Alain Billionnet

Using a Mixed Integer Quadratic Programming Solver for the Unconstrained Quadratic 0-1 Problem

In this paper, we consider problem (P) of minimizing a quadratic function q(x)=xtQx+ctx of binary variables. Our main idea is to use the recent Mixed Integer Quadratic Programming (MIQP) solvers. But, for this, we have to first convexify the objective function q(x). A classical trick is to raise up the diagonal entries of Q by a vector u until (Q+diag(u)) is positive semidefinite. Then, using the fact that xi2=xi, we can obtain an equivalent convex objective function, which can then be handled by an MIQP solver. Hence, computing a suitable vector u constitutes a preprocessing phase in this exact solution method. We devise two different preprocessing methods. The first one is straightforward and consists in computing the smallest eigenvalue of Q. In the second method, vector u is obtained once a classical SDP relaxation of (P) is solved.We carry out computational tests using the generator of (Pardalos and Rodgers, 1990) and we compare our two solution methods to several other exact solution methods. Furthermore, we report computational results for the max-cut problem.

An efficient algorithm for a task allocation problem

This paper presents an efficient algorithm to solve one of the task allocation problems. Task assignment in an heterogeneous multiple processors system is investigated. The cost function is formulated in order to measure the intertask communication and processing costs in an uncapacited network. A formulation of the problem in terms of the minimization of a submodular quadratic pseudo-Boolean function with assignment constraints is then presented. The use of a branch-and-bound algorithm using a Lagrangean relaxation of these constraints is proposed. The lower bound is the value of an approximate solution to the Lagrangean dual problem. A zero-duality gap, that is, a saddle point, is characterized by checking the consistency of a pseudo-Boolean equation. A solution is found for large-scale problems (e.g., 20 processors, 50 tasks, and 200 task communications or 10 processors, 100 tasks, and 300 task communications). Excellent experimental results were obtained which are due to the weak frequency of a duality gap and the efficient characterization of the zero-gap (for practical purposes, this is achieved in linear time). Moreover, from the saddle point, it is possible to derive the optimal task assignment.

Integer programming to schedule a hierarchical workforce with variable demands

We consider a hierarchical workforce in which a higher qualified worker can substitute for a lower qualified one, but not vice versa. Daily labor requirements within a week may vary, but each worker must receive n off-days in the week. This problem has been considered by Hung (R. Hung, Eur. J. Oper. Res. 78(1) (1994) 49–57), who discusses a necessary and sufficient condition for a labor mix to be feasible and presents a simple one-pass method that frequently gives the least cost labor mix. We show in this paper that the integer programming approach is well suited for solving this problem: the definition of the integer programming model is simple, its implementation is immediate by using, for example, the Mathematical programming language (MPL) and the integer programming solver XA, the computation times are low (generally a few seconds on a small microcomputer) and finally the powerful of the integer programming approach allows us to extend the model in two interesting directions.

Improving the performance of standard solvers for quadratic 0-1 programs by a tight convex reformulation: The QCR method

Let (QP) be a 0-1 quadratic program which consists in minimizing a quadratic function subject to linear equality constraints. In this paper, we present QCR, a general method to reformulate (QP) into an equivalent 0-1 program with a convex quadratic objective function. The reformulated problem can then be efficiently solved by a classical branch-and-bound algorithm, based on continuous relaxation. This idea is already present in the literature and used in standard solvers such as CPLEX. Our objective in this work was to find a convex reformulation whose continuous relaxation bound is, moreover, as tight as possible. From this point of view, we show that QCR is optimal in a certain sense. State-of-the-art reformulation methods mainly operate a perturbation of the diagonal terms and are valid for any {0,1} vector. The innovation of QCR comes from the fact that the reformulation also uses the equality constraints and is valid on the feasible solution domain only. Hence, the superiority of QCR holds by construction. However, reformulation by QCR requires the solution of a semidefinite program which can be costly from the running time point of view. We carry out a computational experience on three different combinatorial optimization problems showing that the costly computational time of reformulation by QCR can however result in a drastically more efficient branch-and-bound phase. Moreover, our new approach is competitive with very specific methods applied to particular optimization problems.

Linear programming for the 0-1 quadratic knapsack problem

Abstract In this paper we consider the quadratic knapsack problem which consists in maximizing a positive quadratic pseudo-Boolean function subject to a linear capacity constraint. We propose a new method for computing an upper bound. This method is based on the solution of a continuous linear program constructed by adding to a classical linearization of the problem some constraints rebundant in 0–1 variables but nonredundant in continuous variables. The obtained upper bound is better than the bounds given by other known methods. We also propose an algorithm for computing a good feasible solution. This algorithm is an elaboration of the heuristic methods proposed by Chaillou, Hansen and Mahieu and by Gallo, Hammer and Simeone. The relative error between this feasible solution and the optimum solution is generally less than 1%. We show how these upper and lower bounds can be efficiently used to determine the values of some variables at the optimum. Finally we propose a branch-and-bound algorithm for solving the quadratic knapsack problem and report extensive computational tests.

An exact method based on Lagrangian decomposition for the 0-1 quadratic knapsack problem

Abstract The 0–1 quadratic knapsack problem (QKP) consists in maximizing a quadratic pseudo-Boolean function with positive coefficients subject to a linear capacity constraint. In this paper we present an exact method to solve this problem. This method makes use of the computation of an upper bound for (QKP) which is derived from Lagrangian decomposition. It allows us to find the optimum of instances with up to 150 variables whatever their density, and with up to 300 variables for medium and low density.

Using Integer Programming to Solve the Train-Platforming Problem

We consider the problem of assigning trains to the available tracks at a railway station, given the daily timetable and the structural and operational constraints. This trainplatforming problem is a key problem in railway station operations, and for a large station, many working days are required for an expert planner to construct the train-platforming. This problem was studied in De Luca Cardillo and Mione (1998), where it is formulated as a graph-coloring problem. These authors propose to solve it by an efficient heuristic algorithm combined with reduction techniques. In this paper, we show that integer programming is a very interesting tool to exactly solve the train-platforming problem as formulated in De Luca Cardillo and Mione (1998) by a graph-coloring problem. The fact of being able to solve it in an exact way by using an integer-programming solver obviously has many advantages. Some computational results are reported.

Maximizing a supermodular pseudoboolean function: A polynomial algorithm for supermodular cubic functions

Abstract The problem of maximizing a pseudoboolean function (or equivalently a set function) which is supermodular, has many interesting applications e.g. in combinatorial optimization, Operations Research etc. Up to now, a number of special cases of pseudoboolean functions have been known, the maximization of which can be converted into the search for a maximum flow in an associated network. These were essentially the so-called negative-positive pseudoboolean functions (which, as will be noted here, turn out to be supermodular). First it is shown here how these results on negative-positive functions can be more easily derived by using the concept of conflict graph. The conflict graph approach is then generalized to extend the class of problems amenable to maximum network flow problems to the whole set of cubic supermodular pseudoboolean functions.

Minimization of a quadratic pseudo-Boolean function

Abstract We present a branch and bound algorithm for minimizing a quadratic pseudo-Boolean function f ( x ). At each node of the search tree the lower bound is computed in three phases and is equal to b 1 + b 2 + b 3 . Computation of b 1 is based upon roof duality, b 2 uses the characterization of some positive quadratic posiforms associated with the directed cycles of the implication graph of Aspvall, Plass and Tarjan and b 3 is computed by searching in a posiform of degree 4 some subfunctions which cannot be equal to zero. These subfunctions are found by using the notion of implication between literals. Computational results on several hundred test problems with up to 100 variables demonstrate the efficiency of this lower bound.

A new upper bound for the 0-1 quadratic knapsack problem

The 0-1 quadratic knapsack problem (QKP) consists in maximizing a positive quadratic pseudo-Boolean function subject to a linear capacity constraint. We present in this paper a new method, based on Lagrangian decomposition, for computing an upper bound of QKP. We report computational experiments which demonstrate the sharpness of the bound (relative error very often less than 1%) for large size instances (up to 500 variables).