Nonconvex quadratic programming (QP) is an NP-hard problem that optimizes a general quadratic function over linear constraints. This paper introduces a new global optimization algorithm for thisâ€¦ (More)

Several authors have introduced sequential relaxation techniques â€” based on linear and/or semidefinite programming â€” to generate the convex hull of 0-1 integer points in a polytope in at most nâ€¦ (More)

A short-term forecasting of the electricity price with data-driven algorithms is studied in this research. A stacked denoising autoencoder (SDA) model, a class of deep neural networks, and itsâ€¦ (More)

Nonconvex quadratic programming is an NP-hard problem that optimizes a general quadratic function over linear constraints. This paper introduces a new global optimization algorithm for this problem,â€¦ (More)

We study a class of linear programming (LP) problems motivated by large-scale machine learning applications. After reformulating the LP as a convex nonsmooth problem, we apply Nesterovâ€™s primal-dualâ€¦ (More)

In automatic differentiation, vertex elimination is one of the many methods for Jacobian accumulation. However, finding the optimal vertex elimination sequence of a computational graph is a hardâ€¦ (More)

This study presents a framework for coordinating multi-area optimal power flow with adjustable network topology in an electric power system. The modelling is accomplished in a coordinated but not aâ€¦ (More)

We present semidefinite relaxations of nonconvex, box-constrained quadratic programming, which incorporate the firstand second-order necessary optimality conditions. We compare these relaxations withâ€¦ (More)