1. Introduction Many important real-world problems contain stochastic elements and require optimization. Stochastic programming and simulation-based optimization are two approaches used to address this issue. We do not explicitly discuss other related areas including stochastic control, stochas-tic dynamic programming, and Markov decision processes. We consider a stochastic optimization problem...

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to hel...

one of known methods for measuring, forecasting and managing risk is value at risk, which recently has been used by financial institutions extensively. value at risk (var) is a method for recognizing and evaluating risk and uses standard statistical techniques that have daily using in other contexts. this research is seeking a career for managing investment risk in stock exchange and selection ...

This paper uncovers and explores the close relationship between Monte Carlo Optimization of a parametrized integral (MCO), Parametric machine-Learning (PL), and ‘blackbox’ or ‘oracle’-based optimization (BO). We make four contributions. First, we prove that MCO is mathematically identical to a broad class of PL problems. This identity potentially provides a new application domain for all broadl...

We consider the problem of optimizing a real-valued continuous function f using a Bayesian approach, where the evaluations of f are chosen sequentially by combining prior information about f , which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are u...

Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. A...

in this study, monte carlo statistical mechanical simulations for vinblastine and vincristine werecarried out in standard manner using the metropolis sampling technique in canonical (t, v, n)ensemble., geometrical optimizations of vinblastine and vincristine were carried out with the hfmethod coupled to 6-31g(d) basis sets for all atoms. simulation was done by four force fields ofmm+, bio+, amb...