an important need in radiation therapy is a fast and accurate treatment planning system. this system using ct data and characteristics of the radiation beam, calculates the dose in all points of the patient’s volume. two main factors in planning system are “accuracy” and “speed”. according to these factors, various generation of treatment planning systems are developed.   this article is the re...

This paper briefly reviews some properties of Monte Carlo simulation and emphasizes the link to evolutionary computation. It shows how this connection can help to study evolutionary algorithms within a unified framework. It also gives some practical examples of implementation inspired from MOSES (the mutation-or-selection evolution strategy).

A recently introduced Importance Sampling strategy based on a least squares optimization is applied to the Monte Carlo simulation of Libor Market Models. Such Least Squares Importance Sampling (LSIS) allows the automatic optimization of the sampling distribution within a trial class by means of a quick presimulation algorithm of straightforward implementation. With several numerical examples we...

Optimization in the presence of noise is a difficult task. Most of the optimization methods available are totally deterministic in nature, and, when applied to problems affected by noise, they are either unable to reach an optimum or they may reach a false one. In this Letter we present a novel optimization scheme, called the stochastic gradient approximation (SGA). The method has its roots in ...

Abstract. We prove several theorems to give sufficient conditions for convergence of quantum annealing, which is a protocol to solve generic optimization problems by quantum dynamics. In particular the property of strong ergodicity is proved for the path-integral Monte Carlo implementation of quantum annealing for the transverse Ising model under a power decay of the transverse field. This resu...

In this paper, we propose a population-based optimization algorithm, Sequential Monte Carlo Simulated Annealing (SMC-SA), for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing. We prove an upper bound on the difference between the empirical distribution yielded by SMC-SA and th...

The energy variance optimization algorithm over a fixed ensemble of configurations in variational Monte Carlo often encounters problems of convergence. Being formally identical to a problem of fitting data, we re-examine it from a statistical maximum-likelihood point of view. We show that the assumption of an underlying Gaussian distribution of the local energy, implicit in the standard varianc...

The variational Monte Carlo method is reviewed here. It is in essence a classical statistical mechanics approach, yet allows the calculation of quantum expectation values. We give an introductory exposition of the theoretical basis of the approach, including sampling methods and acceleration techniques; its connection with trial wavefunctions; and how in practice it is used to obtain high quali...

Monte Carlo techniques have become popular in different areas of medical physics over the last 50 years. Factors which have contributed to the wider use include the improved description of radiation transport as well as the optimization of the computing systems. The main advantage of Monte Carlo methodology deals with the simulation of stochastic processes involving random behavior and the quan...

In Stochastic Programming, the aim is often the optimization of a criterion function that can be written as an integral or mean functional with respect to a probability measure P. When this functional cannot be computed in closed form, it is customary to approximate it through an empirical mean functional based on a random Monte Carlo sample. Several improved methods have been proposed, using q...