University of Tennessee researchers James Ostrowski and Rebekah Herrman are developing quantum-computing tools to tackle multi-stage stochastic decision problems in fields like energy, logistics, and ...
Globally, subtle hydrocarbon reservoirs in petroliferous basins have always been challenging targets for exploration research, with thin sand body reservoir prediction being a key focus in this field.
ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...
This study presents an optimization method for arranging lattice radiotherapy (LRT) targets to enhance the contrast between peak and valley doses, aiming to improve the treatment effectiveness and ...
Abstract: Motivated by decentralized sensing and policy evaluation problems, we consider a particular type of distributed stochastic optimization problem over a network, called the online stochastic ...
Optimization on quantum hardware is required in many applications, including Hamiltonian simulation to quantum machine learning; this entails interesting problems that must be addressed both for noisy ...
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