Concave Quadratic Programming Problems Research Articles

Using concave optimization methods for inexact quadratic programming problems with an application to waste management

Quadratic programming is potentially capable of strategic decision making in real world problems. However, practical problems rarely conform to crisp parameters, and hence the prospects of these problems with inexact parameters are inevitably higher. The existing studies regarding public welfare schemes/ organizations reveal that their objectives end up as minimization of cost functions and are governed by linear or concave quadratic programming problems. The present study proposes a method that can be applied to concave type quadratic objective function subject to linear constraints with inexact parameters. A comparison is also drawn with existing methods to establish its simplicity and efficiency. Further, a numerical example is illustrated, and finally, a waste management problem is formulated and solved using the proposed method.

On Box Constrained Concave Quadratic Optimization

This paper presents a class of differential flows to solve concave quadratic programming problems under box constraints. Some properties of the flow are given to reveal the significant relationship between the dynamic of the flow and the geometry of the feasible set. It is shown how the differential flow reaches a vertex point of the box, leading to a global minimizer of the concave quadratic programming. Some illustrative examples are also presented.

Global minimization algorithms for concave quadratic programming problems

: In this article, we consider the concave quadratic programming problem which is known to be NP hard. Based on the improved global optimality conditions by [Dür, M., Horst, R. and Locatelli, M., 1998, Necessary and sufficient global optimality conditions for convex maximization revisited, Journal of Mathematical Analysis and Applications, 217, 637–649] and [Hiriart-Urruty, J.B. and Ledyav, J.S., 1996, A note in the characterization of the global maxima of a convex function over a convex set, Journal of Convex Analysis, 3, 55–61], we develop a new approach for solving concave quadratic programming problems. The main idea of the algorithms is to generate a sequence of local minimizers either ending at a global optimal solution or at an approximate global optimal solution within a finite number of iterations. At each iteration of the algorithms we solve a number of linear programming problems with the same constraints of the original problem. We also present the convergence properties of the proposed algorithms under some conditions. The efficiency of the algorithms has been demonstrated with some numerical examples.

Cutting Plane/Tabu Search Algorithms for Low Rank Concave Quadratic Programming Problems

In this paper, we will propose an efficient heuristic algorithm for solving concave quadratic programming problems whose rank of the objective function is relatively small. This algorithm is a combination of Tuy‘s cutting plane to eliminate the feasible region and a kind of tabu-search method to find a ’good‘ vertex. We first generate a set of V of vertices and select one of these vertices as a starting point at each step, and apply tabu-search and Tuy‘s cutting plane algorithm where the list of tabu consists of those vertices eliminated by cutting planes and those newly generated vertices by cutting planes. When all vertices of the set V are eliminated, the algorithm is terminated. This algorithm need not converge to a global minimum, but it can work very well when the rank is relatively small (up to seven). The incumbent solutions are in fact globally optimal for all tested problems. We also propose an alternative algorithm by incorporating Rosen‘s hyperrectangle cut. This algorithm is more efficient than the combination of Tuy‘s cutting plane and tabu-search.

A low-rank bilinear programming approach for sub-optimal solution of the quadratic assignment problem

This paper is concerned with a new approach for solving quadratic assignment problems (QAP). We first reformulate QAP as a concave quadratic programming problem and apply an outer approximation algorithm. In addition, an improvement routine is incorporated in the final stage of the algorithm. Computational experiments on a set of standard data demonstrate that this algorithm can yield favorable results with a relatively low computational effort.

A Global Optimization Algorithm for Concave Quadratic Programming Problems

Using a global optimality criterion for concave quadratic problems due to Hiriart–Urruty and Lemaréchal, the authors present an algorithm which manages to “escape” from a local solution and lead towards the global one. In addition, this procedure recognizes the unsolvability of the problem (due to unboundedness of the objective function on the feasible region) and also generates an improving feasible direction even if the starting point does not satisfy the Karush/Kuhn/Tucker conditions. As a key subroutine, a recursive procedure will be used which determines whether or not a given symmetric $n \times n$-matrix is copositive, i.e., yields a quadratic form that is positive on a given polyhedral cone. Both this subroutine and the main body of the algorithm frequently employ the simplex method, while all other operations are elementary.

Using copositivity for global optimality criteria in concave quadratic programming problems

In this note we specify a necessary and sufficient condition for global optimality in concave quadratic minimization problems. Using this condition, it follows that, from the perspective of worst-case complexity of concave quadratic problems, the difference between local and global optimality conditions is not as large as in general. As an essential ingredient, we here use thee-subdifferential calculus via an approach of Hiriart-Urruty and Lemarechal (1990).