Adaptive Monte Carlo methods are very efficient techniques designed to tune simulation estimators on-line. In this work, we present an alternative to stochastic approximation to tune the optimal change of measure in the context of importance sampling for normal random vectors. Unlike stochastic approximation, which requires very fine tuning in practice, we propose to use sample average approxim...

We provide an overview of two select topics in Monte Carlo simulationbased methods for stochastic optimization: problems with stochastic constraints and variance reduction techniques. While Monte Carlo simulation-based methods have been successfully used for stochastic optimization problems with deterministic constraints, there is a growing body of work on its use for problems with stochastic c...

introduction after the publication of task group number 43 dose calculation formalism by the american association of physicists in medicine (aapm), this method has been known as the most common dose calculation method in brachytherapy treatment planning. in this formalism, the water phantom is introduced as the reference dosimetry phantom, while the attenuation coefficient of the sources in the...

An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbi...

Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it involves the evaluation of a multidimensional inte...

An inversion technique comprising stochastic search and regularized gradient optimization was developed to solve the atmospheric source characterization problem. The inverse problem comprises retrieving the spatial coordinates, source strength, and the wind speed and wind direction at the source, given certain receptor locations and concentration values at these receptor locations. The Gaussian...

Experimental investigation and optimization of various optical parameters in the design of depth sensitive optical measurements in layered tissues would require a huge amount of time and resources. A computational method to model light transport in layered tissues using Monte Carlo simulations has been developed for decades to reduce the cost incurred during this process. In this work, we emplo...

This paper presents a multi-objective order allocation planning problem with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization process, a Monte Carlo simulation technique and a heuristic pruning technique, is proposed to handle this problem. Experiments based on industrial data are condu...

Introduction: Monte Carlo calculation method is considered to be the most accurate method for dose calculation in radiotherapy. The purpose of this research is comparison between 6 MV Primus LINAC simulation output with commissioning data using EGSnrc and build a Monte Carlo geometry of 6 MV Primus LINAC as realistically as possible. The BEAMnrc and DOSXYZnrc (EGSnrc package) M...

This paper is concerned with chance constrained programming to deal with nonlinear optimization problems with random parameters. Specific Monte Carlo methods to evaluate the gradient and Hessian of probabilistic constraints are proposed and discussed. These methods are implemented in penalization optimization routines adapted to stochastic optimization. They are shown to reduce the computationa...