† Department of Chemical Engineering, Graduate School of Engineering, Tohoku University, 6-6-07 Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan ‡ Department of Environmental Study for ...

The minimization of matrix bandwidth is a cornerstone challenge in computational linear algebra and graph theory, with direct implications for the efficiency of numerical solvers, finite-element ...

Abstract: Orthogonal frequency-division multiplexing (OFDM)-based joint radar communication (JRC) systems have signal distortion when the transmit signal has a high peak-to-average power ratio (PAPR).

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution ...

Understanding the mechanism of how neural networks learn features from data is a fundamental problem in machine learning. Our work explicitly connects the mechanism of neural feature learning to a ...

Linear Programming has been used to solve optimization problems in banking, forestry, petroleum, and medical industries. Optimization can be completed with linear and non linear models. There are ...

Operations research professionals need the best linear programming software for Windows to solve optimization problems. Below we offer a tool that comes with all the essentials to help you perform a ...

ReHLine-Python is the official Python implementation of ReHLine, a powerful solver for large-scale empirical risk minimization (ERM) problems with convex piecewise linear-quadratic (PLQ) loss ...

Abstract: We illustrate some recent results on exact solutions to discrete-time l 1-norm minimization problems with convolution constraints. A fixed-point property for this class of problems is ...

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged ...