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Fifty years ago, transportation and logistics problems were primarily analyzed either from a supply-side or a demand-side perspective, with the fields of operations research and demand modeling evolving separately. Since then, there has been a growing inte ...
We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...
Since the dawn of humanity, human beings seeked to light their surroundings for their well-being, security and development. The efficiency of ancient lighting devices, e.g. oil lamps or candles, was in the order of 0.03-0.04% and jumped to 0.4-0.6% with th ...
GaN exhibits a decomposition tendency for temperatures far below its melting point and common growth temperatures used in metal-organic vapour phase epitaxy (MOVPE).This characteristic is known to be a major obstacle for realising GaN bulk substrate. There ...
We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...
This study applies Six-Sigma DMAIC methodology to enhance the efficiency of a manufacturing production line, specifically targeting the "gaps" between planned and actual working time. Key findings reveal that meetings, scanning, follow-up activities, walki ...
In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...
A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...
In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...
Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...
