Abstract: The aim of this article is to provide a new linear matrix inequality (LMI)-based robust multivariable super-twisting algorithm design able to deal with convex bounded model uncertainties in ...

Primal-dual hybrid gradient (PDHG) is a first-order method for saddle-point problems and convex programming introduced by Chambolle and Pock. Recently, Applegate et al.\ analyzed the behavior of PDHG ...

Some of the methods for solving the convex quadratic problem are active set, interior point, branch and bound, gradient projection, and Lagrangian methods, see [4] -[9] for more information on these ...

Nonlinear convex programming (NCP) has convex functions for both the objective and constraints, where a convex function is one with a single global minimum or maximum and curves downward or upward ...

In this paper we introduce disciplined convex-concave programming (DCCP), which combines the ideas of disciplined convex programming (DCP) with convex-concave programming (CCP). Convex-concave ...

This paper presents two different optimization-based trajectory generation methods for multiple aircrafts, i.e., mixed-integer convex programming (MICP) and difference of convex functions (DC) ...

This paper presents a new heuristic to linearise the convex quadratic programming problem. The usual Karush-Kuhn-Tucker conditions are used but in this case a linear objective function is also ...