Valid inequalities for the multi-dimensional multiple-choice 0–1 knapsack problem

This paper presents a study of the polytope defined by the minimizing form of the binary knapsack inequality, which is a greater-than-or-equal-to constraint, augmented by disjoint generalized upper bound constraints. A set of valid inequalities, called $\alpha$-cover inequalities, is characterized and dominance relationships among them are established. Both sequential and sequence-independent lifting procedures are presented to tighten an $\alpha$-cover inequality that is not facet defining. Computational results aimed at evaluating the strength of the non-dominated, sequentially, and sequence-independent lifted $\alpha$-cover inequalities are provided.     

An evolutionary heuristic for quadratic 0–1 programming

In this paper we present a heuristic algorithm for the well-known Unconstrained Quadratic 0–1 Programming Problem. The approach is based on combining solutions in a genetic paradigm and incorporates intensification algorithms used to improve solutions and speed up the method. Extensive computational experiments on instances with up to 500 variables are presented and we compare our approach both with powerful heuristic and exact algorithms from the literature establishing the effectiveness of the method in terms of solutions quality and computing time.     

Implementing an efficient fptas for the 0–1 multi-objective knapsack problem

In the present work, we are interested in the practical behavior of a new fully polynomial time approximation schemes (fptas) to solve the approximation version of the 0–1 multi-objective knapsack problem. The proposed methodology makes use of very general techniques (such as dominance relations in dynamic programming) and thus may be applicable in the implementation of fptas for other problems as well.     

A Newton’s method for the continuous quadratic knapsack problem

We introduce a new efficient method to solve the continuous quadratic knapsack problem. This is a highly structured quadratic program that appears in different contexts. The method converges after $O(n)$ iterations with overall arithmetic complexity $O(n^2)$ . Numerical experiments show that in practice the method converges in a small number of iterations with overall linear complexity, and is faster than the state-of-the-art algorithms based on median finding, variable fixing, and secant techniques.     

A new penalty parameter for linearly constrained 0–1 quadratic programming problems

The equivalence between the linearly constrained 0–1 quadratic programming problem and the continuous quadratic programming problem is studied in this note. Specifically, we show that the existing penalty parameter from the literature can be further improved.     

An exact algorithm for the 0–1 linear knapsack problem with a single continuous variable

The 0–1 linear knapsack problem with a single continuous variable (KPC) is an extension of the binary knapsack problem (KP). It is an NP-hard problem. In this paper, we show that KPC can be reduced to KP and a pseudo-knapsack problem (PKP), which is similar to the traditional knapsack problem except that the profits of items may be non-positive, and the weight sum has two sided limits on capacity. We use the Dynamic Programming algorithm COMBO (Martello et al., Manag Sci 45(3):414–424, 1999) to solve KP, and modify the branch and bound method EXPKNAP (Pisinger, Eur J Oper Res 87:175–187, 1995) for KP to solve the PKP. Numerical experiments show the efficiency of our method.     

An exact solution method for unconstrained quadratic 0–1 programming: a geometric approach

We explore in this paper certain rich geometric properties hidden behind quadratic 0–1 programming. Especially, we derive new lower bounding methods and variable fixation techniques for quadratic 0–1 optimization problems by investigating geometric features of the ellipse contour of a (perturbed) convex quadratic function. These findings further lead to some new optimality conditions for quadratic 0–1 programming. Integrating these novel solution schemes into a proposed solution algorithm of a branch-and-bound type, we obtain promising preliminary computational results.     

Testing optimality for quadratic 0–1 unconstrained problems

This paper analyses a necessary and sufficient optimality condition for quadratic pseudo-Boolean unconstrained problems. It is proved that in general testing any necessary and sufficient optimality condition is a difficult task for anyNP-hard problem. An-optimality condition is derived together with an approximation scheme to test it.This work has been supported by the Progetto Finalizzato Trasporti 2, 93.01799.PF74.Professor Paolo Carraresi died unexpectedly on March 5, 1994. At the time of his death this paper had been completed. While undertaking the final revision, the other two authors were reminded just how much they were indebted to Professor Carraresi after many years of common work together.     

On surrogating 0–1 knapsack constraints

In this note, we present a scheme for tightening 0–1 knapsack constraints based on other knapsack constraints surrogating. The preparation of this paper was partially supported by DGICYT through grant N. PB95-0407     

Fuzzy programming for multiobjective 0–1 programming problems through revised genetic algorithms

Recently, genetic algorithms (GAs), a new learning paradigm that models a natural evolution mechanism, have received a great deal of attention regarding their potential as optimization techniques for solving combinatorial optimization problems. In this paper, we focus on multiobjective 0–1 programming problems as a generalization of the traditional single objective ones. By considering the imprecise nature of human judgements, we assume that the decision maker may have fuzzy goal for each of the objective functions. After eliciting the linear membership functions through the interaction with the decision maker, we adopt the fuzzy decision of Bellman and Zadeh or minimum-operator for combining them. In order to investigate the applicability of the conventional GAs for the solution of the formulated problems, a lot of numerical simulations are performed by assuming several genetic operators. Then, instead of using the penalty function for treating the constraints, we propose three types of revised GAs which generate only feasible solutions. Illustrative numerical examples demonstrate both feasibility and efficiency of the proposed methods.     

Interactive fuzzy programming for multi-level 0–1 programming problems through genetic algorithms

In this paper, we propose interactive fuzzy programming for multi-level 0–1 programming problems through genetic algorithms. Our method is supposed to apply to hierarchical decision problems in which decision-making at each level is sequential from upper to lower level and decision makers are essentially cooperative. After determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating the satisfactory degrees of the decision makers at the upper level with considerations of overall satisfactory balance among all levels. An illustrative numerical example for three-level 0–1 programming problems is provided to demonstrate the feasibility of the proposed method.     

On triviality of the Kashiwara–Vergne problem for quadratic Lie algebras

We show that the Kashiwara–Vergne (KV) problem for quadratic Lie algebras (that is, Lie algebras admitting an invariant scalar product) reduces to the problem of representing the Campbell–Hausdorff series in the form $\ln (e^x{e}^y)=x+y+[x,a(x,y)]+[y,b(x,y)]$, where $a(x,y)$ and $b(x,y)$ are Lie series in x and y. This observation explains the existence of explicit rational solutions of the quadratic KV problem, whereas constructing an explicit rational solution of the full KV problem would probably require the knowledge of a rational Drinfeld associator. It also gives, in the case of quadratic Lie algebras, a direct proof of the Duflo theorem (implied by the KV problem). To cite this article: A. Alekseev, C. Torossian, C. R. Acad. Sci. Paris, Ser. I 347 (2009).     

Mathematical programming based heuristics for the 0–1 MIP: a survey

The 0–1 mixed integer programming problem is used for modeling many combinatorial problems, ranging from logical design to scheduling and routing as well as encompassing graph theory models for resource allocation and financial planning. This paper provides a survey of heuristics based on mathematical programming for solving 0–1 mixed integer programs (MIP). More precisely, we focus on the stand-alone heuristics for 0–1 MIP as well as those heuristics that use linear programming techniques or solve a series of linear programming models or reduced problems, deduced from the initial one, in order to produce a high quality solution of a considered problem. Our emphasis will be on how mathematical programming techniques can be used for approximate problem solving, rather than on comparing performances of heuristics.     

New convergent heuristics for 0–1 mixed integer programming

Several hybrid methods have recently been proposed for solving 0–1 mixed integer programming problems. Some of these methods are based on the complete exploration of small neighborhoods. In this paper, we present several convergent algorithms that solve a series of small sub-problems generated by exploiting information obtained from a series of relaxations. These algorithms generate a sequence of upper bounds and a sequence of lower bounds around the optimal value. First, the principle of a linear programming-based algorithm is summarized, and several enhancements of this algorithm are presented. Next, new hybrid heuristics that use linear programming and/or mixed integer programming relaxations are proposed. The mixed integer programming (MIP) relaxation diversifies the search process and introduces new constraints in the problem. This MIP relaxation also helps to reduce the gap between the final upper bound and lower bound. Our algorithms improved 14 best-known solutions from a set of 108 available and correlated instances of the 0–1 multidimensional Knapsack problem. Other encouraging results obtained for 0–1 MIP problems are also presented.     

The multiparametric 0–1-integer linear programming problem: A unified approach

We designed an algorithm for the multiparametric 0–1-integer linear programming (ILP) problem with the perturbation of the constraint matrix, the objective function and the right-hand side vector simultaneously considered. Our algorithm works by choosing an appropriate finite sequence of non-parametric mixed integer linear programming (MILP) problems in order to obtain a complete multiparametrical analysis. The algorithm may be implemented by using any software capable of solving MILP problems.     

On the polynomial mixed 0–1 fractional programming problems

This paper proposes a new method of solving polynomial mixed 0–1 fractional programming (P01FP) problems to obtain a global optimum. Given a polynomial 0–1 term x₁x₂,…,x_ny, where x_i is a 0–1 variable and y is a continuous variable; we develop a linearization technique to transfer the x₁x₂,…,x_ny term into a set of mixed 0–1 linear inequalities. Based on this technique, the P01FP can then be solved by a branch-and-bound method to obtain the global solution.     

Persistency in quadratic 0–1 optimization

This paper is concerned with persistency properties which allow the evaluation of some variables at all maximizing points of a quadratic pseudo-Boolean function. Hammer, Hansen and Simeone (1984) have proposed to determine these variables using a procedure described by Balinski for computing a strongly complementary pair of optimal primal and dual solutions for arbitrary linear programs. We propose a linear time algorithm for determining these variables from a best roof off, i.e. from a lowest upper linear bound off.