On Using the Decision Trees to Identify the Local Extrema in Parallel Global Optimization Algorithm

In the present work, the solving of the multidimensional global optimization problems using decision tree to reveal the attractor regions of the local minima is considered. The objective function of the problem is defined as a “black box”, may be non-differentiable, multi-extremal and computational costly. We assume that the function satisfies the Lipschitz condition with a priory unknown constant. Global search algorithm is applied for the search of global minimum in the problems of such type. It is well known that the solution complexity essentially depends on the presence of multiple local extrema. Within the framework of the global search algorithm, we propose a method for selecting the vicinity of local extrema of the objective function based on analysis of accumulated search information. Conducting such an analysis using machine learning techniques allows making a decision to run a local method, which can speed up the convergence of the algorithm. This suggestion was confirmed by the results of numerical experiments demonstrating the speedup when solving a series of test problems.In the present work, the solving of the multidimensional global optimization problems using decision tree to reveal the attractor regions of the local minima is considered. The objective function of the problem is defined as a “black box”, may be non-differentiable, multi-extremal and computational costly. We assume that the function satisfies the Lipschitz condition with a priory unknown constant. Global search algorithm is applied for the search of global minimum in the problems of such type. It is well known that the solution complexity essentially depends on the presence of multiple local extrema. Within the framework of the global search algorithm, we propose a method for selecting the vicinity of local extrema of the objective function based on analysis of accumulated search information. Conducting such an analysis using machine learning techniques allows making a decision to run a local method, which can speed up the convergence of the algorithm. This suggestion was confirmed by the results of numerical experiments demonstrating the speedup when solving a series of test problems

Pressure-induced magnetic collapse and metallization of TlFe1.6Se2\mathrm{TlF}{\mathrm{e}}_{1.6}\mathrm{S}{\mathrm{e}}_{2}

The crystal structure, magnetic ordering, and electrical resistivity of TlFe1.6Se2 were studied at high pressures. Below ~7 GPa, TlFe1.6Se2 is an antiferromagnetically ordered semiconductor with a ThCr2Si2-type structure. The insulator-to-metal transformation observed at a pressure of ~ 7 GPa is accompanied by a loss of magnetic ordering and an isostructural phase transition. In the pressure range ~ 7.5 - 11 GPa a remarkable downturn in resistivity, which resembles a superconducting transition, is observed below 15 K. We discuss this feature as the possible onset of superconductivity originating from a phase separation in a small fraction of the sample in the vicinity of the magnetic transition.Comment: 12 pages, 5 figure

Short-range order and electronic properties of epitaxial graphene

One of the most rapidly developing areas of modern materials science is the study of graphene and materials on its basis. The experimental investigations have revealed different types of defects on the surface of graphene that form the ordered structures of atomic configurations. In the present work, the value of short-range order parameter for different configurations of foreign atoms in a graphene layer was calculated. The effect of various factors on the density of electronic states and electrical resistance in graphene was also investigated. The type of the ordering of foreign atoms in graphene rather than the concentration of impurities, was shown to be responsible for the change in the conductivity of graphene

Measurements, Models, Systems and Design

531 s.

Superconductivity in Weyl Semimetal Candidate MoTe2

In recent years, layered transition-metal dichalcogenides (TMDs) have attracted considerable attention because of their rich physics; for example, these materials exhibit superconductivity, charge density waves, and the valley Hall effect. As a result, TMDs have promising potential applications in electronics, catalysis, and spintronics. Despite the fact that the majority of related research focuses on semiconducting TMDs (e.g., MoS2), the characteristics of WTe2 are provoking strong interest in semimetallic TMDs with extremely large magnetoresistance, pressure-driven superconductivity, and the predicted Weyl semimetal (WSM) state. In this work, we investigate the sister compound of WTe2, MoTe2, which is also predicted to be a WSM and a quantum spin Hall insulator in bulk and monolayer form, respectively. We find that MoTe2 exhibits superconductivity with a resistive transition temperature Tc of 0.1 K. The application of a small pressure (such as 0.4 GPa) is shown to dramatically enhance the Tc, with a maximum value of 8.2 K being obtained at 11.7 GPa (a more than 80-fold increase in Tc). This yields a dome-shaped superconducting phase diagram. Further explorations into the nature of the superconductivity in this system may provide insights into the interplay between strong correlations and topological physics.Comment: 20 pages, 5 figure

The relationship between psychosocial factors and psychological symptoms in menopausal women

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