We study the problem of allocating indivisible goods among agents with additive valuation functions to achieve both fairness and efficiency under the constraint that each agent receives exactly the same number of goods (the balanced constraint). While this constraint is common in real-world scenarios such as team drafts or asset division, it significantly complicates the search for allocations that are both fair and efficient. Envy-freeness up to one good (EF1) is a well-established fairness notion for indivisible goods. Pareto optimality (PO) and its stronger variant, fractional Pareto optimality (fPO), are widely accepted efficiency criteria. Our main contribution establishes both the existence and polynomial-time computability of allocations that are simultaneously EF1 and fPO under balanced constraints in two fundamental cases: (1) when agents have at most two distinct types of valuation functions, and (2) when each agent has a personalized bivalued valuation. Our algorithms leverage novel applications of maximum-weight matching in bipartite graphs and duality theory, providing the first polynomial-time solutions for these cases and offering new insights for constrained fair division problems.

We study a sequential selling problem in which an agent receives daily offers to sell a good, incurs a holding cost each day, and is subject to sunk cost bias---allowing past, irrecoverable costs to influence present decisions. We introduce a formal model parameterizing the degree of sunk cost bias and distinguish between three behavioral types: optimistic (who ignore future bias), naive (who assume their current bias persists), and sophisticated (who anticipate the evolution of their own bias). For each type, we characterize the optimal selling strategy and precisely quantify the worst-case gap in expected objective profit compared to an unbiased agent. Our results show that optimistic agents can suffer a quadratic loss in profit due to excessive waiting, naive agents perform identically to unbiased agents, and sophisticated agents limit their losses to a linear function of the time horizon. These findings clarify how different anticipations of sunk cost bias affect sequential decision-making and suggest targeted interventions to mitigate inefficiency.

We study proportional cake cutting on the interval [0,1] under an equal-length constraint requiring each of the n agents to receive a bundle of length exactly 1/n and to assign value at least 1/n to that bundle. We determine the exact worst-case cut complexity of this problem. The exact value is 2n-2 cuts for every n ≥ 1. The lower bound follows from a simple identical-valuation instance, and the main contribution is the matching upper bound, since the only all-n upper bound previously available for this problem was quadratic. Our upper-bound proof starts from the constrained necklace-splitting theorem of Jojić, Panina, and Živaljević, which gives the required partition into equal-length bundles when the number of bundles is a prime power. The main difficulty is to convert this prime-power input into an exact all-n cut bound while preserving the equal-length constraint. When r is a prime-power divisor of n and s = n/r, our transfer principle constructs r equal-length bundles, builds a balanced fractional assignment of agents to bundles, rounds it by Hall’s theorem to an assignment in which each bundle receives exactly s agents, and recurses inside the bundles without additional overhead beyond the recursive cuts. Using the same constrained necklace-splitting theorem, we also show that 2n-2 cuts suffice for equal-length envy-freeness when n is a prime power. For all n, we give an O(n^1.525) upper bound via a peeling argument based on the Stromquist-Woodall exact-share theorem. The exact all-n envy-free cut complexity remains open.

This paper explores the fair allocation of indivisible items in a multidimensional setting, motivated by the need to address fairness in complex environments where agents assess bundles according to multiple criteria. Such multidimensional settings are not merely of theoretical interest but are central to many real-world applications. For example, cloud computing resources are evaluated based on multiple criteria such as CPU cores, memory, and network bandwidth. In such cases, traditional one-dimensional fairness notions fail to capture fairness across multiple attributes. To address these challenges, we study two relaxed variants of envy-freeness: weak simultaneously envy-free up to c goods (weak sEFc) and strong simultaneously envy-free up to c goods (strong sEFc), which accommodate the multidimensionality of agents’ preferences. Under the weak notion, for every pair of agents and for each dimension, any perceived envy can be eliminated by removing, if necessary, a different set of goods from the envied agent’s allocation. In contrast, the strong version requires selecting a single set of goods whose removal from the envied bundle simultaneously eliminates envy in every dimension. We provide upper and lower bounds on the relaxation parameter c that guarantee the existence of weak or strong sEFc allocations, where these bounds are independent of the total number of items. In addition, we present algorithms for checking whether a weak or strong sEFc allocation exists. Moreover, we establish NP-hardness results for checking the existence of weak sEF1 and strong sEF1 allocations.

Choice correspondences are crucial in decision-making, especially when faced with indifferences or ties. While tie-breaking can transform a choice correspondence into a choice function, it often introduces inefficiencies. This paper introduces a novel notion of path-independence (PI) for choice correspondences, extending the existing concept of PI for choice functions. Intuitively, a choice correspondence is PI if any consistent tie-breaking produces a PI choice function. This new notion yields several important properties. First, PI choice correspondences are rationalizabile, meaning they can be represented as the maximization of a utility function. This extends a core feature of PI in choice functions. Second, we demonstrate that the set of choices selected by a PI choice correspondence for any subset forms a generalized matroid. This property reveals that PI choice correspondences exhibit a nice structural property. Third, we establish that choice correspondences rationalized by ordinally concave functions inherently satisfy the PI condition. This aligns with recent findings that a choice function satisfies PI if and only if it can be rationalized by an ordinally concave function.

In this paper, we introduce the problem of Online Matching with Delays and Size-based Costs (OMDSC). The OMDSC problem involves m requests arriving online. At any time, a group can be formed by matching any number of requests that have been received but remain unmatched. The cost associated with each group is determined by the waiting time for each request within the group and size-dependent cost. The size-dependent cost is specified by a penalty function. Our goal is to partition all the incoming requests into multiple groups while minimizing the total associated cost. This problem is an extension of the TCP acknowledgment problem proposed by Dooly et al. (J. ACM, 2001). It generalizes the cost model for sending acknowledgments. This study reveals the competitive ratios for a fundamental case, in which the penalty function takes only values of either 0 or 1. We classify such penalty functions into three distinct cases: (i) a fixed penalty of 1 regardless of the group size, (ii) a penalty of 0 if and only if the group size is a multiple of a specific integer k, and (iii) other situations. The problem in case (i) is equivalent to the TCP acknowledgment problem, for which Dooly et al. proposed a 2-competitive algorithm. For case (ii), we first show that natural algorithms that match all remaining requests are Ω(√k)-competitive. We then propose an O(log k / log log k)-competitive deterministic algorithm by carefully managing the match size and timing, and prove its optimality. For any penalty function in case (iii), we demonstrate the non-existence of a competitive online algorithm. Additionally, we discuss competitive ratios for other typical penalty functions that are not restricted to take values of 0 or 1.

In this study, we investigate a scheduling problem on identical machines in which jobs require initial setup before execution. We assume that an algorithm can dynamically form a batch (i.e., a collection of jobs to be processed together) from the remaining jobs. The setup time is modeled as a known monotone function of the set of jobs within a batch, while the execution time of each job remains unknown until completion. This uncertainty poses significant challenges for minimizing the makespan. We address these challenges by considering two scenarios: each job batch must be assigned to a single machine, or a batch may be distributed across multiple machines. For both scenarios, we analyze settings with and without preemption. Across these four settings, we design online algorithms that achieve asymptotically optimal competitive ratios with respect to both the number of jobs and the number of machines.

We study the fair division of indivisible items with subsidies among n agents, where the absolute marginal valuation of each item is at most one. Under monotone nondecreasing valuations (where each item is a good), Brustle et al. [9] demonstrated that a maximum subsidy of 2 ( n − 1 ) and a total subsidy of 2 ( n − 1 ) 2 are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most n − 1 per agent and a total subsidy of at most n ( n − 1 ) / 2 . Moreover, when the valuations are monotone nondecreasing, we provide a polynomial-time algorithm that computes an envy-free allocation with a subsidy of at most n − 1.5 per agent and a total subsidy of at most ( n 2 − n − 1 ) / 2 .

We study the fair division of indivisible items with subsidies among n agents, where the absolute marginal valuation of each item is at most one. Under monotone valuations (where each item is a good), it is known that a maximum subsidy of 2(n-1) and a total subsidy of 2(n-1)² are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most n-1 per agent and a total subsidy of at most n(n-1)/2. Moreover, we present further improved bounds for monotone valuations.

The last success problem is an optimal stopping problem that aims to maximize the probability of stopping on the last success in a sequence of independent $n$ Bernoulli trials. In the classical setting where complete information about the distributions is available, Bruss~\cite{B00} provided an optimal stopping policy that ensures a winning probability of $1/e$. However, assuming complete knowledge of the distributions is unrealistic in many practical applications. This paper investigates a variant of the last success problem where samples from each distribution are available instead of complete knowledge of them. When a single sample from each distribution is allowed, we provide a deterministic policy that guarantees a winning probability of $1/4$. This is best possible by the upper bound provided by Nuti and Vondrák~\cite{NV23}. Furthermore, for any positive constant $ε$, we show that a constant number of samples from each distribution is sufficient to guarantee a winning probability of $1/e-ε$.

The fair allocation of mixed goods, consisting of both divisible and indivisible goods, has been a prominent topic of study in economics and computer science. We define an allocation as fair if its utility vector minimizes a symmetric strictly convex function. This fairness criterion includes standard ones such as maximum egalitarian social welfare and maximum Nash social welfare. We address the problem of minimizing a given symmetric strictly convex function when agents have binary valuations. If only divisible goods or only indivisible goods exist, the problem is known to be solvable in polynomial time. In this paper, firstly, we demonstrate that the problem is NP-hard even when all indivisible goods are identical. This NP-hardness is established even for maximizing egalitarian social welfare or Nash social welfare. Secondly, we provide a polynomial-time algorithm for the problem when all divisible goods are identical. To accomplish these, we exploit the proximity structure inherent in the problem. This provides theoretically important insights into the hybrid domain of convex optimization that incorporates both discrete and continuous aspects.

We study indivisible goods allocation problems, including real-life applications such as student placement in public schools, refugee resettlement, and student-project assignment. Such applications are often subject to constraints. This study aims to identify the constraints under which a desirable mechanism can be designed. Regarding the desirable properties of the mechanisms, we focus on Pareto efficiency for students (PE), individual rationality (IR), and group strategy-proofness (GSP). We consider two scenarios: one with and one without endowments. The applicability of either scenario in real-life applications depends on the specific circumstances.

Abstract In this paper, we study the following robust optimization problem. Given a set family representing feasibility and candidate objective functions, we choose a feasible set, and then an adversary chooses one objective function, knowing our choice. The goal is to find a randomized strategy (i.e., a probability distribution over the feasible sets) that maximizes the expected objective value in the worst case. This problem is fundamental in wide areas such as artificial intelligence, machine learning, game theory, and optimization. To solve the problem, we provide a general framework based on the dual linear programming problem. In the framework, we utilize the ellipsoid algorithm with the approximate separation algorithm. We prove that there exists an $$\alpha $$ α -approximation algorithm for our robust optimization problem if there exists an $$\alpha $$ α -approximation algorithm for finding a (deterministic) feasible set that maximizes a nonnegative linear combination of the candidate objective functions. Using our result, we provide approximation algorithms for the max–min fair randomized allocation problem and the maximum cardinality robustness problem with a knapsack constraint.

We study a fair division setting in which a number of players are to be fairly distributed among a set of teams. In our model, not only do the teams have preferences over the players as in the canonical fair division setting, but the players also have preferences over the teams. We focus on guaranteeing envy-freeness up to one player (EF1) for the teams together with a stability condition for both sides. We show that an allocation satisfying EF1, swap stability, and individual stability always exists and can be computed in polynomial time, even when teams may have positive or negative values for players. Similarly, a balanced and swap stable allocation that satisfies a relaxation of EF1 can be computed efficiently. When teams have nonnegative values for players, we prove that an EF1 and Pareto optimal allocation exists and, if the valuations are binary, can be found in polynomial time. We also examine the compatibility between EF1 and justified envy-freeness.

We investigate the problem of random assignment of indivisible goods, in which each agent has an ordinal preference and a constraint. Our goal is to characterize the conditions under which there always exists a random assignment that simultaneously satisfies efficiency and envy-freeness. The probabilistic serial mechanism ensures the existence of such an assignment for the unconstrained setting. In this paper, we consider a more general setting in which each agent can consume a set of items only if the set satisfies her feasibility constraint. Such constraints must be taken into account in student course placements, employee shift assignments, and so on. We demonstrate that an efficient and envy-free assignment may not exist even for the simple case of partition matroid constraints, where the items are categorized, and each agent demands one item from each category. We then identify special cases in which an efficient and envy-free assignment always exists. For these cases, the probabilistic serial cannot be naturally extended; therefore, we provide mechanisms to find the desired assignment using various approaches.

Network analysis has played a key role in knowledge discovery and data mining. In many real-world applications in recent years, we are interested in mining multilayer networks, where we have a number of edge sets called layers, which encode different types of connections and/or time-dependent connections over the same set of vertices. Among many network analysis techniques, dense subgraph discovery, aiming to find a dense component in a network, is an essential primitive with a variety of applications in diverse domains. In this paper, we introduce a novel optimization model for dense subgraph discovery in multilayer networks. Our model aims to find a stochastic solution, i.e., a probability distribution over the family of vertex subsets, rather than a single vertex subset, whereas it can also be used for obtaining a single vertex subset. For our model, we design an LP-based polynomial-time exact algorithm. Moreover, to handle large-scale networks, we also devise a simple, scalable preprocessing algorithm, which often reduces the size of the input networks significantly and results in a substantial speed-up. Computational experiments demonstrate the validity of our model and the effectiveness of our algorithms.

The modularity is the best known and widely used quality function for community detection in graphs. We investigate the approximability of the modularity maximization problem and some related problems. We first design a polynomial-time 0.4209-additive approximation algorithm for the modularity maximization problem, which improves the current best additive approximation error of 0.4672. Our theoretical analysis also demonstrates that the proposed algorithm obtains a nearly-optimal solution for any instance with a high modularity value. We next design a polynomial-time 0.1660-additive approximation algorithm for the maximum modularity cut problem. Finally, we extend our algorithm to some related problems.

We study the problem of fairly allocating a set of indivisible goods to risk-neutral agents in a stochastic setting. We propose an (approximation) algorithm to find a stochastic allocation that maximizes the minimum utility among the agents. The algorithm runs by repeatedly finding an (approximate) allocation to maximize the total virtual utility of the agents. This implies that the problem is solvable in polynomial time when the utilities are gross-substitutes (which is a subclass of submodular). When the utilities are submodular, we can find a (1 − 1/e)-approximate solution for the problem and this is best possible unless P=NP. We also extend the problem where a stochastic allocation must satisfy the (ex ante) envy-freeness. Under this condition, we demonstrate that the problem is NP-hard even when every agent has an additive utility with a matroid constraint (which is a subclass of gross-substitutes). Furthermore, we propose a polynomial-time algorithm for the setting with a restriction that the matroid constraint is common to all agents.

Quality assurance is one of the most important problems in crowdsourcing and human computation, and it has been extensively studied from various aspects. Typical approaches for quality assurance include unsupervised approaches such as introducing task redundancy (i.e., asking the same question to multiple workers and aggregating their answers) and supervised approaches such as using worker performance on past tasks or injecting qualification questions into tasks in order to estimate the worker performance. In this paper, we propose to utilize the worker performance as a global constraint for inferring the true answers. The existing semi-supervised approaches do not consider such use of qualification questions. We also propose to utilize the constraint as a regularizer combined with existing statistical aggregation methods. The experiments using heterogeneous multiple-choice questions demonstrate that the performance constraint not only has the power to estimate the ground truths when used by itself, but also boosts the existing aggregation methods when used as a regularizer.

In this paper, we study the following robust optimization problem. Given an independence system and candidate objective functions, we choose an independent set, and then an adversary chooses one objective function, knowing our choice. The goal is to find a randomized strategy (i.e., a probability distribution over the independent sets) that maximizes the expected objective value in the worst case. This problem is fundamental in wide areas such as artificial intelligence, machine learning, game theory and optimization. To solve the problem, we propose two types of schemes for designing approximation algorithms. One scheme is for the case when objective functions are linear. It first finds an approximately optimal aggregated strategy and then retrieves a desired solution with little loss of the objective value. The approximation ratio depends on a relaxation of an independence system polytope. As applications, we provide approximation algorithms for a knapsack constraint or a matroid intersection by developing appropriate relaxations and retrievals. The other scheme is based on the multiplicative weights update (MWU) method. The direct application of the MWU method does not yield a strict multiplicative approximation algorithm but yield one with an additional additive error term. A key technique to overcome the issue is to introduce a new concept called (η,γ)-reductions for objective functions with parameters η and γ. We show that our scheme outputs a nearly α-approximate solution if there exists an α-approximation algorithm for a subproblem defined by (η,γ)-reductions. This improves approximation ratios in previous results. Using our result, we provide approximation algorithms when the objective functions are submodular or correspond to the cardinality robustness for the knapsack problem.

Aggregating responses from crowd workers is a fundamental task in the process of crowdsourcing. In cases where a few experts are overwhelmed by a large number of non-experts, most answer aggregation algorithms such as the majority voting fail to identify the correct answers. Therefore, it is crucial to extract reliable experts from the crowd workers. In this study, we introduce the notion of "expert core", which is a set of workers that is very unlikely to contain a non-expert. We design a graph-mining-based efficient algorithm that exactly computes the expert core. To answer the aggregation task, we propose two types of algorithms. The first one incorporates the expert core into existing answer aggregation algorithms such as the majority voting, whereas the second one utilizes information provided by the expert core extraction algorithm pertaining to the reliability of workers. We then give a theoretical justification for the first type of algorithm. Computational experiments using synthetic and real-world datasets demonstrate that our proposed answer aggregation algorithms outperform state-of-the-art algorithms.

In this paper, we introduce online knapsack problems with a resource buffer. In the problems, we are given a knapsack with capacity $1$, a buffer with capacity $R\ge 1$, and items that arrive one by one. Each arriving item has to be taken into the buffer or discarded on its arrival irrevocably. When every item has arrived, we transfer a subset of items in the current buffer into the knapsack. Our goal is to maximize the total value of the items in the knapsack. We consider four variants depending on whether items in the buffer are removable (i.e., we can remove items in the buffer) or non-removable, and proportional (i.e., the value of each item is proportional to its size) or general. For the general&non-removable case, we observe that no constant competitive algorithm exists for any $R\ge 1$. For the proportional&non-removable case, we show that a simple greedy algorithm is optimal for every $R\ge 1$. For the general&removable and the proportional&removable cases, we present optimal algorithms for small $R$ and give asymptotically nearly optimal algorithms for general $R$.

We consider the maximization problem of monotone submodular functions under an uncertain knapsack constraint. Specifically, the problem is discussed in the situation where the knapsack capacity is not given explicitly and can be accessed only through an oracle that answers whether or not the current solution is feasible when an item is added to the solution. Assuming that cancellation of the last item is allowed when it overflows the knapsack capacity, we discuss the robustness ratios of adaptive policies for this problem, which are the worst case ratios of the objective values achieved by the output solutions to the optimal objective values. We present a randomized policy of robustness ratio $(1-1/e)/2$ and a deterministic policy of robustness ratio $2(1-1/e)/21$. We also consider a universal policy that chooses items following a precomputed sequence. We present a randomized universal policy of robustness ratio $(1-1/\sqrt[4]{e})/2$. When cancellation is not allowed, no randomized adaptive policy achieves a constant robustness ratio. Because of this hardness, we assume that a probability distribution of the knapsack capacity is given, and we consider computing a sequence of items that maximizes the expected objective value. We present a polynomial time randomized algorithm of approximation ratio $(1-1/\sqrt[4]{e})/4-\epsilon$ for any small constant $\epsilon >0$.

This paper examines two-sided matching with budget constraints where one side (a firm or hospital) can make monetary transfers (offer wages) to the other (a worker or doctor). In a standard model, while multiple doctors can be matched to a single hospital, a hospital has a maximum quota; thus, the number of doctors assigned to a hospital cannot exceed a certain limit. In our model, in contrast, a hospital has a fixed budget; that is, the total amount of wages allocated by each hospital to doctors is constrained. With budget constraints, stable matchings may fail to exist and checking for the existence is hard. To deal with the nonexistence of stable matchings, we extend the "matching with contracts" model of Hatfield and Milgrom so that it deals with approximately stable matchings where each of the hospitals' utilities after deviation can increase by a factor up to a certain amount. We then propose two novel mechanisms that efficiently return a stable matching that exactly satisfies the budget constraints. Specifically, by sacrificing strategy-proofness, our first mechanism achieves the best possible bound. We also explore a special case on which a simple mechanism is strategy-proof for doctors, while maintaining the best possible bound of the general case.

We study a decentralized matching market in which each firm sequentially makes offers to potential workers. For each offer, the worker can choose "accept" or "reject," but the decision is irrevocable. The acceptance of an offer guarantees her job at the firm, but it may also eliminate chances of better offers from other firms in the future. We formulate this market as a perfect-information extensive-form game played by the workers. Each instance of this game has a unique subgame perfect equilibrium (SPE), which does not necessarily lead to a stable matching and has some perplexing properties. Our aim is to establish the complexity of computing the SPE, or more precisely, deciding whether each offer is accepted in the SPE. We show that the tractability of this problem drastically changes according to the number of potential offers related to each firm and worker. If each firm makes offers to at most two workers (or each worker receives offers from at most two firms), then the problem is efficiently solved by a variant of the deferred acceptance algorithm. In contrast, the problem is PSPACE-hard even if both firms and workers are related to at most three offers.

In this paper, we introduce maximum composition ordering problems. The input is n real functions $$f_1,\dots ,f_n:\mathbb {R}\rightarrow \mathbb {R}$$ and a constant $$c\in \mathbb {R}$$ . We consider two settings: total and partial compositions. The maximum total composition ordering problem is to compute a permutation $$\sigma :[n]\rightarrow [n]$$ which maximizes $$f_{\sigma (n)}\circ f_{\sigma (n-1)}\circ \dots \circ f_{\sigma (1)}(c)$$ , where $$[n]=\{1,\dots ,n\}$$ . The maximum partial composition ordering problem is to compute a permutation $$\sigma :[n]\rightarrow [n]$$ and a nonnegative integer $$k~(0\le k\le n)$$ which maximize $$f_{\sigma (k)}\circ f_{\sigma (k-1)}\circ \dots \circ f_{\sigma (1)}(c)$$ . We propose $$\mathrm {O}(n\log n)$$ time algorithms for the maximum total and partial composition ordering problems for monotone linear functions $$f_i$$ , which generalize linear deterioration and shortening models for the time-dependent scheduling problem. We also show that the maximum total composition ordering problem can be solved in polynomial time if $$f_i$$ is of the form $$\max \{a_ix+b_i,d_i,x\}$$ for some constants $$a_i\,(\ge 0)$$ , $$b_i$$ and $$d_i$$ . As a corollary, we show that the two-valued free-order secretary problem can be solved in polynomial time. We finally prove that there exists no constant-factor approximation algorithm for the problems, even if $$f_i$$ ’s are monotone, piecewise linear functions with at most two pieces, unless P $$=$$ NP.

The optimal pricing problem is a fundamental problem that arises in combinatorial auctions. Suppose that there is one seller who has indivisible items and multiple buyers who want to purchase a combination of the items. The seller wants to sell his items for the highest possible prices, and each buyer wants to maximize his utility (i.e., valuation minus payment) as long as his payment does not exceed his budget. The optimal pricing problem seeks a price of each item and an assignment of items to buyers such that every buyer achieves the maximum utility under the prices. The goal of the problem is to maximize the total payment from buyers. In this paper, we consider the case that the valuations are submodular. We show that the problem is computationally hard even if there exists only one buyer. Then we propose approximation algorithms for the unlimited budget case. We also extend the algorithm for the limited budget case when there exists one buyer and multiple buyers collaborate with each other.

Suppose that we are given sample access to an unknown distribution p over n elements and an explicit distribution q over the same n elements. We would like to reject the null hypothesis "p=q" after seeing as few samples as possible, when p =/= q, while we never want to reject the null, when p=q. Well-known results show that Theta(sqrt{n}/epsilon^2) samples are necessary and sufficient for distinguishing whether p equals q versus p is epsilon-far from q in total variation distance. However, this requires the distinguishing radius epsilon to be fixed prior to deciding how many samples to request. Our goal is instead to design sequential hypothesis testers, i.e. online algorithms that request i.i.d. samples from p and stop as soon as they can confidently reject the hypothesis p=q, without being given a lower bound on the distance between p and q, when p =/= q. In particular, we want to minimize the number of samples requested by our tests as a function of the distance between p and q, and if p=q we want the algorithm, with high probability, to never reject the null. Our work is motivated by and addresses the practical challenge of sequential A/B testing in Statistics. We show that, when n=2, any sequential hypothesis test must see Omega(1/{d_{tv}(p,q)^2} log log 1/{d_{tv}(p,q)}) samples, with high (constant) probability, before it rejects p=q, where d_{tv}(p,q) is the - unknown to the tester - total variation distance between p and q. We match the dependence of this lower bound on d_{tv}(p,q) by proposing a sequential tester that rejects p=q from at most O({\sqrt{n}}/{d_{tv}(p,q)^2}log log 1/{d_{tv}(p,q)}) samples with high (constant) probability. The Omega(sqrt{n}) dependence on the support size n is also known to be necessary. We similarly provide two-sample sequential hypothesis testers, when sample access is given to both p and q, and discuss applications to sequential A/B testing.

This paper deals with two-sided matching with budget constraints where one side (firm or hospital) can make monetary transfers (offer wages) to the other (worker or doctor). In a standard model, while multiple doctors can be matched to a single hospital, a hospital has a maximum quota: the number of doctors assigned to a hospital cannot exceed a certain limit. In our model, a hospital instead has a fixed budget: the total amount of wages allocated by each hospital to doctors is constrained. With budget constraints, stable matchings may fail to exist and checking the existence is hard. To deal with the nonexistence of stable matchings, we extend the “matching with contracts” model by Hatfield and Milgrom, so that it handles near-feasible matchings that exceeds each budget of the hospitals by a certain amount. We then propose two novel mechanisms that efficiently return such a near-feasible matching that is stable with respect to the actual amount of wages allocated by each hospital. In particular, by sacrificing strategy-proofness, our second mechanism achieves the best possible bound.

In this paper, we study the video advertising in the context of internet advertising. Video advertising is a rapidly growing industry, but its computational aspects have not yet been investigated. A difference between video advertising and traditional display advertising is that the former requires more time to be viewed. In contrast to a traditional display advertisement, a video advertisement has no influence over a user unless the user watches it for a certain amount of time. Previous studies have not considered the length of video advertisements, and time spent by users to watch them. Motivated by this observation, we formulate a new online optimization problem for optimizing the allocation of video advertisements, and we develop a nearly (1 − 1/e)-competitive algorithm for finding an envy-free allocation of video advertisements.

This paper studies optimal matroid partitioning problems for various objective functions. In the problem, we are given a finite set $E$ and $k$ weighted matroids $(E, \mathcal{I}_i, w_i)$, $i = 1, \dots, k$, and our task is to find a minimum partition $(I_1,\dots,I_k)$ of $E$ such that $I_i \in \mathcal{I}_i$ for all $i$. For each objective function, we give a polynomial-time algorithm or prove NP-hardness. In particular, for the case when the given weighted matroids are identical and the objective function is the sum of the maximum weight in each set (i.e., $\sum_{i=1}^k\max_{e\in I_i}w_i(e)$), we show that the problem is strongly NP-hard but admits a PTAS.

In this paper, we study the effect of surrogate objective functions in optimization problems. We introduce surrogate ratio as a measure of such effect, where the surrogate ratio is the ratio between the optimal values of the original and surrogate objective functions. We prove that the surrogate ratio is at most mu^{|1/p - 1/q|} when the objective functions are p- and q-norms, and the feasible region is a mu-dimensional space (i.e., a subspace of R^mu), a mu-intersection of matroids, or a mu-extendible system. We also show that this is the best possible bound. In addition, for mu-systems, we demonstrate that the ratio becomes mu^{1/p} when p < q and unbounded if p > q. Here, a mu-system is an independence system such that for any subset of ground set the ratio of the cardinality of the largest to the smallest maximal independent subset of it is at most mu. We further extend our results to the surrogate ratios for approximate solutions.

Given an edge-weighted undirected graph G = (V, E, w), the density of S subseteq V is defined as w(S)/|S|, where w(S) is the sum of weights of the edges in the subgraph induced by S. The densest subgraph problem asks for S subseteq V that maximizes the density w(S)/|S|. The problem has received significant attention recently because it can be solved exactly in polynomial time. However, the densest subgraph problem has a drawback; it may happen that the obtained subset is too large or too small in comparison with the desired size of the output. In this study, we address the size issue by generalizing the density of S subseteq V. Specifically, we introduce the f -density of S subseteq V, which is defined as w(S)/f (|S|), where f : Z geq 0 to R geq 0 is a monotonically non-decreasing function. In the f-densest subgraph problem (f-DS), we are asked to find S subseteq V that maximizes the f-density w(S)/f (|S|). Although f-DS does not explicitly specify the size of the output subset of vertices, we can handle the above size issue using a convex size function f or a concave size function f appropriately. For f-DS with convex function f, we propose a nearly-linear-time algorithm with a provable approximation guarantee. In particular, for f-DS with f(x) = x^alpha (alpha in [1, 2]), our algorithm has an approximation ratio of 2 · n^{(alpha-1)(2-alpha)}. On the other hand, for f-DS with concave function f , we propose a linear-programming-based polynomial-time exact algorithm. It should be emphasized that this algorithm obtains not only an optimal solution to the problem but also subsets of vertices corresponding to the extreme points of the upper convex hull of {(|S|, w(S)) | S subseteq V }, which we refer to as the dense frontier points. We also propose a flow-based combinatorial exact algorithm for unweighted graphs that runs in O(n^3) time. Finally, we propose a nearly-linear-time 3-approximation algorithm.

In this study, we introduce a novel quality function for a network community, which we refer to as the communitude. The communitude has a strong statistical background. Specifically, it measures the Z-score of a subset of vertices S with respect to the fraction of the number of edges within the subgraph induced by S. Due to the null model of a random graph used in the definition, our quality function focuses not only on the inside of the subgraph but also on the cut edges, unlike some quality functions for extracting dense subgraphs. To evaluate the detection ability of our quality function, we address the communitude maximization problem and its variants for realistic scenarios. For the problems, we propose a two-phase heuristic algorithm together with some modified versions. In the first phase, it repeatedly removes the vertex with the smallest degree, and then obtains the subgraph with maximum communitude over the iterations. In the second phase, the algorithm improves the obtained solution using a simple local search heuristic. This algorithm runs in linear time when the number of iterations is fixed to a constant; thus, it is applicable to massive graphs. Computational experiments using both synthetic graphs and real-world networks demonstrate the validity and reliability of the proposed quality function and algorithms.

We consider a wireless sensor network localization problem, with range-free and anchor-free settings, i.e., each sensor can only detect which sensors are in the neighbor. We observe issues with existing algorithms that cause inaccurate localization, and propose a new decomposition-based algorithm for resolving these issues. The proposed algorithm consists of three parts: (1) decomposition of a sensor network into small networks that may have large overlap with other small networks by a randomized ball-decomposition algorithm; (2) localization of each network by MDS-MAP and physical simulation-based local refinement; (3) gluing of small networks by a divide-and-conquer algorithm. Intuitively, our algorithm finds a good localization because it finds almost optimal localization for each small graph, and moreover, it glues them together optimally. We conduct computational experiments in both synthetic and realistic setting. The proposed algorithm is more accurate, efficient, and memory-saving than existing algorithms. In fact, it accurately localizes 200,000 sensors on European region in 3 hours, whereas other existing algorithms scale only up to 10,000 sensors. Thus, the algorithm can handle problem sizes several dozen times as large as existing algorithms can.