Abstract: Estimating low-rank positive-semidefinite (PSD) matrices from symmetric rank-one measurements is of great importance in many applications, such as high-dimensional data processing, quantum ...

This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...

Abstract: This article presents two novel continuous- and discrete-time neural networks (NNs) for solving quadratic minimax problems with linear equality constraints. These two NNs are established ...

This section focuses on the key features and methods for working with linear graphs. It demonstrates how to sketch graphs from rules, derive rules from graphs, and calculate key features such as the ...

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 \textrm{s.t.}~ \quad L \leq Ax \leq ...

The mathematician hopes this method will help students avoid memorizing obtuse formulas. His secret is in generalizing two roots together instead of keeping them as separate values. Quadratic ...