Bitstrings can be permuted via permutations and compared via the lexicographic order. In this paper we study the complexity of finding a minimum of a bitstring via given permutations. As finding a global optimum is known to be NP-complete [László Babai and Eugene M. Luks, 1983], we study the local optima via the class PLS [David S. Johnson et al., 1988] and show hardness for PLS. Additionally, we show that even for one permutation the global optimization problem is NP-complete and give a formula that has these permutation as its symmetries. This answers an open question inspired from Kołodziejczyk and Thapen [Leszek Aleksander Kolodziejczyk and Neil Thapen, 2024] and stated at the SAT and interactions seminar in Dagstuhl.

Abstract The currently fastest known algorithm for k-SAT is PPSZ, named after its inventors (Paturi et al. in J ACM 52(3):337-364, 2005. http://dx.doi.org/10.1145/1066100.1066101 ). Analyzing its running time is much easier for input formulas with a unique satisfying assignment. In this paper, we achieve three goals. First, we simplify the analysis of Hertli (in 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science-FOCS 2011, Los Alamitos, 2011) for input formulas with multiple satisfying assignments. Second, we show a “lifting result”: if you improve PPSZ for k-CNF formulas with a unique satisfying assignment, you will immediately get a (weaker) improvement for general k-CNF formulas. In combination this with results by Hansen et al. (in Charikar and Cohen (ed) Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019) and Scheder (in 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021), who all prove improved time bounds for Unique-k-SAT, this gives improved bounds for general k-SAT. We also generalize our results to the domain of Constraint Satisfaction Problems, i.e., satisfiability with more than two truth values.

PPSZ, for long time the fastest known algorithm for$k$-SAT, works by going through the variables of the input formula in random order; each variable is then set randomly to 0 or 1, unless the correct value can be inferred by an efficiently implementable rule (like small-width resolution; or being implied by a small set of clauses). We show that PPSZ performs exponentially better than previously known, for all$k\geq 3$. For Unique-3-SAT we bound its running time by$O(1.306973^{n})$, which is somewhat better than the algorithm of Hansen, Kaplan, Zamir, and Zwick. All improvements are achieved without changing the original PPSZ. The core idea is to pretend that PPSZ does not process the variables in uniformly random order, but according to a carefully designed distribution. We write “pretend” since this can be done without any actual change to the algorithm.22The full version of this paper can be found under https://eccc.weizmann.ac.il/report/2021/069/

PPSZ is the fastest known algorithm for (d,k)-CSP problems, for most values of d and k. It goes through the variables in random order and sets each variable randomly to one of the d colors, excluding those colors that can be ruled out by looking at few constraints at a time. We propose and analyze a modification of PPSZ: whenever all but 2 colors can be ruled out for some variable, immediately set that variable randomly to one of the remaining colors. We show that our new "impatient PPSZ" outperforms PPSZ exponentially for all k and all d >= 3 on formulas with a unique satisfying assignment.

We construct k-CNFs with m variables on which the strong version of PPSZ k-SAT algorithm, which uses resolution of width bounded by O(√{log log m}), has success probability at most 2^{-(1-(1 + ε)2/k)m} for every ε > 0. Previously such a bound was known only for the weak PPSZ algorithm which exhaustively searches through small subformulas of the CNF to see if any of them forces the value of a given variable, and for strong PPSZ the best known previous upper bound was 2^{-(1-O(log(k)/k))m} (Pudlák et al., ICALP 2017).

Cryptogenography is a secret-leaking game in which one of n players is holding a secret to be leaked. The n players engage in communication as to (1) reveal the secret while (2) keeping the identity of the secret holder as obscure as possible. All communication is public, and no computational hardness assumptions are made, i.e., the setting is purely information theoretic. Brody, Jakobsen, Scheder, and Winkler [Joshua Brody et al., 2014] formally defined this problem, showed that it has an equivalent geometric characterization, and gave upper and lower bounds for the case in which the n players want to leak a single bit. Surprisingly, even the easiest case, where two players want to leak a secret consisting of a single bit, is not completely understood. Doerr and Künnemann [Benjamin Doerr and Marvin Künnemann, 2016] showed how to automatically search for good protocols using a computer, thus finding an improved protocol for the 1-bit two-player case. In this work, we show how the search for upper bounds (impossibility results) can be formulated as a Sum of Squares program. We implement this idea for the 1-bit two-player case and significantly improve the previous upper bound from 47/128 = 0.3671875 to 0.35183.

The currently fastest known algorithm for k-SAT is PPSZ named after its inventors Paturi, Pudlak, Saks, and Zane. Analyzing its running time is much easier for input formulas with a unique satisfying assignment. In this paper, we achieve three goals. First, we simplify Hertli's analysis for input formulas with multiple satisfying assignments. Second, we show a "translation result": if you improve PPSZ for k-CNF formulas with a unique satisfying assignment, you will immediately get a (weaker) improvement for general k-CNF formulas. Combining this with a result by Hertli from 2014, in which he gives an algorithm for Unique-3-SAT slightly beating PPSZ, we obtain an algorithm beating PPSZ for general 3-SAT, thus obtaining the so far best known worst-case bounds for 3-SAT.

We construct uniquely satisfiable $k$-CNF formulas that are hard for the algorithm PPSZ. Firstly, we construct graph-instances on which "weak PPSZ" has savings of at most $(2 + ε) / k$; the saving of an algorithm on an input formula with $n$ variables is the largest $γ$ such that the algorithm succeeds (i.e. finds a satisfying assignment) with probability at least $2^{ - (1 - γ) n}$. Since PPSZ (both weak and strong) is known to have savings of at least $\frac{π^2 + o(1)}{6k}$, this is optimal up to the constant factor. In particular, for $k=3$, our upper bound is $2^{0.333\dots n}$, which is fairly close to the lower bound $2^{0.386\dots n}$ of Hertli [SIAM J. Comput.'14]. We also construct instances based on linear systems over $\mathbb{F}_2$ for which strong PPSZ has savings of at most $O\left(\frac{\log(k)}{k}\right)$. This is only a $\log(k)$ factor away from the optimal bound. Our constructions improve previous savings upper bound of $O\left(\frac{\log^2(k)}{k}\right)$ due to Chen et al. [SODA'13].

We give new characterizations and lower bounds relating classes in the communication complexity polynomial hierarchy and circuit complexity to limited memory communication models. We introduce the notion of rectangle overlay complexity of a function f {0, 1}n × {0, 1}n→{0, 1}. This is a natural combinatorial complexity measure in terms of combinatorial rectangles in the communication matrix of f. Furthermore, we consider memory less and limited-memory communication models, originally introduced in Brody, Chen, Papakonstantinou, Song, and Sun with slightly different terminology. In these communication models there are two parameters of interest: The maximum message length s (which we think of as space) and the number of memory states w. Specifically, these are one-way protocols which proceed in rounds. In each round, Alice sends a message of at most s bits to Bob, receiving a message from Alice, Bob has to decide on the spot whether to output 0 or 1, or to continue the protocol. If he decides to continue, he immediately forgets Alice's message. In memory less protocols, no memory is transferred between different rounds (but Bob still has "space" to hold Alice's messages within each round). We can make Bob more powerful by giving him w memory states. He can change into a new state at the end of each round. We show that rectangle overlays completely characterize memory less protocols. Then, we go on to show several connections to the communication complexity polynomial hierarchy defined by Babai, Frankl and Simon in 1986. This hierarchy has recently regained attention because its connection to the algebrization barrier in complexity theory (Aaronson and Wigderson, 2009). We show that PNPccis completely characterized by memory less protocols with polylog(n) space (maximum message length), and thus it admits a purely combinatorial characterization in terms of rectangle overlays. If Bob has 3 memory states and Alice sends messages of length polylog(n), they can compute every level of Sigma_k in the communication complexity hierarchy (for constant k), and also every function in AC0. Furthermore, we show that with 5 memory states and messages of length polylog(n) they can compute exactly the functions in the communication class PSPACEcc. This gives the first meaningful characterization of PSPACEccin terms of space, originally defined in Babai, Frankl, and Simon without any notion of space. We also study equivalences and separations between our limited memory communication model and branching programs, and relations to circuit classes.

We consider the following cryptographic secret leaking problem. A group of players communicate with the goal of learning (and perhaps revealing) a secret held initially by one of them. Their conversation is monitored by a computationally unlimited eavesdropper, who wants to learn the identity of the secret-holder. Despite the unavailability of key, some protection can be provided to the identity of the secret-holder. We call the study of such communication problems, either from the group's or the eavesdropper's point of view, cryptogenography. We introduce a basic cryptogenography problem and show that two players can force the eavesdropper to missguess the origin of a secret bit with probability 1/3; we complement this with a hardness result showing that they cannot do better than than 3/8. We prove that larger numbers of players can do better than 0.5644, but no group of any size can achieve 0.75.

In 1998, Paturi, Pudlák, Saks, and Zane presented PPSZ, an elegant randomized algorithm for k-SAT. Fourteen years on, this algorithm is still the fastest known worst-case algorithm. They proved that its expected running time on k-CNF formulas with n variables is at most , where εk ∊ Ω(1/k). So far, no exponential lower bounds at all have been known. In this paper, we construct hard instances for PPSZ. That is, we construct satisfiable k-CNF formulas over n variables on which the expected running time is at least , for εk ∊ O(log2 k/k).

Schoening in 1999 presented a simple randomized algorithm for k-SAT with running time an * poly(n) for a = 2(k-1)/k. We give a deterministic version of this algorithm running in time an+o(n).

We call a CNF formula {\em linear} if any two clauses have at most one variable in common. We show that there exist unsatisfiable linear $k$-CNF formulas with at most $4k^24^k$ clauses, and on the other hand, any linear $k$-CNF formula with at most $\frac{4^k}{8e^2k^2}$ clauses is satisfiable. The upper bound uses probabilistic means, and we have no explicit construction coming even close to it. One reason for this is that unsatisfiable linear formulas exhibit a more complex structure than general (non-linear) formulas: First, any treelike resolution refutation of any unsatisfiable linear $k$-CNF formula has size at least $2^{2^{\frac{k}{2}-1}}$. This implies that small unsatisfiable linear $k$-CNF formulas are hard instances for Davis-Putnam style splitting algorithms. Second, if we require that the formula $F$ have a {\em strict} resolution tree, i.e. every clause of $F$ is used only once in the resolution tree, then we need at least $a^{a^{\iddots^a}}$ clauses, where $a \approx 2$ and the height of this tower is roughly $k$.