We study the fully dynamic pattern matching problem where the pattern may contain up to k wildcard symbols, each matching any symbol of the alphabet. Both the text and the pattern are subject to updates (insert, delete, change). We design an algorithm with 𝒪(n log² n) preprocessing and update/query time 𝒪̃(kn^{k/{k+1}} + k² log n). The bound is truly sublinear for a constant k, and sublinear when k = o(log n). We further complement our results with a conditional lower bound: assuming subquadratic preprocessing time, achieving truly sublinear update time for the case k = Ω(log n) would contradict the Strong Exponential Time Hypothesis (SETH). Finally, we develop sublinear algorithms for two special cases: - If the pattern contains w non-wildcard symbols, we give an algorithm with preprocessing time 𝒪(nw) and update time 𝒪(w + log n), which is truly sublinear whenever w is truly sublinear. - Using FFT technique combined with block decomposition, we design a deterministic truly sublinear algorithm with preprocessing time 𝒪(n^{1.8}) and update time 𝒪(n^{0.8} log n) for the case that there are at most two non-wildcards.

We study the problem of fair allocation of indivisible goods for subadditive agents. While constant-MMS bounds have been given for additive and fractionally subadditive agents, the best existential bound for the case of subadditive agents is 1/O(log n log log n). In this work, we improve this bound to a 1/O((log log n)2)-MMS guarantee. To this end, we introduce new matching techniques and rounding methods for subadditive valuations that we believe are of independent interest and will find their applications in future work.

Abstract. Longest common subsequence ( LCS ) is a classic and central problem in combinatorial optimization. While LCS admits a quadratic time solution, recent evidence suggests that solving the problem may be impossible in truly subquadratic time. A special case of LCS wherein each character appears at most once in every string is equivalent to the longest increasing subsequence (LIS) problem which can be solved in quasilinear time. In this work, we present novel algorithms for approximating LCS in truly subquadratic time and LIS in truly sublinear time. Our approximation factors depend on the ratio of the optimal solution size to the input size. We denote this ratio by [Formula: see text] and obtain the following results for LCS and LIS without any prior knowledge of [Formula: see text]: a truly subquadratic time algorithm for LCS with approximation factor [Formula: see text] and a truly sublinear time algorithm for LIS with approximation factor [Formula: see text]. The triangle inequality was recently used by M. Boroujeni, S. Ehsani, M. Ghodsi, M. HajiAghayi, and S. Seddingham [ Proceedings of the 29 th Annual ACM-SIAM Symposium on Discrete Algorithms, 2018, pp. 1170–1189] and D. Chakraborty, D. Das, E. Goldenberg, M. Koucky, and M. Saks [ Proceedings of the 59 th Annual IEEE Symposium on Foundations of Computer Science, 2018, pp. 979–990] to present new approximation algorithms for edit distance. Our techniques for LCS extend the notion of the triangle inequality to nonmetric settings.

We consider the classic 1-center problem: Given a set $P$ of $n$ points in a metric space find the point in $P$ that minimizes the maximum distance to the other points of $P$. We study the complexity of this problem in $d$-dimensional $\ell_p$-metrics and in edit and Ulam metrics over strings of length $d$. Our results for the 1-center problem may be classified based on $d$ as follows. $\bullet$ Small $d$: Assuming the hitting set conjecture (HSC), we show that when $d=ω(\log n)$, no subquadratic algorithm can solve 1-center problem in any of the $\ell_p$-metrics, or in edit or Ulam metrics. $\bullet$ Large $d$: When $d=Ω(n)$, we extend our conditional lower bound to rule out subquartic algorithms for 1-center problem in edit metric (assuming Quantified SETH). On the other hand, we give a $(1+ε)$-approximation for 1-center in Ulam metric with running time $\tilde{O_{\varepsilon}}(nd+n^2\sqrt{d})$. We also strengthen some of the above lower bounds by allowing approximations or by reducing the dimension $d$, but only against a weaker class of algorithms which list all requisite solutions. Moreover, we extend one of our hardness results to rule out subquartic algorithms for the well-studied 1-median problem in the edit metric, where given a set of $n$ strings each of length $n$, the goal is to find a string in the set that minimizes the sum of the edit distances to the rest of the strings in the set.

A suffix tree is a trie-like data structure that stores every suffix of an input string of length n. Finding the Suffix Tree of a given string is a well-studied and classic problem. A compressed suffix tree is constructible in O(n) time using the well-known algorithm of McCreight (JACM, 1976). Suffix trees alongside with hashing are two powerful tools in solving location-sensitive string problems. Many well-studied fundamental string problems such as String Matching, Longest Palindrome Substring (LPS), Longest Common Substring (LCS), and Longest Common Prefix (LCP) queries are location-sensitive and have linear time solutions via reductions to suffix tree.

Abstract Longest common substring (), longest palindrome substring (), and Ulam distance () are three fundamental string problems that can be classically solved in near linear time. In this work, we present sublinear time quantum algorithms for these problems along with quantum lower bounds. Our results shed light on a very surprising fact: Although the classic solutions for and are almost identical (via suffix trees), their quantum computational complexities are different. While we give an exact $${{\tilde{O}}}(\sqrt{n})$$ O~(n) time algorithm for , we prove that needs at least time $$\tilde{\Omega }(n^{2/3})$$ Ω~(n2/3) even for 0/1 strings.

In this work, we study the maximin share fairness notion for allocation of indivisible goods in the subadditive and fractionally subadditive settings. While previous work refutes the possibility of obtaining an allocation which is better than 1/2-MMS, the only positive result for the subadditive setting states that when the number of items is equal to m, there always exists an Ω(1/log m)-\MMS allocation. Since the number of items may be larger than the number of agents (n), such a bound can only imply a weak bound of Ω(1/(n log n))-MMS allocation in general. In this work, we improve this gap exponentially to an Ω(1/(log n log log n))-MMS guarantee. In addition to this, we prove that when the valuation functions are fractionally subadditive, a 1/4.6-MMS allocation is guaranteed to exist. This also improves upon the previous bound of 1/5-MMS guarantee for the fractionally subadditive setting.

Simulated annealing is an effective and general means of optimization. It is in fact inspired by metallurgy, where the temperature of a material determines its behavior in thermodynamics. Likewise, in simulated annealing, the actions that the algorithm takes depend entirely on the value of a variable which captures the notion of temperature. Typically, simulated annealing starts with a high temperature, which makes the algorithm pretty unpredictable, and gradually cools the temperature down to become more stable. A key component that plays a crucial role in the performance of simulated annealing is the criteria under which the temperature changes namely, the cooling schedule. Motivated by this, we study the following question in this work: "Given enough samples to the instances of a specific class of optimization problems, can we design optimal (or approximately optimal) cooling schedules that minimize the runtime or maximize the success rate of the algorithm on average when the underlying problem is drawn uniformly at random from the same class?" We provide positive results both in terms of sample complexity and simulation complexity. For sample complexity, we show that O (m^1/2) samples suffice to find an approximately optimal cooling schedule of length m. We complement this result by giving a lower bound of Ω (m^1/3) on the sample complexity of any learning algorithm that provides an almost optimal cooling schedule. These results are general and rely on no assumption. For simulation complexity, however, we make additional assumptions to measure the success rate of an algorithm. To this end, we introduce the monotone stationary graph that models the performance of simulated annealing. Based on this model, we present polynomial time algorithms with provable guarantees for the learning problem.

The edit distance (ED) and longest common subsequence (LCS) are two fundamental problems which quantify how similar two strings are to one another. In this paper, we first consider these problems in the asymmetric streaming model introduced by Andoni, Krauthgamer and Onak [Andoni et al., 2010] (FOCS'10) and Saks and Seshadhri [Saks and Seshadhri, 2013] (SODA'13). In this model we have random access to one string and streaming access the other one. Our main contribution is a constant factor approximation algorithm for ED with memory Õ(n^δ) for any constant δ > 0. In addition to this, we present an upper bound of Õ _ε(√n) on the memory needed to approximate ED or LCS within a factor 1±ε. All our algorithms are deterministic and run in polynomial time in a single pass. We further study small-space approximation algorithms for ED, LCS, and longest increasing sequence (LIS) in the non-streaming setting. Here, we design algorithms that achieve 1 ± ε approximation for all three problems, where ε > 0 can be any constant and even slightly sub-constant. Our algorithms only use poly-logarithmic space while maintaining a polynomial running time. This significantly improves previous results in terms of space complexity, where all known results need to use space at least Ω(√n). Our algorithms make novel use of triangle inequality and carefully designed recursions to save space, which can be of independent interest.

We present a novel sublinear time algorithm for approximating LIS. If we denote the ratio of the solution size over the input size by λ, our approach yields an algorithm with an approximation factor of Ω(λ∊) for any constant ∊ > 0, and a truly sublinear runtime. This improves over for example the recent work of Rubinstein et al. [RSSS19] that approximates LIS within a factor Ω(λ3) in truly sublinear time. Our work makes use of a grid packing technique recently introduced by Mitzenmacher and Seddighin to approximate LIS in the dynamic setting [MS20], providing another application for this technique.

We study distributed algorithms for string matching problem in presence of wildcard characters. Given a string T (a text), we look for all occurrences of another string P (a pattern) as a substring of string T. Each wildcard character in the pattern matches a specific class of strings based on its type. String matching is one of the most fundamental problems in computer science, especially in the fields of bioinformatics and machine learning. Persistent effort has led to a variety of algorithms for the problem since 1960s.

We study dynamic algorithms for the longest increasing subsequence (LIS) problem. A dynamic LIS algorithm maintains a sequence subject to operations of the following form arriving one by one: insert an element, delete an element, or substitute an element for another. After each update, the algorithm must report the length of the longest increasing subsequence of the current sequence.

Edit distance is one of the most fundamental problems in combinatorial optimization to measure the similarity between strings. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an almost optimal MPC (massively parallel computation) algorithm for Ulam distance and improve MPC algorithms for edit distance. Our algorithm for Ulam distance is almost optimal in the sense that (1) the approximation factor of our algorithm is 1+ε1+ε, (2) the round complexity of our algorithm is constant, (3) the total memory of our algorithm is almost linear (~Oε(n)Õε(n)), and (4) the overall running time of our algorithm is almost linear which is the best known for Ulam distance. We also improve the work of Hajiaghayi et al. for edit distance in terms of total memory. The best previously known MPC algorithm for edit distance requires ~O(n2x)Õ(n2x) machines when the memory of each machine is bounded by ~O(n1-x)Õ(n1-x). In this work, we improve the number of machines to ~O(n(9/5)x)Õ(n(9/5)x) while keeping the memory limit intact. Moreover, the round complexity of our algorithm is constant and the total running time of our algorithm is truly subquadratic. However, our improvement comes at the expense of a constant factor in the approximation guarantee of the algorithm. This improvement is inspired by the recent techniques of Boroujeni et al. and Chakraborty et al. for obtaining truly subquadratic time algorithms for edit distance.

Edit distance and longest common subsequence are among the most fundamental problems in combinatorial optimization. Recent developments have proven strong lower bounds against subquadratic time solutions for both problems. Moreover, the best approximation factors for subquadratic time solutions have been limited to 3 for edit distance and super constant for longest common subsequence. Improved approximation algorithms for these problems1 are some of the biggest open questions in combinatorial optimization. In this work, we present improved algorithms for both edit distance and longest common subsequence. The running times are truly subquadratic, though we obtain 1 + o(1) approximate solutions for both problems if the input satisfies a mild condition. In this setting, first, an adversary chooses one of the input strings. Next, this string is perturbed by a random procedure, and then the adversary chooses the second string after observing the perturbed one.

In this paper, we provide new approximation algorithms for dynamic variations of the longest increasing subsequence (LIS) problem, and the complementary distance to monotonicity (DTM) problem. In this setting, operations of the following form arrive sequentially: (i) add an element, (ii) remove an element, or (iii) substitute an element for another. At every point in time, the algorithm has an approximation to the longest increasing subsequence (or distance to monotonicity). We present a (1+є)-approximation algorithm for DTM with polylogarithmic worst-case update time and a constant factor approximation algorithm for LIS with worst-case update time Õ(n є) for any constant є > 0.

Longest common subsequence (LCS) is a classic and central problem in combinatorial optimization. While LCS admits a quadratic time solution, recent evidence suggests that solving the problem may be impossible in truly subquadratic time. A special case of LCS wherein each character appears at most once in every string is equivalent to the longest increasing subsequence problem (LIS) which can be solved in quasilinear time. In this work, we present novel algorithms for approximating LCS in truly subquadratic time and LIS in truly sublinear time. Our approximation factors depend on the ratio of the optimal solution size over the input size. We denote this ratio by λ and obtain the following results for LCS and LIS without any prior knowledge of λ. • A truly subquadratic time algorithm for LCS with approximation factor O(λ^3). • A truly sublinear time algorithm for LIS with approximation factor O(λ^3). Triangle inequality was recently used by Boroujeni et al. [1] and Chakraborty et al.[2] to present new approximation algorithms for edit distance. Our techniques for LCS extend the notion of triangle inequality to non-metric settings.

String similarity measures are among the most fundamental problems in computer science. The notable examples are edit distance (ED) and longest common subsequence (LCS). These problems find their applications in various contexts such as computational biology, text processing, compiler optimization, data analysis, image analysis, etc. In this work, we revisit edit distance and longest common subsequence in the parallel settings. We present massively parallel algorithms for both problems that are optimal in the following senses: The approximation factor of our algorithms is 1 + ∊. The round complexity of our algorithms is constant. The total running time of our algorithms over all machines is Õ(n2). This matches the running time of the best-known solutions for approximating edit distance and longest common subsequence within a 1 + ∊ factor in the sequential setting. Our result for edit distance substantially improves the massively parallel algorithm of [15] in terms of approximation factor, round complexity, number of machines, and total running time. Our unified approach to tackle both problems is to divide one of the strings into smaller blocks and try to locally predict which intervals of the other string correspond to each block in an optimal solution. Our main technical contribution is a novel parallel algorithm for computing a set of compositions, and recursively decomposing each function into a set of smaller iterative compositions (in terms of memory needed to solve the problem). These two methods together give us a strong tool for approximating combinatorial problems. For instance, LCS can be formulated as a recursive composition of functions and therefore this tool enables us to approximate LCS within a factor 1 + ∊. Indeed, we recursively decompose the problem until we are able to compute the solution on a single machine. Since our methods are quite general, we expect this technique to find its applications in other combinatorial problems as well.

Longest common subsequence (LCS) is one of the most fundamental problems in combinatorial optimization. Apart from theoretical importance, LCS has enormous applications in bioinformatics, revision control systems, and data comparison programs1. Although a simple dynamic program computes LCS in quadratic time, it has been recently proven that the problem admits a conditional lower bound and may not be solved in truly subquadratic time [2]. In addition to this, LCS is notoriously hard with respect to approximation algorithms. Apart from a trivial sampling technique that obtains a nx approximation solution in time O(n2–2x) nothing else is known for LCS. This is in sharp contrast to its dual problem edit distance for which several linear time solutions are obtained in the past two decades [4, 5, 9, 10, 16]. In this work, we present the first nontrivial algorithm for approximating LCS in linear time. Our main result is a linear time algorithm for the longest common subsequence which has an approximation factor of O(n0.497956). This beats the barrier for approximating LCS in linear time.

Edit distance is one of the most fundamental problems in combinatorial optimization. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an almost optimal MPC algorithm for Ulam distance and improve MPC algorithms for edit distance. Our algorithm for Ulam distance is optimal in the sense that (1) the approximation factor of our algorithm is 1+ε, (2) the round complexity of our algorithm is constant, (3) the total memory of our algorithm is almost linear (~O(n)), and (4) the overall running time of our algorithm is almost linear which is the best known for Ulam distance. Similar to edit distance and longest common subsequence (LCS) which are considered as dual problems, Ulam distance and longest increasing subsequence (LIS) are also seen as dual problems. LIS is equivalent to a special case of LCS where each string can contain each character at most once. In that sense, our result for Ulam distance complements the work of Im et al., wherein a similar result is presented for łis. We also improve the work of Hajiaghayi et al. for edit distance in terms of total memory. The best previously known MPC algorithm for edit distance requires ~O(n2x) machines when the memory of each machine is bounded by ~O(n1-x). In this work, we improve the number of machines to ~O(n1.75x) while keeping the memory limit intact. Moreover, the round complexity of our algorithm is constant and the total running time of our algorithm is truly subquadratic. However, our improvement comes at the expense of a constant factor in the approximation guarantee of the algorithm. This improvement is inspired by the recent techniques of Boroujeni et al. and Chakraborty et al. for obtaining truly subquadratic time algorithms for edit distance.

Edit distance is one of the most fundamental problems in computer science. Tree edit distance is a natural generalization of edit distance to ordered rooted trees. Such a generalization extends the applications of edit distance to areas such as computational biology, structured data analysis (e.g., XML), image analysis, and compiler optimization. Perhaps the most notable application of tree edit distance is in the analysis of RNA molecules in computational biology where the secondary structure of RNA is typically represented as a rooted tree.

We study fair allocation of indivisible goods to agents with unequal entitlements. Fair allocation has been the subject of many studies in both divisible and indivisible settings. Our emphasis is on the case where the goods are indivisible and agents have unequal entitlements. This problem is a generalization of the work by Procaccia and Wang (2014) wherein the agents are assumed to be symmetric with respect to their entitlements. Although Procaccia and Wang show an almost fair (constant approximation) allocation exists in their setting, our main result is in sharp contrast to their observation. We show that, in some cases with n agents, no allocation can guarantee better than 1/n approximation of a fair allocation when the entitlements are not necessarily equal. Furthermore, we devise a simple algorithm that ensures a 1/n approximation guarantee. Our second result is for a restricted version of the problem where the valuation of every agent for each good is bounded by the total value he wishes to receive in a fair allocation. Although this assumption might seem without loss of generality, we show it enables us to find a 1/2 approximation fair allocation via a greedy algorithm. Finally, we run some experiments on real-world data and show that, in practice, a fair allocation is likely to exist. We also support our experiments by showing positive results for two stochastic variants of the problem, namely stochastic agents and stochastic items.

In an instance of the network design problem, we are given a graph G=(V,E), an edge-cost function c:E -> R^{>= 0}, and a connectivity criterion. The goal is to find a minimum-cost subgraph H of G that meets the connectivity requirements. An important family of this class is the survivable network design problem (SNDP): given non-negative integers r_{u v} for each pair u,v in V, the solution subgraph H should contain r_{u v} edge-disjoint paths for each pair u and v. While this problem is known to admit good approximation algorithms in the offline case, the problem is much harder in the online setting. Gupta, Krishnaswamy, and Ravi [Gupta et al., 2012] (STOC'09) are the first to consider the online survivable network design problem. They demonstrate an algorithm with competitive ratio of O(k log^3 n), where k=max_{u,v} r_{u v}. Note that the competitive ratio of the algorithm by Gupta et al. grows linearly in k. Since then, an important open problem in the online community [Naor et al., 2011; Gupta et al., 2012] is whether the linear dependence on k can be reduced to a logarithmic dependency. Consider an online greedy algorithm that connects every demand by adding a minimum cost set of edges to H. Surprisingly, we show that this greedy algorithm significantly improves the competitive ratio when a congestion of 2 is allowed on the edges or when the model is stochastic. While our algorithm is fairly simple, our analysis requires a deep understanding of k-connected graphs. In particular, we prove that the greedy algorithm is O(log^2 n log k)-competitive if one satisfies every demand between u and v by r_{uv}/2 edge-disjoint paths. The spirit of our result is similar to the work of Chuzhoy and Li [Chuzhoy and Li, 2012] (FOCS'12), in which the authors give a polylogarithmic approximation algorithm for edge-disjoint paths with congestion 2. Moreover, we study the greedy algorithm in the online stochastic setting. We consider the i.i.d. model, where each online demand is drawn from a single probability distribution, the unknown i.i.d. model, where every demand is drawn from a single but unknown probability distribution, and the prophet model in which online demands are drawn from (possibly) different probability distributions. Through a different analysis, we prove that a similar greedy algorithm is constant competitive for the i.i.d. and the prophet models. Also, the greedy algorithm is O(log n)-competitive for the unknown i.i.d. model, which is almost tight due to the lower bound of [Garg et al., 2008] for single connectivity.

Protecting valuable \em targets from an adversary is an ever-important international concern with far-reaching applications in wildlife protection, border protection, counter-terrorism, protection of ships from piracy, etc. As a successful recent approach, \em security games cast these issues as two-player games between a \em defender and an \em attacker. The defender decides on how to allocate the available \em resources to protect targets against the attacker who strives to inflict damage on them. The main question of interest here is equilibrium computation. Our focus in this paper is on \em spatio-temporal security games. However, inspired by the paper of Xu [EC'16], we start with a general model of security games and show that any approximation (of any factor) for the defender's best response (DBR) problem leads to an approximation of the same factor for the actual game. In most applications of security games, the targets are mobile. This leads to a well-studied class of succinct games, namely \em spatio-temporal security games, that is played in space and time. In such games, the defender has to specify a time-dependent patrolling strategy over a spatial domain to protect a set of moving targets. We give a generalized model of prior spatio-temporal security games that is played on a base graph G . That is, the patrols can be placed on the vertices of G and move along its edges over time. This unifies and generalizes prior spatio-temporal models that only consider specific spatial domains such as lines or grids. Graphs can further model many other domains of practical interest such as roads, internal maps of buildings, etc. Finding an optimal defender strategy becomes NP-hard on general graphs. To overcome this, we give an LP relaxation of the DBR problem and devise a rounding technique to obtain an almost optimal integral solution. More precisely, we show that one can achieve a $(1-ε)$-approximation in polynomial time if we allow the defender to use $łceil łn(1/ε)\rceil$ times more patrols. We later show that this result is in some sense the best possible polynomial time algorithm (unless P=NP). Furthermore, we show that by using a novel \em dependent rounding technique, the same LP relaxation gives an optimal solution for specific domains of interest, such as one-dimensional spaces. This result simplifies and improves upon the prior algorithm of Behnezhad et al. ~[EC'17] on several aspects and can be generalized to other graphs of interest such as cycles. Lastly, we note that most prior algorithms for security games assume that the attacker attacks only once and become intractable for a super-constant number of attacks. Our algorithms are fully polynomial in the input size and work for any given number of attacks.

We study the problem of fair allocation for indivisible goods. We use the maxmin share paradigm introduced by Budish~\citeBudish:first as a measure for fairness. \procacciafirst ~\citeProcaccia:first were the first to investigate this fundamental problem in the additive setting. They show that a maxmin guarantee (1-$\MMS$ allocation) is not always possible even when the number of agents is limited to 3. While the existence of an approximation solution (e.g. a $1/2$-$\MMS$ allocation) is quite straightforward, improving the guarantee becomes subtler for larger constants. \sprocacciafirst ~\citeProcaccia:first provide a proof for the existence of a $2/3$-$\MMS$ allocation and leave the question open for better guarantees. Our main contribution is an answer to the above question. We improve the result of \sprocacciafirst~to a $3/4$ factor in the additive setting. The main idea for our $3/4$-$\MMS$ allocation method is clustering the agents. To this end, we introduce three notions and techniques, namely reducibility, matching allocation, and cycle-envy-freeness, and prove the approximation guarantee of our algorithm via non-trivial applications of these techniques. Our analysis involves coloring and double counting arguments that might be of independent interest. One major shortcoming of the current studies on fair allocation is the additivity assumption on the valuations. We alleviate this by extending our results to the case of submodular, fractionally subadditive, and subadditive settings. More precisely, we give constant approximation guarantees for submodular and XOS agents, and a logarithmic approximation for the case of subadditive agents. Furthermore, we complement our results by providing close upper bounds for each class of valuation functions. Finally, we present algorithms to find such allocations for additive, submodular, and XOS settings in polynomial time. The reader can find a summary of our results in Table \refresultstable.

We study mechanism design for procurement auctions in which the goal is to buy a subset of items or hire a team of providers. In order to measure the efficiency of a mechanism, one defines an appropriate benchmark which denotes a reasonable expectation of the payments and defines the overpayment of a mechanism based on the benchmark. This ratio is called the \em frugality ratio of the mechanism. Procurement auctions are well-studied and benchmarks proposed for these auctions have evolved over a sequence of papers ~\citearcher2007frugal,chen2010frugal,elkind2007frugality,karlinbeyond,kempe2010frugal. In this work, we introduce a newer benchmark, and based on that, study classic procurement auctions. Our benchmark addresses critical issues raised by the unintuitive behavior of the previous benchmarks. We show two attractive properties for our benchmark which have been lacking in the previous proposals: \em monotonicity and \em smoothness. Based on our benchmark, we provide positive results for vertex cover and knapsack auctions. Prior to this work, \kempefrugal\citekempe2010frugal propose a constant approximation mechanism for vertex cover auctions. However, their analysis suffers from an error. We give a correct analysis to the mechanism of \kempefrugal\citekempe2010frugal with respect to our benchmark. In particular, we prove their mechanism is optimal up to a constant factor. Our analysis is different from what \kempefrugal\citekempe2010frugal propose. We also study the knapsack auctions and give a truthful mechanism for such auctions with a bounded frugality ratio. We show that this is almost tight by presenting a lower bound on the frugality ratio of any truthful mechanism for such auctions. All our results depend on both properties of the benchmark.

Mixed strategies are often evaluated based on the expected payoff that they guarantee. This is not always desirable. In this paper, we consider games for which maximizing the expected payoff deviates from the actual goal of the players. To address this issue, we introduce the notion of a (u,p)-maxmin strategy which ensures receiving a minimum utility of u with probability at least p. We then give approximation algorithms for the problem of finding a (u, p)-maxmin strategy for these games. The first game that we consider is Colonel Blotto, a well-studied game that was introduced in 1921. In the Colonel Blotto game, two colonels divide their troops among a set of battlefields. Each battlefield is won by the colonel that puts more troops in it. The payoff of each colonel is the weighted number of battlefields that she wins. We show that maximizing the expected payoff of a player does not necessarily maximize her winning probability for certain applications of Colonel Blotto. For example, in presidential elections, the players’ goal is to maximize the probability of winning more than half of the votes, rather than maximizing the expected number of votes that they get. We give an exact algorithm for a natural variant of continuous version of this game. More generally, we provide constant and logarithmic approximation algorithms for finding (u, p)-maxmin strategies. We also introduce a security game version of Colonel Blotto which we call auditing game. It is played between two players, a defender and an attacker. The goal of the defender is to prevent the attacker from changing the outcome of an instance of Colonel Blotto. Again, maximizing the expected payoff of the defender is not necessarily optimal. Therefore we give a constant approximation for (u, p)-maxmin strategies.

The edit distance between two strings is defined as the smallest number of insertions, deletions, and substitutions that need to be made to transform one of the strings to another one. Approximating edit distance in subquadratic time is “one of the biggest unsolved problems in the field of combinatorial pattern matching” [21]. Our main result is a quantum constant approximation algorithm for computing the edit distance in truly subquadratic time. More precisely, we give an O(n1.858) quantum algorithm that approximates the edit distance within a factor of 7. We further extend this result to an O(n1.781) quantum algorithm that approximates the edit distance within a larger constant factor. Our solutions are based on a framework for approximating edit distance in parallel settings. This framework requires as black box an algorithm that computes the distances of several smaller strings all at once. For a quantum algorithm, we reduce the black box to metric estimation and provide efficient algorithms for approximating it. We further show that this framework enables us to approximate edit distance in distributed settings. To this end, we provide a MapReduce algorithm to approximate edit distance within a factor of 3, with sublinearly many machines and sublinear memory. Also, our algorithm runs in a logarithmic number of rounds.

The knapsack problem is a fundamental problem in combinatorial optimization. It has been studied extensively from theoretical as well as practical perspectives as it is one of the most well-known NP-hard problems. The goal is to pack a knapsack of size t with the maximum value from a collection of n items with given sizes and values.

Online advertising allows advertisers to implement fine-tuned targeting of users. While such precise targeting leads to more effective advertising, it introduces challenging multidimensional pricing and bidding problems for publishers and advertisers. In this context, advertisers and publishers need to deal with an exponential number of possibilities. As a result, designing efficient and compact multidimensional bidding and pricing systems and algorithms are practically important for online advertisement. Compact bidding languages have already been studied in the context of multiplicative bidding. In this paper, we study the compact pricing problem.

In the Colonel Blotto game, which was initially introduced by Borel in 1921, two colonels simultaneously distribute their troops across different battlefields.The winner of each battlefield is determined independently by a winner-take-all rule. The ultimate payoff of each colonel is the number of battlefields he wins. This game is commonly used for analyzing a wide range of applications such as the U.S presidential election, innovative technology competitions, advertisements, etc. There have been persistent efforts for finding the optimal strategies for the Colonel Blotto game. After almost a century Ahmadinejad, Dehghani, Hajiaghayi, Lucier, Mahini, and Seddighin provided a poly-time algorithm for finding the optimal strategies. They first model the problem by a Linear Program (LP) with exponential number of constraints and use Ellipsoid method to solve it. However, despite the theoretical importance of their algorithm, it ishighly impractical. In general, even Simplex method (despite its exponential running-time) performs better than Ellipsoid method in practice. In this paper, we provide the first polynomial-size LP formulation of the optimal strategies for the Colonel Blotto game. We use linear extension techniques. Roughly speaking, we project the strategy space polytope to a higher dimensional space, which results in a lower number of facets for the polytope.We use this polynomial-size LP to provide a novel, simpler and significantly faster algorithm for finding the optimal strategies for the Colonel Blotto game. We further show this representation is asymptotically tight in terms of the number of constraints. We also extend our approach to multi-dimensional Colonel Blotto games, and implement our algorithm to observe interesting properties of Colonel Blotto; for example, we observe the behavior of players in the discrete model is very similar to the previously studied continuous model.

In this paper we study a stochastic variant of the celebrated $k$-server problem. In the k-server problem, we are required to minimize the total movement of k servers that are serving an online sequence of $t$ requests in a metric. In the stochastic setting we are given t independent distributions in advance, and at every time step i a request is drawn from P_i. Designing the optimal online algorithm in such setting is NP-hard, therefore the emphasis of our work is on designing an approximately optimal online algorithm. We first show a structural characterization for a certain class of non-adaptive online algorithms. We prove that in general metrics, the best of such algorithms has a cost of no worse than three times that of the optimal online algorithm. Next, we present an integer program that finds the optimal algorithm of this class for any arbitrary metric. Finally by rounding the solution of the linear relaxation of this program, we present an online algorithm for the stochastic k-server problem with an approximation factor of $3$ in the line and circle metrics and factor of O(log n) in general metrics. In this way, we achieve an approximation factor that is independent of k, the number of servers. Moreover, we define the Uber problem, motivated by extraordinary growth of online network transportation services. In the Uber problem, each demand consists of two points -a source and a destination- in the metric. Serving a demand is to move a server to its source and then to its destination. The objective is again minimizing the total movement of the k given servers. It is not hard to show that given an alpha-approximation algorithm for the k-server problem, we can obtain a max{3,alpha}-approximation algorithm for the Uber problem. Motivated by the fact that demands are usually highly correlated with the time (e.g. what day of the week or what time of the day the demand is arrived), we study the stochastic Uber problem. Using our results for stochastic k-server we can obtain a 3-approximation algorithm for the stochastic Uber problem in line and circle metrics, and a O(log n)-approximation algorithm for a general metric of size n. Furthermore, we extend our results to the correlated setting where the probability of a request arriving at a certain point depends not only on the time step but also on the previously arrived requests.

We study the problem of computing Nash equilibria of zero-sum games.Many natural zero-sum games have exponentially many strategies, but highly structured payoffs. For example, in the well-studied Colonel Blotto game (introduced by Borel in 1921), players must divide a pool of troops among a set of battlefields with the goal of winning (i.e., having more troops in) a majority. The Colonel Blotto game is commonly used for analyzing a wide range of applications from the U.S presidential election, to innovative technology competitions, toadvertisement, to sports.However, because of the size of the strategy space, standard methods for computing equilibria of zero-sum games fail to be computationally feasible.Indeed, despite its importance, only few solutions for special variants of the problem are known. In this paper we show how to compute equilibria of Colonel Blotto games. Moreover, our approach takes the form of a general reduction: to find a Nash equilibrium of a zero-sum game, it suffices to design a separation oracle for the strategy polytope of any bilinear game that is payoff-equivalent. We then apply this technique to obtain the first polytime algorithms for a variety of games. In addition to Colonel Blotto, we also show how to compute equilibria in an infinite-strategy variant called the General Lotto game; this involves showing how to prune the strategy space to a finite subset before applying our reduction. We also consider the class of dueling games, first introduced by Immorlica et al. (2011). We show that our approach provably extends the class of dueling games for which equilibria can be computed: we introduce a new dueling game, the matching duel, on which prior methods fail to be computationally feasible but upon which our reduction can be applied.

We design the first online algorithm with poly-logarithmic competitive ratio for the edge-weighted degree-bounded Steiner forest (EW-DB-SF) problem and its generalized variant. We obtain our result by demonstrating a new generic approach for solving mixed packing/covering integer programs in the online paradigm. In EW-DB-SF, we are given an edge-weighted graph with a degree bound for every vertex. Given a root vertex in advance, we receive a sequence of terminal vertices in an online manner. Upon the arrival of a terminal, we need to augment our solution subgraph to connect the new terminal to the root. The goal is to minimize the total weight of the solution while respecting the degree bounds on the vertices. In the offline setting, edge-weighted degree-bounded Steiner tree (EW-DB-ST) and its many variations have been extensively studied since early eighties. Unfortunately, the recent advancements in the online network design problems are inherently difficult to adapt for degree-bounded problems. In particular, it is not known whether the fractional solution obtained by standard primal-dual techniques for mixed packing/covering LPs can be rounded online. In contrast, in this paper we obtain our result by using structural properties of the optimal solution, and reducing the EW-DB-SF problem to an exponential-size mixed packing/covering integer program in which every variable appears only once in covering constraints. We then design a generic integral algorithm for solving this restricted family of IPs. As mentioned above, we demonstrate a new technique for solving mixed packing/covering integer programs. Define the covering frequency k of a program as the maximum number of covering constraints in which a variable can participate. Let m denote the number of packing constraints. We design an online deterministic integral algorithm with competitive ratio of O(k*log(m)) for the mixed packing/covering integer programs. We prove the tightness of our result by providing a matching lower bound for any randomized algorithm. We note that our solution solely depends on m and k. Indeed, there can be exponentially many variables. Furthermore, our algorithm directly provides an integral solution, even if the integrality gap of the program is unbounded. We believe this technique can be used as an interesting alternative for the standard primal-dual techniques in solving online problems.

We study competition in a general framework introduced by Immorlica, Kalai, Lucier, Moitra, Postlewaite, and Tennenholtz and answer their main open question. Immorlica et al. considered classic optimization problems in terms of competition and introduced a general class of games called dueling games. They model this competition as a zero-sum game, where two players are competing for a user’s satisfaction. In their main and most natural game, the ranking duel, a user requests a webpage by submitting a query and players output an ordering over all possible webpages based on the submitted query. The user tends to choose the ordering which displays her requested webpage in a higher rank. The goal of both players is to maximize the probability that her ordering beats that of her opponent and gets the user's attention. Immorlica et al. show this game directs both players to provide suboptimal search results. However, they leave the following as their main open question: "does competition between algorithms improve or degrade expected performance?" (see the introduction for more quotes) In this paper, we resolve this question for the ranking duel and a more general class of dueling games. More precisely, we study the quality of orderings in a competition between two players. This game is a zero-sum game, and thus any Nash equilibrium of the game can be described by minimax strategies. Let the value of the user for an ordering be a function of the position of her requested item in the corresponding ordering, and the social welfare for an ordering be the expected value of the corresponding ordering for the user. We propose the price of competition which is the ratio of the social welfare for the worst minimax strategy to the social welfare obtained by asocial planner. Finding the price of competition is another approach to obtain structural results of Nash equilibria. We use this criterion for analyzing the quality of orderings in the ranking duel. Although Immorlica et al. show that the competition leads to suboptimal strategies, we prove the quality of minimax results is surprisingly close to that of the optimum solution. In particular, via a novel factor-revealing LP for computing price of anarchy, we prove if the value of the user for an ordering is a linear function of its position, then the price of competition is at least 0.612 and bounded above by 0.833. Moreover we consider the cost minimization version of the problem. We prove, the social cost of the worst minimax strategy is at most 3 times the optimal social cost. Last but not least, we go beyond linear valuation functions and capture the main challenge for bounding the price of competition for any arbitrary valuation function. We present a principle which states that the lower bound for the price of competition for all 0-1 valuation functions is the same as the lower bound for the price of competition for all possible valuation functions. It is worth mentioning that this principle not only works for the ranking duel but also for all dueling games. This principle says, in any dueling game, the most challenging part of bounding the price of competition is finding a lower bound for 0-1 valuation functions. We leverage this principle to show that the price of competition is at least 0.25 for the generalized ranking duel.

People make decisions and express their opinions according to their communities. A natural idea for controlling the diffusion of a behavior is to find influential people, and employ them to spread a desired behavior. We investigate an influencing problem when individuals' behaviors are affected by their friends in an opinion formation process. Our goal is to design efficient algorithms for finding opinion leaders such that changing their opinions has a great impact on the overall external behaviors in the society. We study directed social networks and define a set of problems like maximizing the sum of individuals' behaviors or maximizing the number of individuals whose external behaviors are above a threshold. We discuss the complexity of the defined problems and design polynomial-time optimum algorithms for the non NP-hard variants of them. We also propose polynomial-time approximation algorithms with guaranteed performances and prove inapproximability results for the NP-hard variants of these problems. Furthermore, we run simulations on real-world social networks and show our proposed algorithm outperforms the classical algorithms such as degree-based algorithm, closeness-based algorithm, and pagerank-based algorithm.