Prediction of three-dimensional structures of proteins and peptides by global optimization of the free energy estimate has been attempted without much success for over thirty years. The key problems were the insufficient accuracy of the free energy estimate and the giant size of the conformational space. Global optimization of the free energy function of a peptide in internal coordinate space i...

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. relative dose v...

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem.

A Monte Carlo simulated annealing algorithm based on the generalized entropy of Tsallis is presented. The algorithm obeys detailed balance and reduces to a steepest descent algorithm at low temperatures. Application to the conformational optimization of a tetrapeptide demonstrates that the algorithm is more effective in locating low energy minima than standard simulated annealing based on molec...

Steepest Descent. Discrete Iterative Optimization. Markov Chain Monte Carlo (MCMC). NOTE: NOT FOR DISTRIBUTION!!

The stochastic approximation Monte Carlo (SAMC) algorithm has recently been proposed as a dynamic optimization algorithm in the literature. In this paper, we show in theory that the samples generated by SAMC can be used for Monte Carlo integration via a dynamically weighted estimator by calling some results from the literature of nonhomogeneous Markov chains. Our numerical results indicate that...

Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov c...

In large domains, Monte-Carlo tree search (MCTS) is required to estimate the values of states as efficiently and accurately possible. However, standard update rule in backpropagation assumes a stationary distribution for returns, particularly min-max trees, convergence true value can be slow because averaging. We present two methods, Softmax MCTS Monotone MCTS, which generalize previous attempt...

In this paper, we revisit the recently established theoretical guarantees for the convergence of the Langevin Monte Carlo algorithm of sampling from a smooth and (strongly) log-concave density. We improve the existing results when the convergence is measured in the Wasserstein distance and provide further insights on the very tight relations between, on the one hand, the Langevin Monte Carlo fo...