We consider the optimal scheduling of hydropower plants in a hydrothermal interconnected system. This problem, of outmost importance for large-scale power systems with a high proportion of hydraulic ...

Learn how to solve problems using linear programming. A linear programming problem involves finding the maximum or minimum value of an equation, called the objective functions, subject to a system of ...

Abstract: This letter proposes an inverse-free, noise-tolerant neurodynamic approach with a self-adaptive gain for solving time-varying quadratic programming problems (TVQPs). The proposed ...

Elliot Varoy does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their ...

This paper proposes an exact method to solve an integer indefinite quadratic bilevel problem with multiple objectives at the upper level, where the objective functions at both levels are a product of ...

Like the rest of its Big Tech cadre, Google has spent lavishly on developing generative AI models. Google’s AI can clean up your text messages and summarize the web, but the company is constantly ...

$$ \begin{array}{ll} \underset{x \in \mathbb{R}^n}{\min} \quad \frac{1}{2}\langle x,Qx \rangle + \langle c, x \rangle +\phi(x)\\ \text{s.t.} \quad \quad \quad \quad ...

The problem of approximation to a given function, or of fitting a given set of data, where the approximating function is required to have certain of its derivatives of specified sign over the whole ...

Abstract: Gauss’s principle of least constraint transforms a dynamics problem into a pure minimization framework. We show that this minimization problem is a Strongly Convex Quadratic Programming ...

How to solve linear programming and quadratic programming with inequality constraint only? For LP, I tried to use OSQP and pass the objective as (None, -c), the equality constraint as (None, None), ...