Correction to "A linear-time algorithm for triangulating a simple polygon"

The bandwidth of a n-vertex graph G is the smallest integer b such that there exists a bijective function f : V(G) → {1,...,n}, called a layout of G, such that for every edge uv ∈ E(G), |f(u) − f(v)| ≤ b. In the Bandwidth problem we are given as input a graph G and integer b, and asked whether the bandwidth of G is at most b. We present two results concerning the parameterized complexity of the Bandwidth problem on trees.First we show that an algorithm for Bandwidth with running time f(b)n o(b) would violate the Exponential Time Hypothesis, even if the input graphs are restricted to be trees of pathwidth at most two. Our lower bound shows that the classical 2O(b) n b + 1 time algorithm by Saxe [SIAM Journal on Algebraic and Discrete Methods, 1980] is essentially optimal.Our second result is a polynomial time algorithm that given a tree T and integer b, either correctly concludes that the bandwidth of T is more than b or finds a layout of T of bandwidth at most b O(b). This is the first parameterized approximation algorithm for the bandwidth of trees.

This book presents a view of several important topics in the modern field of distributed algorithms. The book is intended to provide useful background and reference information for professional engineers dealing with distributed systems, and for entering researchers who wish to develop a sufficient background in distributed algorithms. In addition, the book is suitable for providing a textbook-style introduction to distributed algorithms for advanced undergraduates or early graduate students who are taking a relevant course or consider pursuing research in the field.The reference sources for this book incluude mainly research papers on the modern theory of Distributed Computing that routinely appear in conferences such as the ACM Symposium on Principles of Distributed Computing (PODC), or the International Symposium on DIStributed Computing (DISC). Fundamental milestones of this theory include also research papers from other major conferences of Theoretical Computer Science that are of wider interest, such as the ACM Symposium on Theory of Computing (STOC), or from leading theory journals, such as the Journal of the ACM or SIAM Journal on Computing.

We have a lot of sorting algorithms in practice in this modern informatica world. Most of them are operating in quadratic time, some have managed to get them down to logarithmic time combined with linear, but there is no sorting algorithm that is efficient enough to sort data in linear time alone. In this paper, we will try to devise some mathematical support to the failure of minimalizing the sorting algorithms in linear time by comparing three well-known approximations of ! with detailed mathematical proofs associated with it. Further, this paper deals with detailed mathematical analysis to support the fact the minimum time complexity that can be attained by a sorting algorithm is of order O( ) without taking into account any extrapolated system advancement or computer architecture modification. There are various sorting algorithms like Merge sort, Heap sort, Interpolation sort, etc. which attains this minimum possible time complexity (worst case). But if we consider some biased cases then better time complexity can be obtained. If the elements of the array are in arithmetic progression, then interpolation search takes lesser time and there are many such biased cases where a particular algorithm gives better output but this paper deals with the general case i.e., no conditions are imposed on the elements of the array.

A faster FPTAS for knapsack problem with cardinality constraint

The traditional algorithm of L. Stockmeyer (1983) for area minimization of slicing floorplans has time (and space) complexity O(n/sup 2/) in the worst case, or O(n log n) for balanced slicing. For more than a decade, it is considered the best possible. In this paper, we present a new algorithm of worst-case time (and space) complexity O(n log n), where n is the total number of realizations for the basic blocks, regardless whether the slicing is balanced or not. We also prove /spl Omega/(n log n) is the lower bound and the time complexity of any area minimization algorithm. Therefore, the new algorithm not only finds the optimal realization, but also has an optimal running time.

This issue of SICOMP contains six specially selected papers from STOC 2020, the Fifty-second Annual ACM Symposium on the Theory of Computing, which was held June 22--26, 2020, initially planned at Chicago, Illinois, but due to COVID-19 was an online conference in the end. The papers here were chosen to represent the range and quality of the STOC program. These papers have been revised and extended by their authors and subjected to the standard thorough reviewing process of SICOMP. The program committee for STOC 2020 consisted of an executive committee made up of Nima Anari, Boaz Barak, Sébastien Bubeck, Mark Bun, Arkadev Chattopadhyay, Chandra Chekuri, Julia Chuzhoy, Marek Cygan, Ilias Diakonikolas, Yevgeniy Dodis, Sebastian Forster, Ankit Garg, Nika Haghtalab, Prahladh Harsha, Justin Holmgren, Piotr Indyk, Rahul Jain, Sanjeev Khanna, Dakshita Khurana, Pravesh Kothari, Robert Krauthgamer, Marvin Künnemann, Tengyu Ma, Rafael Oliveira, Merav Parter, Sofya Raskhodnikova, Robert Robere, Dana Ron, Noga Ron-Zewi, Thatchaphol Saranurak, Balasubramanian Sivan, Christian Sohler, Madhur Tulsiani, Omri Weinstein, Christian Wulff-Nilsen, and Henry Yuen. The program chair was Julia Chuzhoy. Included in this issue are the following papers: ``Explicit Near-Ramanujan Graphs of Every Degree" by Sidhanth Mohanty, Ryan O'Donnell, and Pedro Paredes shows a deterministic poly$(n)$-time algorithm that outputs a $d$-regular graph on $\Theta(n)$ vertices that is $\epsilon$-near-Ramanujan. ``Reducing Path TSP to TSP" by Vera Traub, Jens Vygen, and Rico Zenklusen presents a black-box reduction from the path version of the traveling salesman problem (Path TSP) to the classical tour version (TSP). ``Nearly Optimal Static Las Vegas Succinct Dictionary" by Huacheng Yu obtains a randomized dictionary data structure using ${OPT}+{poly}\lg n+O(\lg^{(\ell)} U)$ bits of space with expected constant query time for the static dictionary problem. ``Separating the Communication Complexity of Truthful and Nontruthful Algorithms for Combinatorial Auctions" by Sepehr Assadi, Hrishikesh Khandeparkar, Raghuvansh Raj Saxena, and S. Matthew Weinberg provides the first separation in the approximation guarantee achievable by truthful and nontruthful combinatorial auctions with polynomial communication. ``Strong Average-Case Circuit Lower Bounds from Nontrivial Derandomization" by Lijie Chen and Hanlin Ren establish a connection between nondeterministic algorithms estimating the acceptance probability of a given circuit and average-case lower bounds for nondeterministic time classes. ``Improved Bounds for Perfect Sampling of $k$-Colorings in Graphs" by Siddharth Bhandari and Sayantan Chakraborty presents a randomized algorithm that takes as input an undirected $n$-vertex graph $G$ with maximum degree $\Delta$ and an integer $k>3\Delta$ and returns a random proper $k$-coloring of $G$. We thank the authors, the STOC 2020 program committee, the STOC 2020 external reviewers, and the SICOMP referees for all of their hard work. Arkadev Chattopadhyay, Marek Cygan, Noga Ron-Zewi, Christian Wulff-Nilsen - Guest editors

In this paper, we study the problem of efficiently finding gene clusters formalized by nested common intervals between two genomes represented either as permutations or as sequences. Considering permutations, we give several algorithms whose running time depends on the size of the actual output rather than the output in the worst case. Indeed, we first provide a straightforward O(n 3) time algorithm for finding all nested common intervals. We reduce this complexity by providing an O(n 2) time algorithm computing an irredundant output. Finally, we show, by providing a third algorithm, that finding only the maximal nested common intervals can be done in linear time. Considering sequences, we provide solutions (modifications of previously defined algorithms and a new algorithm) for different variants of the problem, depending on the treatment one wants to apply to duplicated genes.KeywordsGene ClusterLinear TimeTime AlgorithmIdentity PermutationConsecutive IntegerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present the first polynomial time algorithm that finds the shortest route in a simple polygon such that all points of the polygon are visible from the route. This route is called the shortest watchman route, and we do not assume any restrictions on the route or on the simple polygon. Our algorithm runs in worst case O(n 6 ) time, but it is adaptive, making it run faster on polygons with a simple structure.

In this chapter we study two related areas of theoretical computer science: computability theory and computational complexity. Each of these subjects take mathematical problems as objects of study. The aim is not to solve these problems, but rather to classify them by level of difficulty. Time complexity classifies a given problem according to the length of time required for a computer to solve the problem. The polynomial-time problems P and the nondeterministic polynomial-time problems NP are the two most prominent classes of time complexity. Some problems cannot be solved by the algorithmic process of a computer. We refer to problems as decidable or undecidable according to whether or not there exists an algorithm that solves the problem. Computability theory considers undecidable problems and the brink between the undecidable and the decidable. There are only countably many algorithms and uncountably many problems to solve. From this fact we deduce that most problems are not decidable. To proceed beyond this fact, we must state precisely what we mean by an “algorithm” and a “problem.” One of the aims of this chapter is to provide a formal definition for the notion of an algorithm. The types of problems we shall consider are represented by the following examples. • The even problem: Given an n ∈ ℕ, determine whether or not n is even. • The 10-clique problem: Given finite graph, determine whether or not there exists a subgraph that is isomorphic to the 10-clique. • The satisfiability problem for first-order logic: Given a sentence of first-order logic, determine whether or not it is satisfiable. The first problem is quite easy. To determine whether a given number is even, we simply check whether the last digit of the number is 0, 2, 4, 6 or 8. The second problem is harder. If the given graph is large and does contain a 10-clique as a subgraph, then we may have to check many subsets of the graph before we find it. Time complexity gives precise meaning to the ostensibly subjective idea of one problem being “harder” than another. The third problem is the most difficult of the three problems.

We investigate the fundamental problem of generating bipartite classical distributions or quantum states. By designing efficient communication protocols and proving their optimality, we establish a number of intriguing connections to fundamental measures in optimization, convex geometry, and information theory. 1. To generate a classical distribution P(x, y), we tightly characterize the minimum amount of quantum communication needed by the psd-rank of P (as a matrix), a measure recently proposed by Fiorini, Massar, Pokutta, Tiwary and de Wolf (Proceedings of the 44th A CM Symposium on Theory of Computing, pages 95–106, 2012) in studies of the minimum size of extended formulations of optimization problems such as TSP. This echos the previous characterization for the optimal classical communication cost by the nonnegative rank of P. The result is obtained via investigating the more general case of bipartite quantum state generation and designing an optimal protocol for it. 2. When an approximation of ε is allowed to generate a distribution (X, Y) ∼ P, we present a classical protocol of the communication cost O((C(X, Y) + 1)/ε), where C(X, Y) is common information, a well-studied measure in information theory introduced by Wyner (IEEE Transactions on Information Theory, 21(2):163–179, 1975). This also links nonnegative rank and common information, two seemingly unrelated quantities in different fields. 3. For approximately generating a quantum pure state |ψ〉, we completely characterize the minimum cost by a corresponding approximate rank, closing a possibly exponential gap left in Ambainis, Schulman, Ta-Shma, Vazirani and Wigderson (SIAM Journal on Computing, 32(6):1570–1585, 2003).

We consider the classic scheduling problem of minimizing the total weighted flow-time on a single machine (min-WPFT), when preemption is allowed. In this problem, we are given a set of $n$ jobs, each job having a release time $r_j$, a processing time $p_j$, and a weight $w_j$. The flow-time of a job is defined as the amount of time the job spends in the system before it completes; that is, $F_j = C_j - r_j$, where $C_j$ is the completion time of job. The objective is to minimize the total weighted flow-time of jobs. This NP-hard problem has been studied quite extensively for decades. In a recent breakthrough, Batra, Garg, and Kumar presented a {\em pseudo-polynomial} time algorithm that has an $O(1)$ approximation ratio. The design of a truly polynomial time algorithm, however, remained an open problem. In this paper, we show a transformation from pseudo-polynomial time algorithms to polynomial time algorithms in the context of min-WPFT. Our result combined with the result of Batra, Garg, and Kumar settles the long standing conjecture that there is a polynomial time algorithm with $O(1)$-approximation for min-WPFT.

The distributed $(\Delta + 1)$-coloring problem is one of the most fundamental and well-studied problems in distributed algorithms. Starting with the work of Cole and Vishkin in 1986, a long line of gradually improving algorithms has been published. The state-of-the-art running time, prior to our work, is $O(\Delta \log \Delta + \log^* n)$, due to Kuhn and Wattenhofer [Proceedings of the $25$th Annual ACM Symposium on Principles of Distributed Computing, Denver, CO, 2006, pp. 7--15]. Linial [Proceedings of the $28$th Annual IEEE Symposium on Foundation of Computer Science, Los Angeles, CA, 1987, pp. 331--335] proved a lower bound of $\frac{1}{2} \log^* n$ for the problem, and Szegedy and Vishwanathan [Proceedings of the 25th Annual ACM Symposium on Theory of Computing, San Diego, CA, 1993, pp. 201--207] provided a heuristic argument that shows that algorithms from a wide family of locally iterative algorithms are unlikely to achieve a running time smaller than $\Theta(\Delta \log \Delta)$. We present a deterministic $(\Delta + 1)$-coloring distributed algorithm with running time $O(\Delta) + \frac{1}{2} \log^* n$. We also present a trade-off between the running time and the number of colors, and devise an $O(\lambda\cdot\Delta)$-coloring algorithm, with running time $O(\Delta / \lambda + \log^* n)$, for any parameter $\lambda > 1$. Our algorithm breaks the heuristic barrier of Szegedy and Vishwanathan and achieves running time which is linear in the maximum degree $\Delta$. On the other hand, the conjecture of Szegedy and Vishwanathan may still be true, as our algorithm does not belong to the family of locally iterative algorithms. On the way to this result we study a generalization of the notion of graph coloring, which is called defective coloring [L. Cowen, R. Cowen, and D. Woodall, J. Graph Theory, 10 (1986), pp. 187--195]. In an $m$-defective $p$-coloring the vertices are colored with $p$ colors so that each vertex has up to $m$ neighbors with the same color. We show that an $m$-defective $p$-coloring with reasonably small $m$ and $p$ can be computed very efficiently in the distributed setting. We also develop a technique to employ multiple defective colorings of various subgraphs of the original graph $G$ for computing a $(\Delta+1)$-coloring of $G$. We believe that these techniques are of independent interest.

<abstract><p>We study the problem of non-preemptively scheduling jobs from two agents on an unbounded serial-batch machine. Agents $ A $ and $ B $ have $ n_A $ and $ n_B $ jobs. The machine can process any number of jobs sequentially as a batch, and the processing time of the batch is equal to the total processing time of the jobs in it. Each batch requires a setup time before it is processed. Compatibility means that the jobs from different agents can be processed in a common batch; Otherwise, the jobs from different agents are incompatible. Both the compatible and incompatible models are considered, under both the batch availability and item availability assumptions. Batch availability means that any job in a batch is not available until all the jobs in this batch are completed. Item availability means that a job in a batch becomes available immediately after it is completed processing. The completion time of a job is defined to be the moment when it is available. The goal is to minimize the makespan of agent $ A $ and the maximum lateness of agent $ B $ simultaneously. For the compatible model with batch availability, an $ O(n_A+n_B^2\log n_B) $-time algorithm is presented which improves the existing $ O(n_A+n_B^4\log n_B) $-time algorithm. A slight modification of the algorithm solves the incompatible model with batch availability in $ O(n_A+n_B^2\log n_B) $ time, which has the same time complexity as the existing algorithm. For the compatible model with item availability, the analysis shows that it is easy and admits an $ O(n_A+n_B\log n_B) $-time algorithm. For the incompatible model with item availability, an $ O(n_A+n_B\log n_B) $-time algorithm is also obtained which improves the existing $ O(n_A+n_B^2) $-time algorithm. The algorithms can generate all Pareto optimal points and find a corresponding Pareto optimal schedule for each Pareto optimal point.</p></abstract>

We present a reformulation of the 2n+o(n) lower bound of Bent and John [Proceedings of the 17th Annual ACM Symposium on Theory of Computing, 1985, pp. 213--216] for the number of comparisons needed for selecting the median of n elements. Our reformulation uses a weight function. Apart from giving a more intuitive proof for the lower bound, the new formulation opens up possibilities for improving it. We use the new formulation to show that any pair-forming median finding algorithm, i.e., a median finding algorithm that starts by comparing $\lfloor n/2\rfloor$ disjoint pairs of elements must perform, in the worst case, at least 2.01 n + o(n) comparisons. This provides strong evidence that selecting the median requires at least cn+o(n) comparisons for some c> 2.

Robust sunflowers are a generalization of combinatorial sunflowers that have applications in monotone circuit complexity Rossman (SIAM J. Comput. 43:256–279, 2014), DNF sparsification Gopalan et al. (Comput. Complex. 22:275–310 2013), randomness extractors Li et al. (In: APPROX-RANDOM, LIPIcs 116:51:1–13, 2018), and recent advances on the Erdős-Rado sunflower conjecture Alweiss et al. (In: Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, STOC. Association for Computing Machinery, New York, NY, USA, 2020) Lovett et al. (From dnf compression to sunflower theorems via regularity, 2019) Rao (Discrete Anal. 8,2020). The recent breakthrough of Alweiss, Lovett, Wu and Zhang Alweiss et al. (In: Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, STOC. Association for Computing Machinery, New York, NY, USA, 2020) gives an improved bound on the maximum size of a w-set system that excludes a robust sunflower. In this paper, we use this result to obtain an exp (n^{1/2-o(1)}) lower bound on the monotone circuit size of an explicit n-variate monotone function, improving the previous best known exp (n^{1/3-o(1)}) due to Andreev (Algebra and Logic, 26:1–18, 1987) and Harnik and Raz (In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, ACM, New York, 2000). We also show an exp (varOmega (n)) lower bound on the monotone arithmetic circuit size of a related polynomial via a very simple proof. Finally, we introduce a notion of robust clique-sunflowers and use this to prove an n^{varOmega (k)} lower bound on the monotone circuit size of the CLIQUE function for all k leqslant n^{1/3-o(1)}, strengthening the bound of Alon and Boppana (Combinatorica, 7:1–22, 1987).