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Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna
No full textULD Build-Up Scheduling with Logic-Based Benders Decomposition
No full textWe study a complex planning and scheduling problem arising from the build-up process of air cargo pallets and containers, collectively referred to as unit load devices (ULD), in which ULDs must be assigned to workstations for loading. Since air freight usually becomes available gradually along the planning horizon, ULD build-ups must be scheduled neither too early to avoid underutilizing ULD capacity, nor too late to avoid resource conflicts with other flights. Whenever possible, ULDs should be built up in batches, thereby giving ground handlers more freedom to rearrange cargo and utilize the ULD's capacity efficiently. The resulting scheduling problem has an intricate cost function and produces large time-expanded models, especially for longer planning horizons. We propose a logic-based Benders decomposition approach that assigns batches to time intervals and workstations in the master problem, while the actual schedule is decided in a subproblem. By choosing appropriate intervals, the subproblem becomes a feasibility problem that decomposes over the workstations. Additionally, the similarity of many batches is exploited by a strengthening procedure for no-good cuts. We benchmark our approach against a time-expanded MIP formulation from the literature on a publicly available data set. It solves 15% more instances to optimality and decreases run times by more than 50% in the geometric mean. This improvement is especially pronounced for longer planning horizons of up to one week, where the Benders approach solves over 50% instances more than the baselin
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
No full textIn this work, we analyze two of the most fundamental algorithms in geodesically convex optimization: Riemannian gradient descent and (possibly inexact) Riemannian proximal point. We quantify their rates of convergence and produce different variants with several trade-offs. Crucially, we show the iterates naturally stay in a ball around an optimizer, of radius depending on the initial distance and, in some cases, on the curvature. Previous works simply assumed bounded iterates, resulting in rates that were not fully quantified. We also provide an implementable inexact proximal point algorithm and prove several new useful properties of Riemannian proximal methods: they work when positive curvature is present, the proximal operator does not move points away from any optimizer, and we quantify the smoothness of its induced Moreau envelope. Further, we explore beyond our theory with empirical tests
Photocatalytic magnetic microgyroscopes with activity-tunable precessional dynamics
No full textMagnetic nano/microrotors are passive elements that spin around an axis due to an external rotating field while remaining confined to a close plane. They have been used to date in different applications related to fluid mixing, drug delivery or biomedicine. Here we realize an active version of a magnetic microgyroscope which is simultaneously driven by a photo-activated catalytic reaction and a rotating magnetic field. We investigate the uplift dynamics of this colloidal spinner when it stands up and precesses around its long axis while self-propelling due to the light induced decomposition of hydrogen peroxide in water. By combining experiments with theory, we show that activity emerging from the cooperative action of phoretic and osmotic forces effectively increase the gravitational torque which counteracts the magnetic and viscous ones, and carefully measure its contribution
Adaptive Gaussian process regression for inverse problems. ALGORITMY 2024
No full textThis submission contains the code used for the proceedings paper for ALGORITMY 2024, concerning surrogate model-based inverse problems.
Version 2 modified plot appearances and fixed some minor typos
A Ridge-based Approach for Extraction and Visualization of 3D Atmospheric Fronts
No full textAn atmospheric front is an imaginary surface that separates two distinct air masses and is commonly defined as the warm-air side of a frontal zone with high gradients of atmospheric temperature and humidity. These fronts are a widely used conceptual model in meteorology, which are often encountered in the literature as two-dimensional (2D) front lines on surface analysis charts. This paper presents a method for computing three-dimensional (3D) atmospheric fronts as surfaces that is capable of extracting continuous and well-confined features suitable for 3D visual analysis, spatio-temporal tracking, and statistical analyses. Recently developed contour-based methods for 3D front extraction rely on computing the third derivative of a moist potential temperature field. Additionally, they require the field to be smoothed to obtain continuous large-scale structures. This paper demonstrates the feasibility of an alternative method to front extraction using ridge surface computation. The proposed method requires only the sec- ond derivative of the input field and produces accurate structures even from unsmoothed data. An application of the ridge-based method to a data set corresponding to Cyclone Friederike demonstrates its benefits and utility towards visual analysis of the full 3D structure of fronts
