A general theoretical model is described for the NMR spectra of mixtures of sterols and deuterium-labeled phospholipids. In the case of homogeneous membranes, the average quadrupole splittings are determined by equilibria between lipids in cholesterol-phospholipid complexes and lipids not in complexes. Chemical exchange of lipids between those in the free state and those in the complex state af...

We define a new algorithm, named “Drem”, for tuning the weighted linear model in a statistical machine translation system. Drem has two major innovations. First, it uses scaled derivative-free trust-region optimization rather than other methods’ line search or (sub)gradient approximations. Second, it interpolates the decoder output, using information about which decodes produced which translati...

We present an example of the quantum system with higher derivatives in the Lagrangian, which is ghost-free: the spectrum of the Hamiltonian is bounded from below and unitarity is preserved.

In this paper, we present some derivative-free methods for solving system of nonlinear equations based on approximating the Jacobian matrix via acceleration and correction parameters. Furthermore, compute step length using inexact line search procedure. Under appropriate conditions, proved that proposed are globally. We also numerical results to show efficiency by comparing them with existing i...

We propose data profiles as a tool for analyzing the performance of derivativefree optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide...

Many randomized heuristic derivative-free optimization methods share a framework that iteratively learns a model for promising search areas and samples solutions from the model. This paper studies a particular setting of such framework, where the model is implemented by a classification model discriminating good solutions from bad ones. This setting allows a general theoretical characterization...

Reinforcement learning is about agent models that make the best sequential decisions in unknown environments. In an environment, needs to explore environment while exploiting collected information, which usually forms a sophisticated problem solve. Derivative-free optimization, meanwhile, capable of solving problems. It commonly uses sampling-and-updating framework iteratively improve solution,...