A new efficient technique to impose the statistical correlation when using Monte Carlo type method for statistical analysis of computational problems is proposed. The technique is based on stochastic optimization method called Simulated Annealing. The comparison with other techniques presently used and intensive numerical testing showed the superiority and robustness of the method. No significa...

By applying the option pricing theory ideas, this paper models the estimation of ̄rm value distribution function as an entropy optimization problem, subject to correlation constraints. It is shown that the problem can be converted to a dual of a computationally attractive primal geometric programming (GP) problem and easily solved using publicly available software. A numerical example involving...

This paper describes a suite of optimization tools for radiation treatment planning within the Matlab programming environment. The data included with these tools was computed for real patient cases using a Monte Carlo dose engine. The formulation of a series of optimization models is described that utilizes this data within a modeling system. Furthermore, visualization techniques are provided t...

Minimum Cost Polygon Overlay (MCPO) is a unique two-dimensional optimization problem that involves the task of covering a polygon shaped area with a series of rectangular shaped panels. This has a number of applications in the construction industry. This work examines the MCPO problem in order to construct a model that captures essential parameters of the problem to be solved automatically usin...

The accuracy of Monte Carlo and quasi-Monte Carlo methods decreases in problems of high dimensions. Therefore, the objective of this study was to present an optimum method to increase the accuracy of the answer. As the problem gets larger, the resulting accuracy will be higher. In this respect, this study combined the two previous methods, QMC and MC, and presented a hybrid method with efficien...

The article presents three spreadsheet models, which integrate Monte Carlo approach with other decision support techniques, namely: forecasting methods, queuing theory and optimization. The authors indicate the most important reasons to introduce Monte Carlo approach into teaching process of simulation and spreadsheet engineering to undergraduate and graduate business students. The article conc...

We study three wave function optimization methods based on energy minimization in a variational Monte Carlo framework: the Newton, linear, and perturbative methods. In the Newton method, the parameter variations are calculated from the energy gradient and Hessian, using a reduced variance statistical estimator for the latter. In the linear method, the parameter variations are found by diagonali...

Background: Monte Carlo and experimental relative dose determination in a water phantom, due to a high dose rate (HDR) 192Ir source is presented for real energy spectrum and monochromatic at 356 keV. Materials and Methods: The dose distribution has been calculated around the 192Ir located in the center of 30 cm ×30 cm ×30 cm water phantom using MCNP4C code by Monte Carlo method. Relati...

Background: Iodine brachytherapy sources with low photon energies have been widely used in treating cancerous tumors. Dosimetric parameters of brachytherapy sources should be determined according to AAPM TG-43U1 recommendations before clinical use. Monte Carlo codes are reliable tools in calculation of these parameters for brachytherapy sources. Materials and Methods: Dosimetric param...

Many quantitative problems in science, engineering, and economics are nowadays solved via statistical sampling on a computer. Such Monte Carlo methods can be used in three different ways: (1) to generate random objects and processes in order to observe their behavior, (2) to estimate numerical quantities by repeated sampling, and (3) to solve complicated optimization problems through randomized...