Special Section on the 48th Annual ACM Symposium on Theory of Computing (STOC 2016)

This issue of SICOMP contains 14 specially selected papers from the 48th Annual ACM Symposium on Theory of Computing (STOC 2016), held June 18--June 21, 2016, in Cambridge, Massachusetts. The papers here were chosen to represent both the excellence and the broad range of the STOC program. The papers have been revised and extended by the authors and subjected to the standard thorough reviewing process of SICOMP. The program committee members were Alexandr Andoni, Sanjeev Arora, Allison Bishop, Avrim Blum, Keren Censor-Hillel, Timothy Chan, Chandra Chekuri, Jing Chen, Zeev Dvir, Fabrizio Grandoni, Parikshit Gopalan, Kasper Green Larsen, Huijia (Rachel) Lin, Konstantin Makarychev, Yishay Mansour (chair), Jakob Nordström, Debmalya Panigrahi, Prasad Raghavendra, Sofya Raskhodnikova, R Ravi, Mario Szegedy, Êva Tardos, Salil Vadhan, Avi Wigderson, and Ronald de Wolf. We briefly describe here the papers that appear in this special issue. In “Breaking the Logarithmic Barrier for Truthful Combinatorial Auctions with Submodular Bidders,” Shahar Dobzinski provides the first truthful mechanism for welfare maximization in combinatorial auctions with submodular bidders whose approximation ratio is O(log m). Previously the best ratio was O(. In “A Tight Space Bound for Consensus,” Leqi Zhu proves that every randomized wait-free (or obstruction-free) consensus protocol for n processes must use at least n-1 registers. Previously, this bound was known only in the anonymous setting, while for the general case only a n bound was known. In “Two-Source Dispersers for Polylogarithmic Entropy and Improved Ramsey Graphs,” Gil Cohen constructs a 2^(log^c-Ramsey graph for some universal constant c, a significant improvement in this direction. In the language of theoretical computer science, this resolves the problem of explicitly constructing dispersers for two n-bit sources with entropy polylog(n). Previously, such dispersers could only support entropy n). In “Algorithmic Bayesian Persuasion,” Shaddin Dughmi and Haifeng Xu examines Bayesian persuasion through a computational lens for the first time. When the payoff distributions are i.i.d. across actions, the authors provide a polynomial-time optimal solution and a “simple” (1-1/e)-approximation. For independent but nonidentical distributions, \P-hardness is proved. For the general case with a black-box sampling oracle, an FPTAS is provided and shown to be the best possible under the black-box model. In “A Deterministic Almost-Tight Distributed Algorithm for Approximating Single-Source Shortest Paths,” Monika Henzinger, Sebastian Krinninger, and Danupon Nanongkai present a deterministic (1 + o(1))-approximation algorithm for solving the single-source shortest paths problem on distributed weighted networks in O(n^1/2+o(1) + D^1+o(1)) rounds, where n is the number of nodes and D is the diameter of the network. This improves upon previous results in being deterministic and completing in less time or in obtaining a smaller approximation factor. Moreover, it is almost tight due to a known lower bound. In “Lift-and-Round to Improve Weighted Completion Time on Unrelated Machines,” Nikhil Bansal, Aravind Srinivasan, and Ola Svensson improve, by a small but fixed constant, the long-standing approximation factor of 3/2 for the problem of scheduling jobs on unrelated machines so as to minimize the sum of weighted completion times. In “A Duality-Based Unified Approach to Bayesian Mechanism Design,” Yang Cai, Nikhil Devanur, and Seth Matthew Weinberg provide a duality-based unified framework for designing simple and approximately optimal auctions. Using this framework, the authors prove that either a posted-price mechanism or the Vickrey--Clarke--Groves auction with per-bidder entry fees achieves a constant-factor of the optimal revenue achievable by a Bayesian Incentive Compatible mechanism whenever buyers are unit-demand or additive, unifying previous breakthroughs of Chawla et al. and Yao, and improving both approximation ratios. In “A (1+-Approximation for Makespan Scheduling with Precedence Constraints using LP Hierarchies,” Elaine Levey and Thomas Rothvoss consider the problem of scheduling n unit size jobs with a precedence order on m identical machines as to minimize the makespan. They prove that for any fixed epsilon and m, an LP-hierarchy lift of the time-indexed LP with a slightly super poly-logarithmic number of r = (^log rounds provides a (1 + -approximation. The previous best approximation algorithms for this problem guarantee a (2 - 7/(3m+1))-approximation in polynomial time for m and 4/3 for m=3. In “Bipartite Perfect Matching Is in Quasi-NC,” Stephen Fenner, Rohit Gurjar, and Thomas Thierauf show that the bipartite perfect matching problem is in quasi-NC^2. That is, it has uniform circuits of quasi-polynomial size n^O(, and O( n) depth. Previously, only an exponential upper bound was known on the size of such circuits with poly-logarithmic depth. In “Exponential Separation of Communication and External Information,” Anat Ganor, Gillat Kol, and Ran Raz prove the first gap, an exponential gap, between external information complexity and communication complexity of a communication task. Previously such a separation was known only for the internal information vs communication complexity. This result has implication to the question of compressing communication protocols to the amount of information they reveal about the inputs. In “Constant-Round Interactive Proofs for Delegating Computation,” Omer Reingold, Guy Rothblum, and Ron Rothblum design efficient, constant-round interactive proofs. They show that for any statement that can be evaluated in polynomial time and space S, there exists a constant-round interactive protocol where the prover has polynomial runtime and the verifier has a runtime of about n+S). Prior to this work, very little was known about the power of constant-round protocol. This result is a major step for the grand challenge of verifiable delegation of computation. In “Tight Bounds for Single-Pass Streaming Complexity of the Set Cover Problem,” Sepehr Assadi, Sanjeev Khanna, and Yang Li resolve the space complexity of single-pass streaming algorithms for approximating the classic set cover problem. For finding an alpha-approximate set cover (for any n)) using a single-pass streaming algorithm, they show that mn/ space is both sufficient and necessary (up to an O( factor), where m denotes number of the sets and n denotes size of the universe. They further study the problem of estimating the size of a minimum set cover (as opposed to finding the actual sets) and achieve an additional saving of a factor of alpha in the space complexity, which is also the best possible. In “A Polynomial Lower Bound for Testing Monotonicity,” Aleksandrs Belovs and Eric Blais show a polynomial lower bound on query complexity for adaptive testers of monotonicity of an n-variate Boolean function. Prior to this work, similar lower bounds were known only for the nonadaptive testers, and proving similar bounds for adaptive testers has been a major challenge. In “Algorithmic Stability for Adaptive Data Analysis,” Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman take a solid step forward in the area of adaptive data analysis by establishing a clean, tight connection between the notion of differential privacy (max-KL stability) and design of adaptive queries. This connection improves a number of bounds that were known prior to this paper, and generalizes to handle more “data analysis" settings. We thank the authors, the program committee members, and the reviewers for STOC 2016 for their hard work, and we especially thank the SICOMP reviewers for their work in evaluating submitted papers.