Recent work on “quiet planting” in combinatorial optimization aims to generate instances with a hidden solution that is hard to recover, typically by making the planted distribution statistically indistinguishable from uniform for specific algorithms, such as statistical queries. A prominent example is planted $k$-SAT, where $O(n^{k/2})$ clauses can be planted while maintaining indistinguishability from uniform instances, evidenced by prior hardness results which also align with findings in SAT refutation. Despite extensive research and practical use in benchmarking SAT solvers, the challenge of quietly planting multiple solutions while preserving hardness has remained an open problem. This work initiates the study of quiet planting with an arbitrary number of solutions, proposing the first method to construct quiet planting distributions for $k$-SAT formulas that accommodate more than one solution. We provide statistical query lower bounds for distinguishing these planted instances from uniform ones, and our method allows for planting solutions with arbitrary geometric relationships, including varying Hamming distances. A key innovation facilitating multiple solutions is the ability to incorporate arbitrary correlations between variable selection in clauses and their negation patterns, departing from prior approaches. We also investigate the worst-case complexity of SAT by showing the difficulty in distinguishing satisfiable instances with numerous solutions from unsatisfiable ones, addressing an open problem of Hsieh, Mohanty, and Xu (CCC’22). From a technical standpoint, we generalize the concept of $(r-1)$-wise uniformness in clause distributions, proving hardness holds if the marginal distribution over negation patterns is $(r-1)$-wise uniform, and reveal a connection to binary linear codes, demonstrating how a $[k, t, r]$ code can guide the planting of up to $2^t - 1$ solutions on $k$ variables with $(r-1)$-wise uniform negation distributions.

Load balancing is among the basic primitives in distributed computing. In this paper, we consider this problem when executed locally on a network with nodes prone to failures. We show that there exist lightweight network topologies that are immune to message delivery failures incurred by (at most) a constant fraction of all nodes. More precisely, we design a novel deterministic fault-tolerant local load balancing (LLB) algorithm, which, similarly to their classical counterparts working in fault-free networks, has a relatively simple structure and guarantees exponentially fast convergence to the average value despite crash and omission failures.

One of the classic problems in online decision-making is the secretary problem, where the goal is to hire the best secretary out of n rankable applicants or, in a natural extension, to maximize the probability of selecting the largest number from a sequence arriving in random order. Many works have considered generalizations of this problem where one can accept multiple values subject to a combinatorial constraint. The seminal work of Babaioff, Immorlica, Kempe, and Kleinberg (SODA'07, JACM'18) proposed the matroid secretary conjecture, suggesting that there exists an O(1)-competitive algorithm for the matroid constraint, and many works since have attempted to obtain algorithms for both general matroids and specific classes of matroids. The ultimate goal of these results is to obtain an e-competitive algorithm, and the strong matroid secretary conjecture states that this is possible for general matroids. One of the most important classes of matroids is the graphic matroid, where a set of edges in a graph is deemed independent if it contains no cycle. Given the rich combinatorial structure of graphs, obtaining algorithms for these matroids is often seen as a good first step towards solving the problem for general matroids. For matroid secretary, Babaioff et al. (SODA'07, JACM'18) first studied graphic matroid case and obtained a 16-competitive algorithm. Subsequent works have improved the competitive ratio, most recently to 4 by Soto, Turkieltaub, and Verdugo (SODA'18). In this paper, we break the 4-competitive barrier for the problem, obtaining a new algorithm with a competitive ratio of 3.95. For the special case of simple graphs (i.e., graphs that do not contain parallel edges) we further improve this to 3.77. Intuitively, solving the problem for simple graphs is easier as they do not contain cycles of length two. A natural question that arises is whether we can obtain a ratio arbitrarily close to e by assuming the graph has a large enough girth. We answer this question affirmatively, proving that one can obtain a competitive ratio arbitrarily close to e even for constant values of girth, providing further evidence for the strong matroid secretary conjecture. We further show that this bound is tight: for any constant g, one cannot obtain a competitive ratio better than e even if we assume that the input graph has girth at least g. To our knowledge, such a bound was not previously known even for simple graphs.

Fault-tolerant Consensus is about reaching agreement on some of the input values in a limited time by non-faulty autonomous processes, despite of failures of processes or communication medium. This problem is particularly challenging and costly against an adaptive adversary with full information. Bar-Joseph and Ben-Or (PODC'98) were the first who proved an absolute lower bound Ω(√{n/log n}) on expected time complexity of Consensus in any classical (i.e., randomized or deterministic) message-passing network with n processes succeeding with probability 1 against such a strong adaptive adversary crashing processes. Seminal work of Ben-Or and Hassidim (STOC'05) broke the Ω(√{n/log n}) barrier for consensus in the classical (deterministic and randomized) networks by enhancing the model with quantum channels. In such networks, quantum communication between every pair of processes participating in the protocol is also allowed. They showed an (expected) constant-time quantum algorithm for a linear number of crashes t < n/3. In this paper, we improve upon that seminal work by reducing the number of quantum and communication bits to an arbitrarily small polynomial, and even more, to a polylogarithmic number - though, the latter in the cost of a slightly larger polylogarithmic time (still exponentially smaller than the time lower bound Ω(√{n/log n}) for the classical computation models).

We give the first non-trivial decremental dynamic embedding of a weighted, undirected graph $G$ into $\ell_p$ space. Given a weighted graph $G$ undergoing a sequence of edge weight increases, the goal of this problem is to maintain a (randomized) mapping $\phi: (G,d) \to (X,\ell_p)$ from the set of vertices of the graph to the $\ell_p$ space such that for every pair of vertices $u$ and $v$, the expected distance between $\phi(u)$ and $\phi(v)$ in the $\ell_p$ metric is within a small multiplicative factor, referred to as the distortion, of their distance in $G$. Our main result is a dynamic algorithm with expected distortion $O(\log^2 n)$ and total update time $O\left((m^{1+o(1)} \log^2 W + Q)\log(nW) \right)$, where $W$ is the maximum weight of the edges, $Q$ is the total number of updates and $n, m$ denote the number of vertices and edges in $G$ respectively. This is the first result of its kind, extending the seminal result of Bourgain ’85 to the expanding field of dynamic algorithms. Moreover, we demonstrate that in the fully dynamic regime, where we tolerate edge insertions as well as deletions, no algorithm can explicitly maintain an embedding into $\ell_p$ space that has a low distortion with high probability.

We study the complexity of the problem of reaching agreement in a synchronous distributed system, also called consensus, by n autonomous parties, when the communication links from/to faulty parties can omit messages. The faulty parties are selected and controlled by an adaptive, full-information, computationally unbounded adversary. We design a randomized algorithm that works in [EQUATION] rounds and sends O(n2 log3 n) total number of communication bits, where the number of faulty parties can be Θ(n). When the number of faulty parties is linear in n, our result is simultaneously tight for both these measures within polylogarithmic factors: due to the Ω(n2) lower bound on the number of messages send by any Monte Carlo solution, by Abraham et al. (PODC'19), and due to the [EQUATION] lower bound on the number of rounds of any Las Vegas solution, by Bar-Joseph and Ben-Or (PODC'98). Thereby, this work settles the landscape of the consensus problem in the omission failures model, which stood as an open question since the work of Dolev and Strong (SICOMP'83).

We study the power of posted pricing mechanisms for Bayesian online optimization problems subject to combinatorial feasibility constraints. When the objective is to maximize social welfare, the problem is widely studied in the literature on prophet inequalities. While most (though not all) existing algorithms for prophet inequalities are implemented using a pricing mechanism, whether or not this can be done in general is unknown, and was formally left as an open question by Dutting, Feldman, Kesselheim, and Lucier (FOCS 2017, SICOMP 2020). Understanding the power and limitations of posted prices is important from a mechanism design perspective because any posted price mechanism is truthful, and is also interesting in its own right as it can guide future research on prophet inequalities.

We develop deterministic algorithms for the problems of consensus, gossiping and checkpointing with nodes prone to failing. Distributed systems are modeled as synchronous complete networks. Failures are represented either as crashes or Byzantine faults with authentication. The algorithmic goal is to have both linear running time and linear amount of communication for as large an upper bound t on the number of faults as possible, with respect to the number of nodes n. For crash failures, these bounds of optimality are t = O(n / (log n)) for consensus and t = O(n / (log2 n)) for gossiping and checkpointing. For the model of Byzantine faults with authentication, we show how to reach consensus in both linear running time and communication for t = O(√n). We show how to implement the consensus algorithm for crash failures in the single-port model such as to preserve the range of t for which both the running time and communication are optimal. We prove a lower bound Ω (t+log n) on the running time of algorithms for each of the considered problems.

Fault-tolerant consensus is about reaching agreement on some of the input values in a limited time by non-faulty autonomous processes, despite of failures of processes or communication medium. This problem is particularly challenging and costly against an adaptive adversary with full information. Bar-Joseph and Ben-Or [7] (PODC'98) were the first who proved an absolute lower bound [EQUATION] on expected time complexity of consensus in any classic (i.e., randomized or deterministic) message-passing network with n processes succeeding with probability 1 against such a strong adaptive adversary crashing processes.

In this work we initiate the study of buy-and-sell prophet inequalities. We start by considering what is arguably the most fundamental setting. In this setting the online algorithm observes a sequence of prices one after the other. At each time step, the online algorithm can decide to buy and pay the current price if it does not hold the item already; or it can decide to sell and collect the current price as a reward if it holds the item.

We study consensus and checkpointing in synchronous distributed systems. There are n nodes that communicate by sending messages, and any two nodes can communicate directly. The nodes are prone to crashing, with an upper bound t on the number of crashes. Algorithms use overlay networks of choice to save on the amount of communication. We explore using Ramanujan graphs as such overlay networks. We demonstrate that Ramanujan graphs have topological properties conducive to fault-tolerance and time/communication efficiency of distributed algorithms. Our consensus algorithm assumes binary input values, runs in O(t) time and sends O(n+t log t) bits. The algorithm sends the optimum number O(n) of bits for t=O(n/log n), thus for this range of t it improves on the algorithm by Galil, Mayer and Yung [FOCS 1995] that also sends O(n) bits but works in exponential time. The consensus algorithm can be implemented such that a node sends a message to at most one node at a round while maintaining the asymptotic time and communication performance bounds. Our checkpointing algorithm runs in linear time O(n) and with O(n log7 n) messages. It improves on the most communication-efficient and time-optimal algorithm by Galil, Mayer and Yung [FOCS 1995], which may have O(n1+ε) messages sent, for any chosen constant ε>0.

We study the Weighted Min Cut problem in the Adaptive Massively Parallel Computation (AMPC) model. In 2019, Behnezhad et al. [3] introduced the AMPC model as an extension of the Massively Parallel Computation (MPC) model. In the past decade, research on highly scalable algorithms has had significant impact on many massive systems. The MPC model, introduced in 2010 by Karloff et al. [16], which is an abstraction of famous practical frameworks such as MapReduce, Hadoop, Flume, and Spark, has been at the forefront of this research. While great strides have been taken to create highly efficient MPC algorithms for a range of problems, recent progress has been limited by the 1-vs-2 Cycle Conjecture [20], which postulates that the simple problem of distinguishing between one and two cycles requires Ω(log n) MPC rounds. In the AMPC model, each machine has adaptive read access to a distributed hash table even when communication is restricted (i.e., in the middle of a round). While remaining practical [4], this gives algorithms the power to bypass limitations like the 1-vs-2 Cycle Conjecture.

Consensus is one of the most thoroughly studied problems in distributed computing, yet there are still complexity gaps that have not been bridged for decades. In particular, in the classical message-passing setting with processes’ crashes, since the seminal works of Bar-Joseph and Ben-Or [PODC 1998] and Aspnes and Waarts [SICOMP 1996, JACM 1998] in the previous century, there is still a fundamental unresolved question about communication complexity of fast randomized Consensus against a (strong) adaptive adversary crashing processes arbitrarily online. The best known upper bound on the number of communication bits is Θ(n3/2/√logn) per process, while the best lower bound is Ω(1). This is in contrast to randomized Consensus against a (weak) oblivious adversary, for which time-almost-optimal algorithms guarantee amortized O(1) communication bits per process. We design an algorithm against adaptive adversary that reduces the communication gap by nearly linear factor to O(√n· n) bits per process, while keeping almost-optimal (up to factor O(log3 n)) time complexity O(√n·log5/2 n).

We study Consensus in synchronous networks with arbitrary connected topologies. Nodes may be faulty, in the sense of either Byzantine or proneness to crashing. Let t denote a known upper bound on the number of faulty nodes, and D_s denote a maximum diameter of a network obtained by removing up to s nodes, assuming the network is (s+1)-connected. We give an algorithm for Consensus running in time t + D_{2t} with nodes subject to Byzantine faults. We show that, for any algorithm solving Consensus for Byzantine nodes, there is a network G and an execution of the algorithm on this network that takes Ω(t + D_{2t}) rounds. We give an algorithm solving Consensus in t + D_{t} communication rounds with Byzantine nodes using authenticated messages of polynomial size. We show that for any numbers t and d > 4, there exists a network G and an algorithm solving Consensus with Byzantine nodes using authenticated messages in fewer than t + 3 rounds on G, but all algorithms solving Consensus without message authentication require at least t + d rounds on G. This separates Consensus with Byzantine nodes from Consensus with Byzantine nodes using message authentication, with respect to asymptotic time performance in networks of arbitrary connected topologies, which is unlike complete networks. Let f denote the number of failures actually occurring in an execution and unknown to the nodes. We develop an algorithm solving Consensus against crash failures and running in time 𝒪(f + D_{f}), assuming only that nodes know their names and can differentiate among ports; this algorithm is also communication-efficient, by using messages of size 𝒪(mlog n), where n is the number of nodes and m is the number of edges. We give a lower bound t+D_t-2 on the running time of any deterministic solution to Consensus in (t+1)-connected networks, if t nodes may crash.