The aim of this paper is to propose a solution algorithm for a particular class of rank-two nonconvex programs having a polyhedral feasible region. The algorithm lies within the class of the so called “optimal level solutions” parametric methods. The subproblems obtained by means of this parametrical approach are quadratic convex ones, but not necessarily neither strictly convex nor linear. For this very reason, in order to solve in an unifying framework all of the considered rank-two nonconvex programs a new approach needs to be proposed. The efficiency of the algorithm is improved by means of the use of underestimation functions. The results of a computational test are provided and discussed
In this paper we derive formulas for computing graphical derivatives of the (possibly multivalued) s...
This paper introduces constructing convex-relaxed programs for nonconvex optimization problems. Bran...
Lecture Notes in Computer Science book series (LNCS, volume 11653)In a (linear) parametric optimizat...
AbstractThe aim of this paper is to propose a solution algorithm for a particular class of rank-two ...
The aim of this paper is to propose a solution algorithm for a particular class of rank-two nonconve...
In this paper a solution algorithm for a class of rank-two nonconvex programs having a polyhedral fe...
Low rank problems are nonlinear minimization problems in which the objective function, by means of a...
Low rank problems are nothing but nonlinear minimization problems over polyhedrons where a linear tr...
In this paper a method to solve two different classes of low-rank gener- alized linear programs havi...
The aim of this paper is two-fold. First, the so-called ‘optimal level solutions’ method is describe...
In this paper, we developed a novel algorithmic approach for thesolution of multi-parametric non-con...
The aim of this paper is to show how a wide class of generalized quadratic programs can be solved, i...
This paper addresses a practical method for minimizing a class of saddle functions f: R "-+ R1 ...
In this paper, we focus on a special nonconvex quadratic program whose feasible set is a structured ...
Abstract This paper presents a linear decomposition approach for a class of nonconvex programming pr...
In this paper we derive formulas for computing graphical derivatives of the (possibly multivalued) s...
This paper introduces constructing convex-relaxed programs for nonconvex optimization problems. Bran...
Lecture Notes in Computer Science book series (LNCS, volume 11653)In a (linear) parametric optimizat...
AbstractThe aim of this paper is to propose a solution algorithm for a particular class of rank-two ...
The aim of this paper is to propose a solution algorithm for a particular class of rank-two nonconve...
In this paper a solution algorithm for a class of rank-two nonconvex programs having a polyhedral fe...
Low rank problems are nonlinear minimization problems in which the objective function, by means of a...
Low rank problems are nothing but nonlinear minimization problems over polyhedrons where a linear tr...
In this paper a method to solve two different classes of low-rank gener- alized linear programs havi...
The aim of this paper is two-fold. First, the so-called ‘optimal level solutions’ method is describe...
In this paper, we developed a novel algorithmic approach for thesolution of multi-parametric non-con...
The aim of this paper is to show how a wide class of generalized quadratic programs can be solved, i...
This paper addresses a practical method for minimizing a class of saddle functions f: R "-+ R1 ...
In this paper, we focus on a special nonconvex quadratic program whose feasible set is a structured ...
Abstract This paper presents a linear decomposition approach for a class of nonconvex programming pr...
In this paper we derive formulas for computing graphical derivatives of the (possibly multivalued) s...
This paper introduces constructing convex-relaxed programs for nonconvex optimization problems. Bran...
Lecture Notes in Computer Science book series (LNCS, volume 11653)In a (linear) parametric optimizat...