Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.     

On the Competitive Ratio for Online Facility Location

We consider the problem of Online Facility Location, where the demand points arrive online and must be assigned irrevocably to an open facility upon arrival. The objective is to minimize the sum of facility and assignment costs. We prove that the competitive ratio for Online Facility Location is Θ . On the negative side, we show that no randomized algorithm can achieve a competitive ratio better than Ω against an oblivious adversary even if the demands lie on a line segment. On the positive side, we present a deterministic algorithm which achieves a competitive ratio of in every metric space. A preliminary version of this work appeared in the Proceedings of the 30th International Colloquium on Automata, Languages and Programming (ICALP 2003), Lecture Notes in Computer Science 2719. This work was done while the author was at the Max-Planck-Institut für Informatik, Saarbrücken, Germany, and was partially supported by the Future and Emerging Technologies programme of the EU under contract number IST-1999-14186 (ALCOM–FT).     

The Network as a Storage Device: Dynamic Routing with Bounded Buffers

We study dynamic routing in store-and-forward packet networks where each network link has bounded buffer capacity for receiving incoming packets and is capable of transmitting a fixed number of packets per unit of time. At any moment in time, packets are injected at various network nodes with each packet specifying its destination node. The goal is to maximize the throughput, defined as the number of packets delivered to their destinations. In this paper, we make some progress on throughput maximization in various network topologies. Let n and m denote the number of nodes and links in the network, respectively. For line networks, we show that Nearest-to-Go (NTG), a natural distributed greedy algorithm, is -competitive, essentially matching a known lower bound on the performance of any greedy algorithm. We also show that if we allow the online routing algorithm to make centralized decisions, there is a randomized polylog(n)-competitive algorithm for line networks as well as for rooted tree networks, where each packet is destined for the root of the tree. For grid graphs, we show that NTG has a competitive ratio of while no greedy algorithm can achieve a ratio better than . Finally, for arbitrary network topologies, we show that NTG is -competitive, improving upon an earlier bound of O(mn). An extended abstract appeared in the Proceedings of the 8th Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2005, Berkeley, CA, USA, pp. 1–13, Lecture Notes in Computer Science, vol. 1741, Springer, Berlin. S. Angelov is supported in part by NSF Career Award CCR-0093117, NSF Award ITR 0205456 and NIGMS Award 1-P20-GM-6912-1. S. Khanna is supported in part by an NSF Career Award CCR-0093117, NSF Award CCF-0429836, and a US-Israel Binational Science Foundation Grant. K. Kunal is supported in part by an NSF Career Award CCR-0093117 and NSF Award CCF-0429836.     

Competitive Analysis of Scheduling Algorithms for Aggregated Links

We study an online job scheduling problem arising in networks with aggregated links. The goal is to schedule n jobs, divided into k disjoint chains, on m identical machines, without preemption, so that the jobs within each chain complete in the order of release times and the maximum flow time is minimized. We present a deterministic online algorithm with competitive ratio , and show a matching lower bound, even for randomized algorithms. The performance bound for we derive in the paper is, in fact, more subtle than a standard competitive ratio bound, and it shows that in overload conditions (when many jobs are released in a short amount of time), ’s performance is close to the optimum. We also show how to compute an offline solution efficiently for k=1, and that minimizing the maximum flow time for k,m≥2 is -hard. As by-products of our method, we obtain two offline polynomial-time algorithms for minimizing makespan: an optimal algorithm for k=1, and a 2-approximation algorithm for any k. W. Jawor and M. Chrobak supported by NSF grants OISE-0340752 and CCR-0208856. Work of C. Dürr conducted while being affiliated with the Laboratoire de Recherche en Informatique, Université Paris-Sud, 91405 Orsay. Supported by the CNRS/NSF grant 17171 and ANR Alpage.     

Faster Deterministic Communication in Radio Networks

We study the communication primitives of broadcasting (one-to-all communication) and gossiping (all-to-all communication) in known topology radio networks, i.e., where for each primitive the schedule of transmissions is precomputed based on full knowledge about the size and the topology of the network. We show that gossiping can be completed in time units in any radio network of size n, diameter D, and maximum degree Δ=Ω(log n). This is an almost optimal schedule in the sense that there exists a radio network topology, specifically a Δ-regular tree, in which the radio gossiping cannot be completed in less than units of time. Moreover, we show a schedule for the broadcast task. Both our transmission schemes significantly improve upon the currently best known schedules by Gąsieniec, Peleg, and Xin (Proceedings of the 24th Annual ACM SIGACT-SIGOPS PODC, pp. 129–137, 2005), i.e., a O(D+Δlog n) time schedule for gossiping and a D+O(log ³ n) time schedule for broadcast. Our broadcasting schedule also improves, for large D, a very recent O(D+log ² n) time broadcasting schedule by Kowalski and Pelc. A preliminary version of this paper appeared in the proceedings of ISAAC’06. F. Cicalese supported by the Sofja Kovalevskaja Award 2004 of the Alexander von Humboldt Stiftung. F. Manne and Q. Xin supported by the Research Council of Norway through the SPECTRUM project.     

Algorithms for Modular Counting of Roots of Multivariate Polynomials

Given a multivariate polynomial P(X ₁,…,X _n ) over a finite field , let N(P) denote the number of roots over . The modular root counting problem is given a modulus r, to determine N _r (P)=N(P)mod r. We study the complexity of computing N _r (P), when the polynomial is given as a sum of monomials. We give an efficient algorithm to compute N _r (P) when the modulus r is a power of the characteristic of the field. We show that for all other moduli, the problem of computing N _r (P) is -hard. We present some hardness results which imply that our algorithm is essentially optimal for prime fields. We show an equivalence between maximum-likelihood decoding for Reed-Solomon codes and a root-finding problem for symmetric polynomials. P. Gopalan’s and R.J Lipton’s research was supported by NSF grant CCR-3606B64. V. Guruswami’s research was supported in part by NSF grant CCF-0343672 and a Sloan Research Fellowship.     

Improved Approximate String Matching Using Compressed Suffix Data Structures

Approximate string matching is about finding a given string pattern in a text by allowing some degree of errors. In this paper we present a space efficient data structure to solve the 1-mismatch and 1-difference problems. Given a text T of length n over an alphabet A, we can preprocess T and give an -bit space data structure so that, for any query pattern P of length m, we can find all 1-mismatch (or 1-difference) occurrences of P in O(|A|mlog log n+occ) time, where occ is the number of occurrences. This is the fastest known query time given that the space of the data structure is o(nlog ² n) bits. The space of our data structure can be further reduced to O(nlog |A|) with the query time increasing by a factor of log  ^ε n, for 0<ε≤1. Furthermore, our solution can be generalized to solve the k-mismatch (and the k-difference) problem in O(|A| ^k m ^k (k+log log n)+occ) and O(log  ^ε n(|A| ^k m ^k (k+log log n)+occ)) time using an -bit and an O(nlog |A|)-bit indexing data structures, respectively. We assume that the alphabet size |A| is bounded by for the -bit space data structure.     

An $\mathsf{EDF}$ -based restricted-migration scheduling algorithm for multiprocessor soft real-time systems

There has been much recent interest in the use of the earliest-deadline-first ( ) algorithm for scheduling soft real-time sporadic task systems on identical multiprocessors. In hard real-time systems, a significant disparity exists between -based schemes and Pfair scheduling: on M processors, the worst-case schedulable utilization for all known variants is approximately M/2, whereas it is M for optimal Pfair algorithms. This is unfortunate because -based algorithms entail lower scheduling and task-migration overheads. However, such a disparity in schedulability can be alleviated by easing the requirement that all deadlines be met, which may be sufficient for soft real-time systems. In particular, in recent work, we have shown that if task migrations are not restricted, then (i.e. , global ) can ensure bounded tardiness for a sporadic task system with no restrictions on total utilization. Unrestricted task migrations in global may be unappealing for some systems, but if migrations are forbidden entirely, then bounded tardiness cannot be guaranteed. In this paper, we address the issue of striking a balance between task migrations and system utilization by proposing an algorithm called , which is based upon and treads a middle path, by restricting, but not eliminating, task migrations. Specifically, under , the ability to migrate is required for at most M−1 tasks, and it is sufficient that every such task migrate between two processors and at job boundaries only. , like global , can ensure bounded tardiness to a sporadic task system as long as the available processing capacity is not exceeded, but, unlike global , may require that per-task utilizations be capped. The required cap is quite liberal, hence, should enable a wide range of soft real-time applications to be scheduled with no constraints on total utilization.

A ( $2-c\frac{1}{\sqrt{N}}$ )-Approximation Algorithm for the Stable Marriage Problem

We consider the problem of finding a stable matching of maximum size when both ties and unacceptable partners are allowed in preference lists. This problem is NP-hard and the current best known approximation algorithm achieves the approximation ratio 2−c(log N)/N, where c is an arbitrary positive constant and N is the number of men in an input. In this paper, we improve the ratio to , where c is an arbitrary constant that satisfies . A preliminary version of this paper was presented at the 16th Annual International Symposium on Algorithms and Computation, ISAAC 2005.     

Lower bound for scalable Byzantine Agreement

We consider the problem of computing Byzantine Agreement in a synchronous network with n processors, each with a private random string, where each pair of processors is connected by a private communication line. The adversary is malicious and non-adaptive, i.e., it must choose the processors to corrupt at the start of the algorithm. Byzantine Agreement is known to be computable in this model in an expected constant number of rounds. We consider a scalable model where in each round each uncorrupt processor can send to any set of log n other processors and listen to any set of log n processors. We define the loss of an execution to be the number of uncorrupt processors whose output does not agree with the output of the majority of uncorrupt processors. We show that if there are t corrupt processors, then any randomised protocol which has probability at least 1/2 + 1/ logn of loss less than requires at least f rounds. This also shows that lossless protocols require both rounds, and for at least one uncorrupt processor to send messages during the protocol.     

Communication-Aware Processor Allocation for Supercomputers: Finding Point Sets of Small Average Distance

We give processor-allocation algorithms for grid architectures, where the objective is to select processors from a set of available processors to minimize the average number of communication hops. The associated clustering problem is as follows: Given n points in ℜ ^d , find a size-k subset with minimum average pairwise L ₁ distance. We present a natural approximation algorithm and show that it is a -approximation for two-dimensional grids; in d dimensions, the approximation guarantee is , which is tight. We also give a polynomial-time approximation scheme (PTAS) for constant dimension d, and we report on experimental results.     

Subquadratic Algorithms for 3SUM

We obtain subquadratic algorithms for 3SUM on integers and rationals in several models. On a standard word RAM with w-bit words, we obtain a running time of . In the circuit RAM with one nonstandard AC ⁰ operation, we obtain . In external memory, we achieve O(n ²/(MB)), even under the standard assumption of data indivisibility. Cache-obliviously, we obtain a running time of . In all cases, our speedup is almost quadratic in the “parallelism” the model can afford, which may be the best possible. Our algorithms are Las Vegas randomized; time bounds hold in expectation, and in most cases, with high probability.     

On Enumerating Minimal Dicuts and Strongly Connected Subgraphs

We consider the problems of enumerating all minimal strongly connected subgraphs and all minimal dicuts of a given strongly connected directed graph G=(V,E). We show that the first of these problems can be solved in incremental polynomial time, while the second problem is NP-hard: given a collection of minimal dicuts for G, it is NP-hard to tell whether it can be extended. The latter result implies, in particular, that for a given set of points , it is NP-hard to generate all maximal subsets of contained in a closed half-space through the origin. We also discuss the enumeration of all minimal subsets of whose convex hull contains the origin as an interior point, and show that this problem includes as a special case the well-known hypergraph transversal problem. This research was supported by the National Science Foundation (Grant IIS-0118635). The third and fourth authors are also grateful for the partial support by DIMACS, the National Science Foundation’s Center for Discrete Mathematics and Theoretical Computer Science. Our friend and co-author, Leonid Khachiyan tragically passed away on April 29, 2005.     

On the Complexity of 2-Monotone Restarting Automata

The -automaton is the weakest form of the nondeterministic version of the restarting automaton that was introduced by Jančar et al. to model the so-called analysis by reduction. Here it is shown that the class ℒ(R) of languages that are accepted by -automata is incomparable under set inclusion to the class of Church-Rosser languages and to the class of growing context-sensitive languages. In fact this already holds for the class of languages that are accepted by 2-monotone -automata. In addition, we prove that already the latter class contains -complete languages, showing that already the 2-monotone -automaton has a surprisingly large expressive power. The results of this paper have been announced at DLT 2004 in Auckland, New Zealand. This work was mainly carried out while T. Jurdziński was visiting the University of Kassel, supported by a grant from the Deutsche Forschungsgemeinschaft (DFG). F. Mráz and M. Plátek were partially supported by the Grant Agency of the Czech Republic under Grant-No. 201/04/2102 and by the program ‘Information Society’ under project 1ET100300517. F. Mráz was also supported by the Grant Agency of Charles University in Prague under Grant-No. 358/2006/A-INF/MFF.     

Approximation Algorithms for Multi-Criteria Traveling Salesman Problems

We analyze approximation algorithms for several variants of the traveling salesman problem with multiple objective functions. First, we consider the symmetric TSP (STSP) with γ-triangle inequality. For this problem, we present a deterministic polynomial-time algorithm that achieves an approximation ratio of and a randomized approximation algorithm that achieves a ratio of . In particular, we obtain a 2+ε approximation for multi-criteria metric STSP. Then we show that multi-criteria cycle cover problems admit fully polynomial-time randomized approximation schemes. Based on these schemes, we present randomized approximation algorithms for STSP with γ-triangle inequality (ratio ), asymmetric TSP (ATSP) with γ-triangle inequality (ratio ), STSP with weights one and two (ratio 4/3) and ATSP with weights one and two (ratio 3/2). A preliminary version of this work has been presented at the 4th Workshop on Approximation and Online Algorithms (WAOA 2006) (Lecture Notes in Computer Science, vol. 4368, pp. 302–315, 2007). B. Manthey is supported by the Postdoc-Program of the German Academic Exchange Service (DAAD). He is on leave from Saarland University and has done part of the work at the Institute for Theoretical Computer Science of the University of Lübeck supported by DFG research grant RE 672/3 and at the Department of Computer Science at Saarland University.     

Path Hitting in Acyclic Graphs

An instance of the path hitting problem consists of two families of paths, and ℋ, in a common undirected graph, where each path in ℋ is associated with a non-negative cost. We refer to and ℋ as the sets of demand and hitting paths, respectively. When p∈ℋ and share at least one mutual edge, we say that p hits q. The objective is to find a minimum cost subset of ℋ whose members collectively hit those of . In this paper we provide constant factor approximation algorithms for path hitting, confined to instances in which the underlying graph is a tree, a spider, or a star. Although such restricted settings may appear to be very simple, we demonstrate that they still capture some of the most basic covering problems in graphs. Our approach combines several novel ideas: We extend the algorithm of Garg, Vazirani and Yannakakis (Algorithmica, 18:3–20, 1997) for approximate multicuts and multicommodity flows in trees to prove new integrality properties; we present a reduction that involves multiple calls to this extended algorithm; and we introduce a polynomial-time solvable variant of the edge cover problem, which may be of independent interest. An extended abstract of this paper appeared in Proceedings of the 14th Annual European Symposium on Algorithms, 2006. This work is part of D. Segev’s Ph.D. thesis prepared at Tel-Aviv University under the supervision of Prof. Refael Hassin.     

Translational lemmas for DLOGTIME-uniform circuits,alternating TMs,and PRAMs

We present translational lemmas for the three standard models of parallel computation, and apply them to obtain tight hierarchy results. It is shown that, for arbitrarily small rational constant , (i) there is a language which can be accepted by a -uniform circuit family of depth and size but not by any -uniform circuit family of depth and size , (ii) there is a language which can be accepted by a -time -space ATM with l worktapes but not by any -time -space ATM with the same l worktapes if the number of tape symbols is fixed, and (iii) there is a language which can be accepted by a -time PRAM with processors but not by any -time PRAM with processors. Here, c > 0, d ≥ 1, r ₁ > 1, and r ₂ ≥ 1 are arbitrary rational constants, and l ≥ 2 is an arbitrary integer. Preliminary versions of different parts of this paper appeared in Proc. MCU 2004 (LNCS 3354) and Proc. FCT 2005 (LNCS 3623).     

Speeding Up HMM Decoding and Training by Exploiting Sequence Repetitions

We present a method to speed up the dynamic program algorithms used for solving the HMM decoding and training problems for discrete time-independent HMMs. We discuss the application of our method to Viterbi’s decoding and training algorithms (IEEE Trans. Inform. Theory IT-13:260–269, 1967), as well as to the forward-backward and Baum-Welch (Inequalities 3:1–8, 1972) algorithms. Our approach is based on identifying repeated substrings in the observed input sequence. Initially, we show how to exploit repetitions of all sufficiently small substrings (this is similar to the Four Russians method). Then, we describe four algorithms based alternatively on run length encoding (RLE), Lempel-Ziv (LZ78) parsing, grammar-based compression (SLP), and byte pair encoding (BPE). Compared to Viterbi’s algorithm, we achieve speedups of Θ(log n) using the Four Russians method, using RLE, using LZ78, using SLP, and Ω(r) using BPE, where k is the number of hidden states, n is the length of the observed sequence and r is its compression ratio (under each compression scheme). Our experimental results demonstrate that our new algorithms are indeed faster in practice. We also discuss a parallel implementation of our algorithms. A preliminary version of this paper appeared in Proc. 18th Annual Symposium on Combinatorial Pattern Matching (CPM), pp. 4–15, 2007. Y. Lifshits’ research was supported by the Center for the Mathematics of Information and the Lee Center for Advanced Networking. S. Mozes’ work conducted while visiting MIT.     

Improving the lazy Krivine machine

Krivine presents the  machine, which produces weak head normal form results. Sestoft introduces several call-by-need variants of the  machine that implement result sharing via pushing update markers on the stack in a way similar to the TIM and the STG machine. When a sequence of consecutive markers appears on the stack, all but the first cause redundant updates. Improvements related to these sequences have dealt with either the consumption of the markers or the removal of the markers once they appear. Here we present an improvement that eliminates the production of marker sequences of length greater than one. This improvement results in the  machine, a more space and time efficient variant of . We then apply the classic optimization of short-circuiting operand variable dereferences to create the call-by-need  machine. Finally, we combine the two improvements in the  machine. On our benchmarks this machine uses half the stack space, performs one quarter as many updates, and executes between 27% faster and 17% slower than our ℒ variant of Sestoft’s lazy Krivine machine. More interesting is that on one benchmark ℒ, , and consume unbounded space, but consumes constant space. Our comparisons to Sestoft’s Mark 2 machine are not exact, however, since we restrict ourselves to unpreprocessed closed lambda terms. Our variant of his machine does no environment trimming, conversion to deBruijn-style variable access, and does not provide basic constants, data type constructors, or the recursive let. (The Y combinator is used instead.)