Approximation schemes for deal splitting and covering integer programs with multiplicity constraints

We consider the problem of splitting an order for R goods, R≥1, among a set of sellers, each having bounded amounts of the goods, so as to minimize the total cost of the deal. In deal splitting with packages (DSP), the sellers offer packages containing combinations of the goods; in deal splitting with price tables (DST), the buyer can generate such combinations using price tables. Our problems, which often occur in online reverse auctions, generalize covering integer programs with multiplicity constraints (CIP), where we must fill up an R-dimensional bin by selecting (with a bounded number of repetitions) from a set of R-dimensional items, such that the overall cost is minimized. Thus, both DSP and DST are NP-hard, already for a single good, and hard to approximate for arbitrary number of goods.In this paper we focus on finding efficient approximations for DSP and DST instances where the number of goods is some fixed constant. In particular, we develop polynomial time approximation schemes (PTAS) for several subclasses of instances of practical interest. Our results include a PTAS for CIP in fixed dimension, and a more efficient (combinatorial) scheme for CIP_∞, where the multiplicity constraints are omitted. Our approximation scheme for CIP_∞ is based on a non-trivial application of the fast scheme for the fractional covering problem, proposed by Fleischer [L. Fleischer, A fast approximation scheme for fractional covering problems with variable upper bounds, in: Proc. of the 15th ACM-SIAM Symposium on Discrete Algorithm, 2004, pp. 994-1003].     

Approximation algorithms for a hierarchically structured bin packing problem

In this paper we study a variant of the bin packing problem in which the items to be packed are structured as the leaves of a tree. The problem is motivated by document organization and retrieval. We show that the problem is NP-hard and we give approximation algorithms for the general case and for the particular case in which all the items have the same size.     

Approximation algorithms for partially covering with edges

The edge dominating set (EDS) and edge-cover (EC) problems are classical graph covering problems in which one seeks a minimum cost collection of edges which covers the edges or vertices, respectively, of a graph. We consider the generalized partial cover version of these problems, in which failing to cover an edge, in the EDS case, or vertex, in the EC case, induces a penalty. Given a bound on the total amount of penalties that we are permitted to pay, the objective is to find a minimum cost cover with respect to this bound. We give an 8/3-approximation for generalized partial EDS. This result matches the best-known guarantee for the {0,1}-EDS problem, a specialization in which only a specified set of edges need to be covered. Moreover, 8/3 corresponds to the integrality gap of the natural formulation of the {0,1}-EDS problem. Our techniques can also be used to derive an approximation scheme for the generalized partial edge-cover problem, which is -complete even though the uniform penalty version of the partial edge-cover problem is in .     

Improved approximation algorithms for low-density instances of the Minimum Entropy Set Cover Problem

We study the approximability of instances of the minimum entropy set cover problem, parameterized by the average frequency of a random element in the covering sets. We analyze an algorithm combining a greedy approach with another one biased towards large sets. The algorithm is controlled by the percentage of elements to which we apply the biased approach. The optimal parameter choice leads to improved approximation guarantees when average element frequency is less than e.     

Adding cardinality constraints to integer programs with applications to maximum satisfiability

Max-SAT-CC is the following optimization problem: Given a formula in CNF and a bound k, find an assignment with at most k variables being set to true that maximizes the number of satisfied clauses among all such assignments. If each clause is restricted to have at most ? literals, we obtain the problem Max-?SAT-CC. Sviridenko [Algorithmica 30 (3) (2001) 398-405] designed a (1−e⁻¹)-approximation algorithm for Max-SAT-CC. This result is tight unless P=NP [U. Feige, J. ACM 45 (4) (1998) 634-652]. Sviridenko asked if it is possible to achieve a better approximation ratio in the case of Max-?SAT-CC. We answer this question in the affirmative by presenting a randomized approximation algorithm whose approximation ratio is . To do this, we develop a general technique for adding a cardinality constraint to certain integer programs. Our algorithm can be derandomized using pairwise independent random variables with small probability space.     

Analysis of Approximation Algorithms for <Emphasis Type="Italic">k</Emphasis>-Set Cover Using Factor-Revealing Linear Programs

We present new combinatorial approximation algorithms for the k-set cover problem. Previous approaches are based on extending the greedy algorithm by efficiently handling small sets. The new algorithms further extend these approaches by utilizing the natural idea of computing large packings of elements into sets of large size. Our results improve the previously best approximation bounds for the k-set cover problem for all values of k≥6. The analysis technique used could be of independent interest; the upper bound on the approximation factor is obtained by bounding the objective value of a factor-revealing linear program. A preliminary version of the results in this paper appeared in Proceedings of the 16th International Symposium on Fundamentals of Computation Theory (FCT ’07), LNCS 4639, Springer, pp. 52–63, 2007. This work was partially supported by the European Union under IST FET Integrated Project 015964 AEOLUS and by the General Secretariat for Research and Technology of the Greek Ministry of Development under programme PENED 2003.     

Approximation algorithms for orthogonal packing problems for hypercubes

Orthogonal packing problems are natural multidimensional generalizations of the classical bin packing problem and knapsack problem and occur in many different settings. The input consists of a set I={_r1,…,_rn}$I=\{r_1,\dots ,r_n\}$ of d$d$-dimensional rectangular items _ri=(_ai,1,…,_ai,d)$r_i=(a_{i,1},\dots ,a_{i,d})$ and a space Q$Q$. The task is to pack the items in an orthogonal and non-overlapping manner without using rotations into the given space. In the strip packing setting the space Q$Q$ is given by a strip of bounded basis and unlimited height. The objective is to pack all items into a strip of minimal height. In the knapsack packing setting the given space Q$Q$ is a single, usually unit sized bin and the items have associated profits _pi$p_i$. The goal is to maximize the profit of a selection of items that can be packed into the bin.     

Asymptotic fully polynomial approximation schemes for variants of open-end bin packing

We consider three variants of the open-end bin packing problem. Such variants of bin packing allow the total size of items packed into a bin to exceed the capacity of a bin, provided that a removal of the last item assigned to a bin would bring the contents of the bin below the capacity. In the first variant, this last item is the minimum sized item in the bin, that is, each bin must satisfy the property that the removal of any item should bring the total size of items in the bin below 1. The next variant (which is also known as lazy bin covering is similar to the first one, but in addition to the first condition, all bins (expect for possibly one bin) must contain a total size of items of at least 1. We show that these two problems admit asymptotic fully polynomial time approximation schemes (AFPTAS). Moreover, they turn out to be equivalent. We briefly discuss a third variant, where the input items are totally ordered, and the removal of the maximum indexed item should bring the total size of items in the bin below 1, and show that this variant is strongly NP-hard.     

Randomized approximation of bounded multicovering problems

The problem of finding approximate solutions for a subclass of multicovering problems denoted byILP(k, b) is considered. The problem involves findingx∈{0,1} ⁿ that minimizes ∑ _j x _j subject to the constraintAx≥b, whereA is a 0–1m×n matrix with at mostk ones per row,b is an integer vector, andb is the smallest entry inb. This subclass includes, for example, the Bounded Set Cover problem whenb=1, and the Vertex Cover problem whenk=2 andb=1. An approximation ratio ofk−b+1 is achievable by known deterministic algorithms. A new randomized approximation algorithm is presented, with an approximation ratio of (k−b+1)(1−(c/m)^1/(k−b+1)) for a small constantc>0. The analysis of this algorithm relies on the use of a new bound on the sum of independent Bernoulli random variables, that is of interest in its own right. The first author was supported in part by a Walter and Elise Haas Career Development Award and by a grant from the Israeli Science Foundation. This work was done white the third author was at the Department of Applied Mathematics and Computer Science, The Weizmann Institute, Rehovot 76100, Israel.     

Non-negative integer basis algorithms for linear equations with integer coefficients

A new class of non-negative integer basis algorithms for linear equations with integer coefficients is developed. Computer experiments with one of the new algorithms and comparisons with other non-negative integer basis algorithms are reported. When the total run times for collections of common simple examples typical of automated deduction applications are computed, the new algorithm has been found to be significantly faster than previous algorithms.     

Approximation algorithms for terrain guarding

We present approximation algorithms and heuristics for several variations of terrain guarding problems, where we need to guard a terrain in its entirety by a minimum number of guards. Terrain guarding has applications in telecommunications, namely in the setting up of antenna networks for wireless communication. Our approximation algorithms transform the terrain guarding instance into a Minimum Set Cover instance, which is then solved by the standard greedy approximation algorithm [J. Comput. System Sci. 9 (1974) 256-278]. The approximation algorithms achieve approximation ratios of O(logn), where n is the number of vertices in the input terrain. We also briefly discuss some heuristic approaches for solving other variations of terrain guarding problems, for which no approximation algorithms are known. These heuristic approaches do not guarantee non-trivial approximation ratios but may still yield good solutions.     

Approximation algorithms for time-dependent orienteering

The time-dependent orienteering problem is dual to the time-dependent traveling salesman problem. It consists of visiting a maximum number of sites within a given deadline. The traveling time between two sites is in general dependent on the starting time.For any ε>0, we provide a (2+ε)-approximation algorithm for the time-dependent orienteering problem which runs in polynomial time if the ratio between the maximum and minimum traveling time between any two sites is constant. No prior upper approximation bounds were known for this time-dependent problem.     

Approximation algorithms for general parallel task scheduling

A general parallel task scheduling problem is considered. A task can be processed in parallel on one of several alternative subsets of processors. The processing time of the task depends on the subset of processors assigned to the task. We first show the hardness of approximating the problem for both preemptive and nonpreemptive cases in the general setting. Next we focus on linear array network of m processors. We give an approximation algorithm of ratio O(logm) for nonpreemptive scheduling, and another algorithm of ratio 2 for preemptive scheduling. Finally, we give a nonpreemptive scheduling algorithm of ratio O(log²m) for m×m two-dimensional meshes.     

A Primal-Dual Approximation Algorithm for Partial Vertex Cover: Making Educated Guesses

We study the partial vertex cover problem. Given a graph G=(V,E), a weight function w:V→R ⁺, and an integer s, our goal is to cover all but s edges, by picking a set of vertices with minimum weight. The problem is clearly NP-hard as it generalizes the well-known vertex cover problem. We provide a primal-dual 2-approximation algorithm which runs in O(nlog n+m) time. This represents an improvement in running time from the previously known fastest algorithm. Our technique can also be used to get a 2-approximation for a more general version of the problem. In the partial capacitated vertex cover problem each vertex u comes with a capacity k _u . A solution consists of a function x:V→ℕ₀ and an orientation of all but s edges, such that the number of edges oriented toward vertex u is at most x _u k _u . Our objective is to find a cover that minimizes ∑ _v∈V x _v w _v . This is the first 2-approximation for the problem and also runs in O(nlog n+m) time. Research supported by NSF Awards CCR 0113192 and CCF 0430650, and the University of Maryland Dean’s Dissertation Fellowship.     

Approximation of min coloring by moderately exponential algorithms

We study in this note approximation algorithms for the problem of coloring the vertices of a graph with as few colors as possible, with moderately exponential running times (and using either polynomial or exponential space), better than those of exact computation. Study of approximation is performed with respect to optimality measures, the minimum number of used colors and the maximum number of unused colors.     

Approximation algorithms for shop scheduling problems with minsum objective: A correction

We present a correction to the paper, “Approximation algorithms for shop scheduling problems with minsum objective” (Journal of Scheduling 2002; 5:287–305) by Queyranne and Sviridenko. This correction provides a correct derivation of its 2eρ approximation result. Wenhua Li and Jinjiang Yuan: Project supported by NNSFC (Grant 10371112) and NSFHN (Grant 0411011200). Maurice Queyranne: Supported by research grants from NSERC, the Natural Sciences and Engineering Research Council of Canada.     

Approximation algorithms for combinatorial multicriteria optimization problems

The computational complexity of combinatorial multiple objective programming problems is investigated. NP-completeness and # P -completeness results are presented. Using two definitions of approximability, general results are presented, which outline limits for approximation algorithms. The performance of the well-known tree and Christofides' heuristics for the traveling salesman problem is investigated in the multicriteria case with respect to the two definitions of approximability.     

A faster parameterized algorithm for set packing

We present an efficient parameterized algorithm for the (k,t)-set packing problem, in which we are looking for a collection of k disjoint sets whose union consists of t elements. The complexity of the algorithm is O(²O(t)nNlogN). For the special case of sets of bounded size, this improves the O(k(ck)n) algorithm of Jia et al. [J. Algorithms 50 (1) (2004) 106].