Supermodular covering knapsack polytope

The supermodular covering knapsack set is the discrete upper level set of a non-decreasing supermodular function. Submodular and supermodular knapsack sets arise naturally when modeling utilities, risk and probabilistic constraints on discrete variables. In a recent paper Atamtürk and Narayanan (2009) study the lower level set of a non-decreasing submodular function.In this complementary paper we describe pack inequalities for the supermodular covering knapsack set and investigate their separation, extensions and lifting. We give sequence-independent upper bounds and lower bounds on the lifting coefficients. Furthermore, we present a computational study on using the polyhedral results derived for solving 0–1 optimization problems over conic quadratic constraints with a branch-and-cut algorithm.     

The 0-1 bidimensional knapsack problem: Toward an efficient high-level primitive tool

Efficient codes exist for exactly solving the 0-1 knapsack problem, which is a common primitive structure in relaxation and decomposition techniques for the solution of general models. We suggest moving to a higher primitive level by using the bidimensional knapsack, which can be used to enhance linear programming or Lagrangean type classical relaxations.With the ultimate aim of providing an exact and efficient solution to the bidimensional knapsack problem, we describe here a heuristic approach based on surrogate duality. In particular, we consider the usefulness of a specific preprocessing phase before a possible enumerative phase.Extensive numerical experiments, based on test problems from the literature as well as randomly generated instances, show that our code compares favorably with the GP procedure developed by Gavish and Pirkul for the multidimensional case.     

Lifting valid inequalities for the precedence constrained knapsack problem

This paper considers the precedence constrained knapsack problem. More specifically, we are interested in classes of valid inequalities which are facet-defining for the precedence constrained knapsack polytope. We study the complexity of obtaining these facets using the standard sequential lifting procedure. Applying this procedure requires solving a combinatorial problem. For valid inequalities arising from minimal induced covers, we identify a class of lifting coefficients for which this problem can be solved in polynomial time, by using a supermodular function, and for which the values of the lifting coefficients have a combinatorial interpretation. For the remaining lifting coefficients it is shown that this optimization problem is strongly NP-hard. The same lifting procedure can be applied to (1,k)-configurations, although in this case, the same combinatorial interpretation no longer applies. We also consider K-covers, to which the same procedure need not apply in general. We show that facets of the polytope can still be generated using a similar lifting technique. For tree knapsack problems, we observe that all lifting coefficients can be obtained in polynomial time. Computational experiments indicate that these facets significantly strengthen the LP-relaxation. Received July 10, 1997 / Revised version received January 9, 1999? Published online May 12, 1999     

Zero-one integer programs with few constraints —Lower bounding theory

The zero-one integer programming problem and its special case, the multiconstraint knapsack problem frequently appear as subproblems in many combinatorial optimization problems. We present several methods for computing lower bounds on the optimal solution of the zero-one integer programming problem. They include Lagrangean, surrogate and composite relaxations. New heuristic procedures are suggested for determining good surrogate multipliers. Based on theoretical results and extensive computational testing, it is shown that for zero-one integer problems with few constraints surrogate relaxation is a viable alternative to the commonly used Lagrangean and linear programming relaxations. These results are used in a follow up paper to develop an efficient branch and bound algorithm for solving zero-one integer programming problems.     

A class of nonlinear nonseparable continuous knapsack and multiple-choice knapsack problems

This paper considers a general class of continuous, nonlinear, and nonseparable knapsack problems, special cases of which arise in numerous operations and financial contexts. We develop important properties of optimal solutions for this problem class, based on the properties of a closely related class of linear programs. Using these properties, we provide a solution method that runs in polynomial time in the number of decision variables, while also depending on the time required to solve a particular one-dimensional optimization problem. Thus, for the many applications in which this one-dimensional function is reasonably well behaved (e.g., unimodal), the resulting algorithm runs in polynomial time. We next develop a related solution approach to a class of continuous, nonlinear, and nonseparable multiple-choice knapsack problems. This algorithm runs in polynomial time in both the number of variables and the number of variants per item, while again dependent on the complexity of the same one-dimensional optimization problem as for the knapsack problem. Computational testing demonstrates the power of the proposed algorithms over a commercial global optimization software package.     

An efficient algorithm for the Lagrangean dual of nonlinear knapsack problems with additional nested constraints

This paper presents an efficient algorithm for solving the Lagrangean dual of nonlinear knapsack problems with additional nested constraints. The dual solution provides a feasible primal solution (if it exists) and associated lower and upper bounds on the optimal objective function value of the primal problem. Computational experience is cited indicating computation time, number of dual iterations, and “tightness” of the bounds.     

Solving capacitated facility location problems by Fenchel cutting planes

In this paper, we apply the Fenchel cutting planes methodology to Capacitated Facility Location problems. We select a suitable knapsack structure from which depth cuts can be obtained. Moreover, we simultaneously obtain a primal heuristic solution. The lower and upper bounds achieved by our procedure are compared with those provided by Lagrangean relaxation of the demand constraints. As the computational results show the Fenchel cutting planes methodology outperforms the Lagrangean one, both in the obtaining of the bounds and in the effectiveness of the branch and bound algorithm using each relaxation as the initial formulation.     

Knapsack problems with setups

Knapsack problems with setups find their application in many concrete industrial and financial problems. Moreover, they also arise as subproblems in a Dantzig–Wolfe decomposition approach to more complex combinatorial optimization problems, where they need to be solved repeatedly and therefore efficiently. Here, we consider the multiple-class integer knapsack problem with setups. Items are partitioned into classes whose use implies a setup cost and associated capacity consumption. Item weights are assumed to be a multiple of their class weight. The total weight of selected items and setups is bounded. The objective is to maximize the difference between the profits of selected items and the fixed costs incurred for setting-up classes. A special case is the bounded integer knapsack problem with setups where each class holds a single item and its continuous version where a fraction of an item can be selected while incurring a full setup. The paper shows the extent to which classical results for the knapsack problem can be generalized to these variants with setups. In particular, an extension of the branch-and-bound algorithm of Horowitz and Sahni is developed for problems with positive setup costs. Our direct approach is compared experimentally with the approach proposed in the literature consisting in converting the problem into a multiple choice knapsack with pseudo-polynomial size.     

An FPTAS for optimizing a class of low-rank functions over a polytope

We present a fully polynomial time approximation scheme (FPTAS) for optimizing a very general class of non-linear functions of low rank over a polytope. Our approximation scheme relies on constructing an approximate Pareto-optimal front of the linear functions which constitute the given low-rank function. In contrast to existing results in the literature, our approximation scheme does not require the assumption of quasi-concavity on the objective function. For the special case of quasi-concave function minimization, we give an alternative FPTAS, which always returns a solution which is an extreme point of the polytope. Our technique can also be used to obtain an FPTAS for combinatorial optimization problems with non-linear objective functions, for example when the objective is a product of a fixed number of linear functions. We also show that it is not possible to approximate the minimum of a general concave function over the unit hypercube to within any factor, unless P = NP. We prove this by showing a similar hardness of approximation result for supermodular function minimization, a result that may be of independent interest.     

The matroidal knapsack: A class of (often) well-solvable problems

A general class of problems, defined in terms of matroids, is recognized to include as special cases a variety of knapsack problems, subject to combinatorial constraints. A polynomial algorithm, based on Lagrangean relaxation, is proposed: A worst case and a probabilistic analysis demonstrate its ability to compute tight upper and lower bounds for the optimum, together with good approximate solutions.     

Surrogate duality in a branch-and-bound procedure for integer programming

The existence of efficient techniques such as subgradient search for solving Lagrangean duals has led to some very successful applications of Lagrangean duality in solving specially structured discrete problems. While surrogate duals have been theoretically shown to provide stronger bounds, the complexity of surrogate dual multiplier search has discouraged their employment in solving integer programs. We have recently suggested a new strategy for computing surrogate dual values that allows us to directly use established Lagrangean search methods for exploring surrogate dual multipliers. This paper considers the problem of incorporating surrogate duality within a branch-and-bound procedure for solving integer programming problems. Computational experience with randomly generated multiconstraint knapsack problems is also reported.     

An exact penalty approach for solving a class of minimization problems with boolean variables

An exact penalty approach for solving minimization problems with a concave objective function, linear constraints and Boolean variables is proposed. The penalty problems have continuous variables. An estimation of the penalty parameter which guarantees the exactness can be calculated on the base of an auxiliary problem. The results are applied to problems with an arbitrary quadratic objective function, linear constraints and Boolean variables. This leads to a modified Lagrangean approach for the latter problems. In the general case, the penalty approach is compared with a direct application of results of global optimization to a modification of the initial problem.     

Selection of Capacity Expansion Projects

The problem of determining a project selection schedule and a production-distribution-inventory schedule for each of a number of plants so as to meet the demands of multiregional markets at minimum discounted total cost during a discrete finite planning horizon is considered. We include the possibility of using inventory and/or imports to delay the expansion decision at each producing region in a transportation network. Through a problem reduction algorithm, the Lagrangean relaxation problem strengthened by the addition of a surrogate constraint becomes a 0–1 mixed integer knapsack problem. Its optimal solution, given a set of Lagrangean multipliers, can be obtained by solving at most two generally smaller 0–1 pure integer knapsack problems. The bound is usually very tight. At each iteration of the subgradient method, we generate a primal feasible solution from the Lagrangean solution. The computational results indicate that the procedure is effective in solving large problems to within acceptable error tolerances.     

Simple solution methods for separable mixed linear and quadratic knapsack problem

Quadratic knapsack problem has a central role in integer and nonlinear optimization, which has been intensively studied due to its immediate applications in many fields and theoretical reasons. Although quadratic knapsack problem can be solved using traditional nonlinear optimization methods, specialized algorithms are much faster and more reliable than the nonlinear programming solvers. In this paper, we study a mixed linear and quadratic knapsack with a convex separable objective function subject to a single linear constraint and box constraints. We investigate the structural properties of the studied problem, and develop a simple method for solving the continuous version of the problem based on bi-section search, and then we present heuristics for solving the integer version of the problem. Numerical experiments are conducted to show the effectiveness of the proposed solution methods by comparing our methods with some state of the art linear and quadratic convex solvers.     

Cone contraction and reference point methods for multi-criteria mixed integer optimization

Interactive approaches employing cone contraction for multi-criteria mixed integer optimization are introduced. In each iteration, the decision maker (DM) is asked to give a reference point (new aspiration levels). The subsequent Pareto optimal point is the reference point projected on the set of admissible objective vectors using a suitable scalarizing function. Thereby, the procedures solve a sequence of optimization problems with integer variables. In such a process, the DM provides additional preference information via pair-wise comparisons of Pareto optimal points identified. Using such preference information and assuming a quasiconcave and non-decreasing value function of the DM we restrict the set of admissible objective vectors by excluding subsets, which cannot improve over the solutions already found. The procedures terminate if all Pareto optimal solutions have been either generated or excluded. In this case, the best Pareto point found is an optimal solution. Such convergence is expected in the special case of pure integer optimization; indeed, numerical simulation tests with multi-criteria facility location models and knapsack problems indicate reasonably fast convergence, in particular, under a linear value function. We also propose a procedure to test whether or not a solution is a supported Pareto point (optimal under some linear value function).     

Lifted inequalities for 0-1 mixed integer programming: Basic theory and algorithms

We study the mixed 0-1 knapsack polytope, which is defined by a single knapsack constraint that contains 0-1 and bounded continuous variables. We develop a lifting theory for the continuous variables. In particular, we present a pseudo-polynomial algorithm for the sequential lifting of the continuous variables and we discuss its practical use.This research was supported by NSF grants DMI-0100020 and DMI-0121495Mathematics Subject Classification (2000): 90C11, 90C27     

Supermodular Functions on Finite Lattices

The supermodular order on multivariate distributions has many applications in financial and actuarial mathematics. In the particular case of finite, discrete distributions, we generalize the order to distributions on finite lattices. In this setting, we focus on the generating cone of supermodular functions because the extreme rays of that cone (modulo the modular functions) can be used as test functions to determine whether two random variables are ordered under the supermodular order. We completely determine the extreme supermodular functions in some special cases.     

An Adapted Step Size Algorithm for a 0-1 Biknapsack Lagrangean Dual

This paper deals with a new algorithm for a 0-1 bidimensional knapsack Lagrangean dual which relaxes one of the two constraints. Classical iterative algorithms generate a sequence of multipliers which converges to an optimal one. In this way, these methods generate a sequence of 0-1 one-dimensional knapsack instances. Generally, the procedure for solving each instance is considered as a black box. We propose to design a new iterative scheme in which the computation of the step size takes into account the algorithmic efficiency of each instance. Our adapted step size iterative algorithm is compared favorably with several other algorithms for the 0-1 biknapsack Lagrangean dual over difficult instances for CPLEX 7.0.     

A Lagrangean dual ascent algorithm for simple plant location problems

We propose to strengthen the separable Lagrangean relaxation of the Simple Plant Location Problem (SPLP) by using Benders inequalities generated during a Lagrangean dual ascent procedure. These inequalities are expressed in terms of the 0–1 variables only, and they can be used as knapsack constraints in the pure integer part of the Lagrangean relaxation. We show how coupling this technique with a good primal heuristic can substantially reduce integrality gaps.     

A note on a general non-linear knapsack problem

This paper discusses a general non-linear knapsack problem with a concave objective function and a single conves constraint. in particular, it includes an efficient procedure to find the continuous (relaxed) solution and a reduction process which computes tight lower and upper bounds on the integer variables. Some implicit enumeration criteria to be used in an enumeration algorithm are also suggested.