The average-case analysis of some on-line algorithms for bin packing

In this paper we give tighter bounds than were previously known for the performance of the bin packing algorithms Best Fit and First Fit when the inputs are uniformly distributed on [0, 1]. We also give a general lower bound for the performance of any on-line bin packing algorithm on this distribution. We prove these results by analyzing optimal matchings on points randomly distributed in a unit square. We give a new lower bound for the up-right matching problem. A preliminary version of this paper appeared inProc. 25th IEEE Symposium on Foundations of Computer Science, 193–200. This research was done while the author was at the Massachusetts Institute of Technology Dept. of Mathematics and was supported by a NSF Graduate Fellowship and by Air Force grant OSR-82-0326.     

Improved processor bounds for combinatorial problems in RNC

Our main result improves the known processor bound by a factor ofn ⁴ (maintaining the expected parallel running time,O(log³ n)) for the following important problem:find a perfect matching in a general or in a bipartite graph with n vertices. A solution to that problem is used in parallel algorithms for several combinatorial problems, in particular for the problems of finding i) a (perfect) matching of maximum weight, ii) a maximum cardinality matching, iii) a matching of maximum vertex weight, iv) a maximums-t flow in a digraph with unit edge capacities. Consequently the known algorithms for those problems are substantially improved.The results of this paper have been presented at the 26-th Annual IEEE Symp. FOCS, Portland, Oregon (October 1985).Partially supported by NSF Grants MCS 8303139 and DCR 8511713.Supporeted by NSF Grants MCS 8203232 and DCR 8507573.     

Euclidean semi-matchings of random samples

A linear programming relaxation of the minimal matching problem is studied for graphs with edge weights determined by the distances between points in a Euclidean space. The relaxed problem has a simple geometric interpretation that suggests the name minimal semi-matching. The main result is the determination of the asymptotic behavior of the length of the minimal semi-matching. It is analogous to the theorem of Beardwood, Halton and Hammersley (1959) on the asymptotic behavior of the traveling salesman problem. Associated results on the length of non-random Euclidean semi-matchings and large deviation inequalities for random semi-matchings are also given.Research supported in part by NSF Grant #DMS-8812868, ARO contract DAAL03-89-G-0092.P001, AFOSR-89-08301.A and NSA-MDA-904-89-2034.     

Quasi-planar graphs have a linear number of edges

A graph is calledquasi-planar if it can be drawn in the plane so that no three of its edges are pairwise crossing. It is shown that the maximum number of edges of a quasi-planar graph withn vertices isO(n).Work on this paper by Pankaj K. Agarwal, Boris Aronov and Micha Sharir has been supported by a grant from the U.S.-Israeli Binational Science Foundation. Work on this paper by Pankaj K. Agarwal has also been supported by NSF Grant CCR-93-01259, by an Army Research Office MURI grant DAAH04-96-1-0013, by an NYI award, and by matching funds from Xerox Corporation. Work on this paper by Boris Aronov has also been supported by NSF Grant CCR-92-11541 and by a Sloan Research Fellowship. Work on this paper by János Pach, Richard Pollack, and Micha Sharir has been supported by NSF Grants CCR-91-22103 and CCR-94-24398. Work by János Pach was also supported by Grant OTKA-4269 and by a CUNY Research Award. Work by Richard Pollack was also supported by NSF Grants CCR-94-02640 and DMS-94-00293. Work by Micha Sharir was also supported by NSF Grant CCR-93-11127, by a Max-Planck Research Award, and by grants from the Israel Science Fund administered by the Israeli Academy of Sciences, and the G.I.F., the German-Israeli Foundation for Scientific Research and Development. Part of the work on this paper was done during the participation of the first four authors in the Special Semester on Computational and Combinatorial Geometry organized by the Mathematical Research Institute of Tel Aviv University, Spring 1995.     

A random 1-011-011-01algorithm for depth first search

In this paper we present a fast parallel algorithm for constructing a depth first search tree for an undirected graph. The algorithm is anRNC algorithm, meaning that it is a probabilistic algorithm that runs in polylog time using a polynomial number of processors on aP-RAM. The run time of the algorithm isO(T _MM(n) log³ n), and the number of processors used isP _MM (n) whereT _MM(n) andP _MM(n) are the time and number of processors needed to find a minimum weight perfect matching on ann vertex graph with maximum edge weightn.This research was done while the first author was visiting the Mathematical Research Institute in Berkeley. Research supported in part by NSF grant 8120790.Supported by Air Force Grant AFOSR-85-0203A.     

A las vegas rnc algorithm for maximum matching

Recently two randomized algorithms were discovered that find a maximum matching in an arbitrary graph in polylog time, when run on a parallel random access machine. Both are Monte Carlo algorithms — they have the drawback that with non-zero probability the output is a non-maximum matching. We use the min-max formula for the size of a maximum matching to convert any Monte Carlo maximum matching algorithm into a Las Vegas (error-free) one. The resulting algorithm returns (with high probability) a maximum matching and a certificate proving that the matching is indeed maximum. Research supported by DARPA grant N00039-84-C-0098 and by a US Army Research Office fellowship.     

A primal-dual approximation algorithm for generalized steiner network problems

We present the first polynomial-time approximation algorithm for finding a minimum-cost subgraph having at least a specified number of edges in each cut. This class of problems includes, among others, the generalized Steiner network problem, also called the survivable network design problem. Ifk is the maximum cut requirement of the problem, our solution comes within a factor of 2k of optimal. Our algorithm is primal-dual and shows the importance of this technique in designing approximation algorithms.Research supported by an NSF Graduate Fellowship, DARPA contracts N00014-91-J-1698 and N00014-92-J-1799, and AT&T Bell Laboratories.Research supported in part by Air Force contract F49620-92-J-0125 and DARPA contract N00014-92-J-1799.Part of this work was done while the author was visiting AT&T Bell Laboratories and Bellcore.     

An optimal lower bound on the number of variables for graph identification

In this paper we show that (n) variables are needed for first-order logic with counting to identify graphs onn vertices. Thek-variable language with counting is equivalent to the (k–1)-dimensional Weisfeiler-Lehman method. We thus settle a long-standing open problem. Previously it was an open question whether or not 4 variables suffice. Our lower bound remains true over a set of graphs of color class size 4. This contrasts sharply with the fact that 3 variables suffice to identify all graphs of color class size 3, and 2 variables suffice to identify almost all graphs. Our lower bound is optimal up to multiplication by a constant becausen variables obviously suffice to identify graphs onn vertices.Research supported by NSF grant CCR-8709818.Research supported by NSF grant CCR-8805978 and Pennsylvania State University Research Initiation grant 428-45.Research supported by NSF grants DCR-8603346 and CCR-8806308.     

Approximation algorithms for general packing problems and their application to the multicast congestion problem

In this paper we present approximation algorithms based on a Lagrangian decomposition via a logarithmic potential reduction to solve a general packing or min–max resource sharing problem with M non-negative convex constraints on a convex set B. We generalize a method by Grigoriadis et al. to the case with weak approximate block solvers (i.e., with only constant, logarithmic or even worse approximation ratios). Given an accuracy , we show that our algorithm needs calls to the block solver, a bound independent of the data and the approximation ratio of the block solver. For small approximation ratios the algorithm needs calls to the block solver. As an application we study the problem of minimizing the maximum edge congestion in a multicast communication network. Interestingly the block problem here is the classical Steiner tree problem that can be solved only approximately. We show how to use approximation algorithms for the Steiner tree problem to solve the multicast congestion problem approximately. This work was done in part when the second author was studying at the University of Kiel. This paper combines our extended abstracts of the 2nd IFIP International Conference on Theoretical Computer Science, TCS 2002, Montréal, Canada and the 3rd Workshop on Approximation and Randomization Algorithms in Communication Networks, ARACNE 2002, Roma, Italy. This research was supported in part by the DFG - Graduiertenkolleg, Effiziente Algorithmen und Mehrskalenmethoden; by the EU Thematic Network APPOL I + II, Approximation and Online Algorithms, IST-1999-14084 and IST-2001-32007; by the EU Research Training Network ARACNE, Approximation and Randomized Algorithms in Communication Networks, HPRN-CT-1999-00112; by the EU Project CRESCCO, Critical Resource Sharing for Cooperation in Complex Systems, IST-2001-33135. The second author was also supported by an MITACS grant of Canada; and by the NSERC Discovery Grant DG 5-48923.     

Hammersley's interacting particle process and longest increasing subsequences

Summary In a famous paper [8] Hammersley investigated the lengthL _n of the longest increasing subsequence of a randomn-permutation. Implicit in that paper is a certain one-dimensional continuous-space interacting particle process. By studying a hydrodynamical limit for Hammersley's process we show by fairly “soft” arguments that limn ^′1/2 EL _n =2. This is a known result, but previous proofs [14, 11] relied on hard analysis of combinatorial asymptotics. Research supported by NSF Grant MCS 92-24857 and the Miller Institute for Basic Research in Science Research supported by NSF Grant DMS92-04864     

Path problems in skew-symmetric graphs

We study path problems in skew-symmetric graphs. These problems generalize the standard graph reachability and shortest path problems. We establish combinatorial solvability criteria and duality relations for the skew-symmetric path problems and use them to design efficient algorithms for these problems. The algorithms presented are competitive with the fastest algorithms for the standard problems.This research was done while the first author was at Stanford University Computer Science Department, supported in part by ONR Office of Naval Research Young Investigator Award N00014-91-J-1855, NSF Presidential Young Investigator Grant CCR-8858097 with matching funds from AT&T, DEC, and 3M, and a grant from Powell Foundation.This research was done while the second author was visiting Stanford University Computer Science Department and supported by the above mentioned NSF and Powell Foundation Grants.     

Alternating cycle-free matchings

The investigation of alternating cycle-free matchings is motivated by the Jump-number problem for partially ordered sets and the problem of counting maximum cardinality matchings in hexagonal systems.We show that the problem of deciding whether a given chordal bipartite graph has an alternating cycle-free matching of a given cardinality is NP-complete. A weaker result, for bipartite graphs only, has been known for some time. Also, the alternating cycle-free matching problem remains NP-complete for strongly chordal split graphs of diameter 2.In contrast, we give algorithms to solve the alternating cycle-free matching problem in polynomial time for bipartite distance hereditary graphs (time O(m ²) on graphs with m edges) and distance hereditary graphs (time O(m ⁵)).     

On the need for special purpose algorithms for minimax eigenvalue problems

It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimization problems involving smooth objective and constraint functions. This result seems very appealing since minimax eigenvalue problems are known to be typically nondifferentiable. In this paper, we show, however, that general purpose nonlinear optimization algorithms usually fail to find a solution to these smooth problems even in the simple case of minimization of the maximum eigenvalue of an affine family of symmetric matrices, a convex problem for which efficient algorithms are available.This work was supported in part by NSF Engineering Research Centers Program No. NSFD-CDR-88-03012 and NSF Grant DMC-84-20740. The author wishes to thank Drs. M. K. H. Fan and A. L. Tits for their many useful suggestions.     

Asymptotics in the random assignment problem

Summary We show that, in the usual probabilistic model for the random assignment problem, the optimal cost tends to a limit constant in probability and in expectation. The method involves construction of an infinite limit structure, in terms of which the limit constant is defined. But we cannot improve on the known numerical bounds for the limit.Research supported by NSF Grant MCS90-01710     

On the Value of a Random Minimum Weight Steiner Tree

Consider a complete graph on n vertices with edge weights chosen randomly and independently from an exponential distribution with parameter 1. Fix k vertices and consider the minimum weight Steiner tree which contains these vertices. We prove that with high probability the weight of this tree is (1+o(1))(k-1)(log n-log k)/n when k =o(n) and n.* Research supported in part by NSF grant DSM9971788 Research supported in part by NSF grants DMS-0106589, CCR-9987845 and by the State of New Jersey. Part of this research was done while visiting IBM T. J. Watson Research Center.     

An Ω(n 4/3) lower bound on the randomized complexity of graph properties

We improve King's (n ^5/4) lower bound on the randomized decision tree complexity of monotone graph properties to (n ^4/3). The proof follows Yao's approach and improves it in a different direction from King's. At the heart of the proof are a duality argument combined with a new packing lemma for bipartite graphs.The paper was written while the author was a graduate student at the University of Chicago and was completed at M.I.T. The work was supported in part by NSF under GRANT number NSF 5-27561, the Air Force under Contract OSR-86-0076 and by DIMACS (Center for Discret Mathematics and Theoretical Computer Science), a National Science Foundation Science and Technology Center-NSF-STC88-09648.     

Bin packing and multiprocessor scheduling problems with side constraint on job types

This paper deals with the bin packing problem and the multiprocessor scheduling problem both with an additional constraint specifying the maximum number of jobs in each type to the processed on a processor. Since these problems are NP-complete, various approximation algorithms are proposed by generalizing those algorithms known for the ordinary bin packing and multiprocessor scheduling problems. The worst-case performance of the proposed algorithms are analyzed, and some computational results are reported to indicate their average case behavior.     

Regeneration for chains with infinite memory

Summary A regeneration structure is established for chains with infinite memory. The memory is required to decay only along a single recurrent path. When there are many recurrent paths (e.g. under conservativity) the construction yields a decomposition into regenerative recurrent classes.Research supported by NSF Grant DMS 89-01464     

THE FFD ALGORITHM FOR THE BIN PACKING PROBLEM WITH KERNEL ITEMS

The FFD algorithm is one of the most famous algorithms for the classical bin packing problem. In this paper,some versions of the FFD algorithm are considered in several bin packing problems. Especially,two of them applied to the bin packing problem with kernel items are analyzed. Tight worst-case performance ratios are obtained.     

Eigenvalues and expanders

Linear expanders have numerous applications to theoretical computer science. Here we show that a regular bipartite graph is an expanderif and only if the second largest eigenvalue of its adjacency matrix is well separated from the first. This result, which has an analytic analogue for Riemannian manifolds enables one to generate expanders randomly and check efficiently their expanding properties. It also supplies an efficient algorithm for approximating the expanding properties of a graph. The exact determination of these properties is known to be coNP-complete. The research was supported by the Weizmann Fellowship for Scientific Research and by Air Force Contract OSR 82-0326.