Dietary Preference of the Queensnake (Regina septemvittata)

The Queensnake (Regina septemvittata) is a small secretive water snake found throughout the eastern United States. Once common, their numbers have declined to the extent that they are now threatened throughout most of their range, largely the result of pollutant-based reduction in prey species. These snakes are assumed to eat molted crayfish exclusively. For some common crayfish species, molting happens only twice a summer during a two- week period. It has not been documented if Queensnakes eat anything besides crayfish on a regular basis. The purpose of this study was to determine the prey preference of Queensnakes with particular focus on crayfish species. Because Queensnakes are considered to be dietary specialists, they are at great risk of becoming extirpated should their food source diminish and are therefore effective bio-indicators for the streams where they live. Data collected from this study will better enable biologists to determine what habitats and prey items are required to conserve this species

Convergence Analysis of Fixed Point Chance Constrained Optimal Power Flow Problems

For optimal power flow problems with chance constraints, a particularly effective method is based on a fixed point iteration applied to a sequence of deterministic power flow problems. However, a priori, the convergence of such an approach is not necessarily guaranteed. This article analyses the convergence conditions for this fixed point approach, and reports numerical experiments including for large IEEE networks

An LDLTLDL^T Trust-Region Quasi-Newton Method

For quasi-Newton methods in unconstrained minimization, it is valuable to develop methods that are robust, i.e., methods that converge on a large number of problems. Trust-region algorithms are often regarded to be more robust than line-search methods, however, because trust-region methods are computationally more expensive, the most popular quasi-Newton implementations use line-search methods. To fill this gap, we develop a trust-region method that updates an $LDL^T$ factorization, scales quadratically with the size of the problem, and is competitive with a conventional line-search method

PLSS: A Projected Linear Systems Solver

We propose iterative projection methods for solving square or rectangular consistent linear systems $Ax = b$. Projection methods use sketching matrices (possibly randomized) to generate a sequence of small projected subproblems, but even the smaller systems can be costly. We develop a process that appends one column each iteration to the sketching matrix and that converges in a finite number of iterations independent of whether the sketch is random or deterministic. In general, our process generates orthogonal updates to the approximate solution $x_k$. By choosing the sketch to be the set of all previous residuals, we obtain a simple recursive update and convergence in at most rank($A$) iterations (in exact arithmetic). By choosing a sequence of identity columns for the sketch, we develop a generalization of the Kaczmarz method. In experiments on large sparse systems, our method (PLSS) with residual sketches is competitive with LSQR, and our method with residual and identity sketches compares favorably to state-of-the-art randomized methods

Anisotropic Assembly of Colloidal Nanoparticles: Exploiting Substrate Crystallinity

We show that the crystal structure of a substrate can be exploited to drive the anisotropic assembly of colloidal nanoparticles. Pentanethiol-passivated Au particles of approximately 2 nm diameter deposited from toluene onto hydrogen-passivated Si(111) surfaces form linear assemblies (rods) with a narrow width distribution. The rod orientations mirror the substrate symmetry, with a high degree of alignment along principal crystallographic axes of the Si(111) surface. There is a strong preference for anisotropic growth with rod widths substantially more tightly distributed than lengths. Entropic trapping of nanoparticles provides a plausible explanation for the formation of the anisotropic assemblies we observe

Shape-Changing Trust-Region Methods Using Multipoint Symmetric Secant Matrices

In this work, we consider methods for large-scale and nonconvex unconstrained optimization. We propose a new trust-region method whose subproblem is defined using a so-called "shape-changing" norm together with densely-initialized multipoint symmetric secant (MSS) matrices to approximate the Hessian. Shape-changing norms and dense initializations have been successfully used in the context of traditional quasi-Newton methods, but have yet to be explored in the case of MSS methods. Numerical results suggest that trust-region methods that use densely-initialized MSS matrices together with shape-changing norms outperform MSS with other trust-region methods