Abstract: In this article, we propose a minimum simplex convolutional network (MiSiCNet) for deep hyperspectral unmixing. Unlike all the deep learning-based unmixing methods proposed in the literature ...
HiGHS is a high performance serial and parallel solver for large scale sparse linear optimization problems of the form $$ \min \quad \dfrac{1}{2}x^TQx + c^Tx \qquad ...
Solow teaches the use of quantitative methods such as statistics and modeling for use in decision making to Weatherhead's MBA, master's and PhD students. Solow uses mathematical models, analysis and ...
A new Ph.D. program in the physics department designed for students who want to be at the forefront of cosmic discovery, data science and interdisciplinary research. The Mellon College of Science is ...
Abstract: A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on ...
Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent ...
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