Abstract: This paper presents a neurodynamic optimization approach to bilevel quadratic programming (BQP). Based on the Karush-Kuhn-Tucker (KKT) theorem, the BQP problem is reduced to a one-level ...

PDHCG is a high-performance, GPU-accelerated implementation of the Primal-Dual Hybrid Gradient (PDHG) algorithm designed for solving large-scale Convex Quadratic Programming (QP) problems. For a ...

This video explains a simple math concept that’s even easier than solving a quadratic equation. With clear logic and intuitive steps, you’ll see how breaking problems down the right way can make math ...

Optimization is a crucial tool throughout science and technology. Large datasets and high dimensional problems create unique challenges for standard optimization techniques such as Newton’s method, ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Abstract: This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are ...

This library provides a solve_qp function to solve convex quadratic programs: $$ \begin{split} \begin{array}{ll} \underset{x}{\mbox{minimize}} & \frac{1}{2} x^T P x ...