterministic side input producing binary sequences of high algorithmic complexity. It is proved that if this complexity exceeds a treshold value, the sequences may be used in the Monte-Carlo methods instead of random samples as the obtained estimates converge to the estimated probability when the length of these binary sequences increases.


Radiation ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 33-51
Author(s):  
Paweł Wysocki ◽  
Krzysztof W. Fornalski

It is well known that ionizing radiation can cause damages to cells that interact with it directly. However, many studies have shown that damages also occur in cells that have not experienced direct interaction. This is due to the so-called bystander effect, which is observed when the irradiated cell sends signals that can damage neighboring cells. Due to the complexity of this effect, it is not easy to strictly describe it biophysically, and thus it is also difficult to simulate. This article reviews various approaches to modeling and simulating the bystander effect from the point of view of radiation biophysics. In particular, the last model presented within this article is part of a larger project of modeling the response of a group of cells to ionizing radiation using Monte Carlo methods. The new approach presented here is based on the probability tree, the Poisson distribution of signals and the saturated dose-related probability distribution of the bystander effect’s appearance, which makes the model very broad and universal.


Author(s):  
Ranjan S. Mehta ◽  
Anquan Wang ◽  
Michael F. Modest ◽  
Daniel C. Haworth

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