Efficient algorithms for solving multiconstraint zero-one knapsack problems to optimality

The multiconstraint 0–1 knapsack problem is encountered when one has to decide how to use a knapsack with multiple resource constraints. Even though the single constraint version of this problem has received a lot of attention, the multiconstraint knapsack problem has been seldom addressed. This paper deals with developing an effective solution procedure for the multiconstraint knapsack problem. Various relaxation of the problem are suggested and theoretical relations between these relaxations are pointed out. Detailed computational experiments are carried out to compare bounds produced by these relaxations. New algorithms for obtaining surrogate bounds are developed and tested. Rules for reducing problem size are suggested and shown to be effective through computational tests. Different separation, branching and bounding rules are compared using an experimental branch and bound code. An efficient branch and bound procedure is developed, tested and compared with two previously developed optimal algorithms. Solution times with the new procedure are found to be considerably lower. This procedure can also be used as a heuristic for large problems by early termination of the search tree. This scheme was tested and found to be very effective.     

Economic planning with institutional price constraints for a decentralized economy

This paper describes the relationship between market prices and shadow prices when the economy has general types of institutional price constraints. We consider a decentralized linear economy where market prices quide the decentralized behavior of each activity and the shadow prices measure the social values of resources. To measure the social values, we introduce a social objective criterion. Hence, our approach could be regarded as a central economic price control with institutional price constraints for a decentralized economy. A simple example is employed to graphically illustrate the wedges between market prices and shadow prices. It has been shown that our problem can be solved through mixed integer linear programming techniques.     

An O(n) algorithm for the multiple-choice knapsack linear program

An algorithm for solving the linear program associated with the multiple choice knapsack problem is described. The algorithm is shown to work in time linear in the number of variables. This improves the previously known best bound for this problem, and is optimal to within a constant factor.     

A new algorithm for the Integer Knapsack Problem and its parallelization

Summary A new sequential algorithm with complexityO(M ²+n) for an Integer Knapsack Problem with capacityM andn objects is proposed. The correspondentO(M ²/p+n) parallel algorithm runs on a ring machine withp processors. Computational results on both a local area network and a transputer network are reported.     

AnO (n)-algorithm for LP-knapsacks with a fixed number of GUB constraints

AnO (n+r logn) algorithm is presented for the linear programming knapsack problem withr generalized upper bounds andn variables. This result which is based on parametric programming and well-known ideas for the design of linear-time algorithms improvesO (n logn) algorithms given byGlover/Klingman [1979] andZemel [1980].

---

Zusammenfassung Es wird einO (n+r logn)-Algorithmus vorgestellt, der LP-Rucksackprobleme mitn Variablen undr verallgemeinerten oberen Schranken für die Variablen löst. Grundlage des Verfahrens ist ein parametrischer Ansatz kombiniert mit bekannten Methoden zur Konstruktion von Algorithmen mit linearer Laufzeit. Der vorgestellte Algorithmus verbessertO (n logn)-Verfahren vonGlover/Klingman [1979] undZemel [1980].

     

AnO(n logn) algorithm for the all-nearest-neighbors Problem

Given a setV ofn points ink-dimensional space, and anL _q -metric (Minkowski metric), the all-nearest-neighbors problem is defined as follows: for each pointp inV, find all those points inV–{p} that are closest top under the distance metricL _q . We give anO(n logn) algorithm for the all-nearest-neighbors problem, for fixed dimensionk and fixed metricL _q . Since there is an (n logn) lower bound, in the algebraic decision-tree model of computation, on the time complexity of any algorithm that solves the all-nearest-neighbors problem (fork=1), the running time of our algorithm is optimal up to a constant factor.This research was supported by a fellowship from the Shell Foundation. The author is currently at AT&T Bell Laboratories, Murray Hill, New Jersey, USA.     

Mobility allowance shuttle transit (MAST) services: MIP formulation and strengthening with logic constraints

We study a hybrid transportation system referred to as mobility allowance shuttle transit (MAST) where vehicles may deviate from a fixed path consisting of a few mandatory checkpoints to serve demand distributed within a proper service area. In this paper we propose a mixed integer programming (MIP) formulation for the static scheduling problem of a MAST type system. Since the problem is NP-Hard, we develop sets of logic cuts, by using reasonable assumptions on passengers’ behavior. The purpose of these constraints is to speed up the search for optimality by removing inefficient solutions from the original feasible region. Experiments show the effectiveness of the developed inequalities, achieving a reduction up to 90% of the CPU solving time for some of the instances.     

A generalized knapsack problem with variable coefficients

The ordinary knapsack problem is to find the optimal combination of items to be packed in a knapsack under a single constraint on the total allowable resources, where all coefficients in the objective function and in the constraint are constant.In this paper, a generalized knapsack problem with coefficients depending on variable parameters is proposed and discussed. Developing an effective branch and bound algorithm for this problem, the concept of relaxation and the efficiency function introduced here will play important roles. Furthermore, a relation between the algorithm and the dynamic programming approach is discussed, and subsequently it will be shown that the ordinary 0–1 knapsack problem, the linear programming knapsack problem and the single constrained linear programming problem with upper-bounded variables are special cases of the interested problem. Finally, practical applications of the problem and its computational experiences will be shown.     

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A Kind of direct methods is presented for the solution of optimal control problems with state constraints.These methods are sequential quadratic programming methods.At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and Linear approximations to constraints is solved to get a search direction for a merit function.The merit function is formulated by augmenting the Lagrangian funetion with a penalty term.A line search is carried out along the search direction to determine a step length such that the merit function is decreased.The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadrade programming methods.     

A polynomial algorithm for integer programming covering problems satisfying the integer round-up property

LetA be a non-negative matrix with integer entries and no zero column. The integer round-up property holds forA if for every integral vectorw the optimum objective value of the generalized covering problem min{1y: yA w, y 0 integer} is obtained by rounding up to the nearest integer the optimum objective value of the corresponding linear program. A polynomial time algorithm is presented that does the following: given any generalized covering problem with constraint matrixA and right hand side vectorw, the algorithm either finds an optimum solution vector for the covering problem or else it reveals that matrixA does not have the integer round-up property.     

A computational study of active set strategies in nonlinear programming with linear constraints

An essential part of many iterative methods for linearly constrained nonlinear programming problems is a procedure for determining those inequality constraints which will be active (that is, satisfied as equalities) at each iteration. We discuss experiments in which we used several strategies for identifying active constraints in conjunction with two well-known algorithms for linearly constrained optimization. The results indicate that in most cases a strategy which keeps the number of constraints in the active set as small as possible is computationally most efficient.Sponsored by the United States Army under Contract No. DAAG29-75-C-0024.     

An algorithm for linear least squares problems with equality and nonnegativity constraints

We present a new algorithm for solving a linear least squares problem with linear constraints. These are equality constraint equations and nonnegativity constraints on selected variables. This problem, while appearing to be quite special, is the core problem arising in the solution of the general linearly constrained linear least squares problem. The reduction process of the general problem to the core problem can be done in many ways. We discuss three such techniques.The method employed for solving the core problem is based on combining the equality constraints with differentially weighted least squares equations to form an augmented least squares system. This weighted least squares system, which is equivalent to a penalty function method, is solved with nonnegativity constraints on selected variables.Three types of examples are presented that illustrate applications of the algorithm. The first is rank deficient, constrained least squares curve fitting. The second is concerned with solving linear systems of algebraic equations with Hilbert matrices and bounds on the variables. The third illustrates a constrained curve fitting problem with inconsistent inequality constraints.     

Multicriteria integer programming: A (hybrid) dynamic programming recursive approach

Dynamic programming recursive equations are used to develop a procedure to obtain the set of efficient solutions to the multicriteria integer linear programming problem. An alternate method is produced by combining this procedure with branch and bound rules. Computational results are reported.     

AnO(n 3 logn) strong polynomial algorithm for an isotonic regression knapsack problem

Journal of Optimization Theory and Applications - We introduce the isotonic regression knapsack problem $$\begin{gathered} \min (1/2)\sum\limits_{i = 1}^n {\{ d_i x_i^2 - 2\alpha _i x_i \} } ,...     

AnO(n logn) algorithm for computing the link center of a simple polygon

We present an algorithm that determines the link center of a simplen-vertex polygonP inO(n logn) time. The link center of a simple polygon is the set of pointsx insideP at which the maximal link-distance fromx to any other point inP is minimized. The link distance between two pointsx andy insideP is defined to be the smallest number of straight edges in a polygonal path insideP connectingx andy. Using our algorithm we also obtain anO(n logn)-time solution to the problem of determining the link radius ofP. The link radius ofP is the maximum link distance from a point in the link center to any vertex ofP. Both results are improvements over theO(n ²) bounds previously established for these problems. The research of J.-R. Sack was supported by the Natural Sciences and Engineering Research Council of Canada.     

A new lower bound for the linear knapsack problem with general integer variables

It is well known that the linear knapsack problem with general integer variables (LKP) is NP-hard. In this paper we first introduce a special case of this problem and develop an O(n) algorithm to solve it. We then show how this algorithm can be used efficiently to obtain a lower bound for a general instance of LKP and prove that it is at least as good as the linear programming lower bound. We also present the results of a computational study that show that for certain classes of problems the proposed bound on average is tighter than other bounds proposed in the literature.     

AnO(n logn) algorithm for the voronoi diagram of a set of simple curve segments

LetX be a given set ofn circular and straight line segments in the plane where two segments may interest only at their endpoints. We introduce a new technique that computes the Voronoi diagram ofX inO(n logn) time. This result improves on several previous algorithms for special cases of the problem. The new algorithm is relatively simple, an important factor for the numerous practical applications of the Voronoi diagram.This work was supported by NSF Grants No. DCR-84-01898 and No. DCR-84-01633.     

A branch-and-cut algorithm for the stochastic uncapacitated lot-sizing problem

This paper addresses a multi-stage stochastic integer programming formulation of the uncapacitated lot-sizing problem under uncertainty. We show that the classical (ℓ,S) inequalities for the deterministic lot-sizing polytope are also valid for the stochastic lot-sizing polytope. We then extend the (ℓ,S) inequalities to a general class of valid inequalities, called the inequalities, and we establish necessary and sufficient conditions which guarantee that the inequalities are facet-defining. A separation heuristic for inequalities is developed and incorporated into a branch-and-cut algorithm. A computational study verifies the usefulness of the inequalities as cuts. This research has been supported in part by the National Science Foundation under Award number DMII-0121495.     

A partial enumeration algorithm for pure nonlinear integer programming

In this paper, a partial enumeration algorithm is developed for a class of pure IP problems. Then, a computational algorithm, named PE_SPEEDUP (partial enumeration speedup), has been developed to use whatever explicit linear constraints are present to speedup the search for a solution. The method is easy to understand and implement, yet very effective in dealing with many pure IP problems, including knapsack problems, reliability optimization, and spare allocation problems. The algorithm is based on monotonicity properties of the problem functions, and uses function values only; it does not require continuity or differentiability of the problem functions. This allows its use on problems whose functions cannot be expressed in closed algebraic form. The reliability and efficiency of the proposed algorithm and the PE_SPEEDUP algorithm has been demonstrated on some integer optimization problems taken from the literature.