Deterministic Time Algorithm Research Articles

Counting and Enumerating Optimum Cut Sets for Hypergraph k-Partitioning Problems for Fixed k

We consider the problem of enumerating optimal solutions for two hypergraph k-partitioning problems, namely, Hypergraph-k-Cut and Minmax-Hypergraph-k-Partition. The input in hypergraph k-partitioning problems is a hypergraph [Formula: see text] with positive hyperedge costs along with a fixed positive integer k. The goal is to find a partition of V into k nonempty parts [Formula: see text]—known as a k-partition—so as to minimize an objective of interest. (1) If the objective of interest is the maximum cut value of the parts, then the problem is known as Minmax-Hypergraph-k-Partition. A subset of hyperedges is a minmax-k-cut-set if it is the subset of hyperedges crossing an optimum k-partition for Minmax-Hypergraph-k-Partition. (2) If the objective of interest is the total cost of hyperedges crossing the k-partition, then the problem is known as Hypergraph-k-Cut. A subset of hyperedges is a min-k-cut-set if it is the subset of hyperedges crossing an optimum k-partition for Hypergraph-k-Cut. We give the first polynomial bound on the number of minmax-k-cut-sets and a polynomial-time algorithm to enumerate all of them in hypergraphs for every fixed k. Our technique is strong enough to also enable an [Formula: see text]-time deterministic algorithm to enumerate all min-k-cut-sets in hypergraphs, thus improving on the previously known [Formula: see text]-time deterministic algorithm, in which n is the number of vertices and p is the size of the hypergraph. The correctness analysis of our enumeration approach relies on a structural result that is a strong and unifying generalization of known structural results for Hypergraph-k-Cut and Minmax-Hypergraph-k-Partition. We believe that our structural result is likely to be of independent interest in the theory of hypergraphs (and graphs). Funding: All authors were supported by NSF AF 1814613 and 1907937.

On the Planar Two-Center Problem and Circular Hulls

Given a set S of n points in the Euclidean plane, the two-center problem is to find two congruent disks of smallest radius whose union covers all points of S. Previously, Eppstein (SODA’97) gave a randomized algorithm of $$O(n\log ^2\!n)$$ expected time and Chan (CGTA’99) presented a deterministic algorithm of $$O(n\log ^2\!n\log ^2\log n)$$ time. In this paper, we propose an $$O(n\log ^2\!n)$$ time deterministic algorithm, which improves Chan’s deterministic algorithm and matches the randomized bound of Eppstein. If S is in convex position, then we solve the problem in $$O(n\log n\log \log n)$$ deterministic time. Our results rely on new techniques for dynamically maintaining circular hulls under point insertions and deletions, which are of independent interest.

Improved Analysis of Highest-Degree Branching for Feedback Vertex Set

Recent empirical evaluations of exact algorithms for Feedback Vertex Set have demonstrated the efficiency of a highest-degree branching algorithm with a degree-based pruning. In this paper, we prove that this empirically fast algorithm runs in \(O(3.460^k n)\) time, where k is the solution size. This improves the previous best \(O(3.619^k n)\)-time deterministic algorithm obtained by Kociumaka and Pilipczuk (Inf Process Lett 114:556–560, 2014. https://doi.org/10.1016/j.ipl.2014.05.001).

Budget Feasible Mechanisms on Matroids

Motivated by many practical applications, in this paper we study budget feasible mechanisms with the goal of procuring an independent set of a matroid. More specifically, we are given a matroid {mathcal {M}}=(E,{mathcal {I}}). Each element of the ground set E is controlled by a selfish agent and the cost of the element is private information of the agent itself. A budget limited buyer has additive valuations over the elements of E. The goal is to design an incentive compatible budget feasible mechanism which procures an independent set of the matroid of largest possible value. We also consider the more general case of the pair {mathcal {M}}=(E,{mathcal {I}}) satisfying only the hereditary property. This includes matroids as well as matroid intersection. We show that, given a polynomial time deterministic algorithm that returns an alpha -approximation to the problem of finding a maximum-value independent set in {mathcal {M}}, there exists an individually rational, truthful and budget feasible mechanism which is (3alpha +1)-approximated and runs in polynomial time, thus yielding also a 4-approximation for the special case of matroids.

Near Optimal Parallel Algorithms for Dynamic DFS in Undirected Graphs

Depth first search (DFS) tree is a fundamental data structure for solving various graph problems. The classical algorithm [54] for building a DFS tree requires O ( m + n ) time for a given undirected graph G having n vertices and m edges. Recently, Baswana et al. [5] presented a simple algorithm for updating the DFS tree of an undirected graph after an edge/vertex update in Õ( n ) 1 time. However, their algorithm is strictly sequential. We present an algorithm achieving similar bounds that can be easily adopted to the parallel environment. In the parallel environment, a DFS tree can be computed from scratch in expected Õ(1) time [2] on an EREW PRAM, whereas the best deterministic algorithm takes Õ(√ n ) time [2, 27] on a CRCW PRAM. Our algorithm can be used to develop optimal time (to poly log n factors) deterministic parallel algorithms for maintaining fully dynamic DFS and fault tolerant DFS of an undirected graph. (1) Parallel Fully Dynamic DFS : Given an arbitrary online sequence of vertex or edge updates, we can maintain a DFS tree of an undirected graph in Õ(1) time per update using m processors on an EREW PRAM. (2) Parallel Fault tolerant DFS : An undirected graph can be preprocessed to build a data structure of size O ( m ), such that for any set of k updates (where k is constant) in the graph, a DFS tree of the updated graph can be computed in Õ(1) time using n processors on an EREW PRAM. For constant k , this is also work optimal (to poly log n factors). Moreover, our fully dynamic DFS algorithm provides, in a seamless manner, nearly optimal (to poly log n factors) algorithms for maintaining a DFS tree in the semi-streaming environment and a restricted distributed model. These are the first parallel, semi-streaming, and distributed algorithms for maintaining a DFS tree in the dynamic setting.

On the maximum weight minimal separator

Given an undirected and connected graph G=(V,E) and two vertices s,t∈V, a vertex subset S that separates s and t is called an s-t separator, and an s-t separator is called minimal if no proper subset of S separates s and t. Moreover, we say that a set S is a minimal separator of G if S is a minimal s-t separator for some s and t. In this paper, we consider finding a minimal (s-t) separator with maximum weight on a vertex-weighted graph. We first prove that these problems are NP-hard. On the other hand, we give an O⁎(twO(tw))-time deterministic algorithm based on tree decompositions where O⁎ is the order notation omitting the polynomial factor of n. Moreover, we improve the algorithm by using the Rank-Based approach and the running time is O⁎(38⋅2ω)tw. Finally, we give an O⁎(9tw⋅W2)-time randomized algorithm to determine whether there exists a minimal (s-t) separator where W is its weight and tw is the treewidth of G.

Optimal deterministic distributed algorithms for maximal independent set in geometric graphs

Finding a maximal independent set (MIS) in a graph is one of the fundamental problems in distributed computing. Researchers in this community are trying to close the time complexity gap of computing a MIS in a graph, way back from Luby’s [STOC’85] O(logn) time (randomized) algorithm and Linial’s [SICOMP’92] Ω(log∗n) lower bound result (n is the number of nodes of the graph). Since then, an extensive research has been done on this problem, however, most of the results are randomized and we are lack of efficient deterministic solution. In fact, no polylogarithmic (in n) time deterministic algorithm is known so far in general graphs — an open question raised by Linial in 1993 [Comb. Probab. Comput.’93]. In this paper, we study the MIS problem in geometric graphs, a class of graphs which has both theoretical and practical importance. We present O(1)-time deterministic algorithms (hence, optimal) for computing MIS in unit interval graphs, unit square graphs and unit disk graphs. The same idea applies to develop optimal MIS algorithm for higher dimensional unit balls and unit hypercubes. We further extend the MIS algorithms to compute approximate maximum independent set (MaxIS) in the above geometric graph classes. The theoretical results are corroborated through extensive experiments to show the effectiveness and efficiency of our algorithms.Our algorithms are fully decentralized, scalable, and robust. In general, nodes exchange only O(logn) bits message through an edge per communication. Moreover, being local, our algorithms are also suitable in failure model, self-stabilizing and appropriate for dynamic environment.

On expansion and contraction of DL-Lite knowledge bases

Knowledge bases (KBs) are not static entities: new information constantly appears and some of the previous knowledge becomes obsolete. In order to reflect this evolution of knowledge, KBs should be expanded with the new knowledge and contracted from the obsolete one. This problem is well-studied for propositional but much less for first-order KBs. In this work we investigate knowledge expansion and contraction for KBs expressed in DL-Lite, a family of description logics (DLs) that underlie the tractable fragment OWL2QL of the Web Ontology Language OWL2. We start with a novel knowledge evolution framework and natural postulates that evolution should respect, and compare our postulates to the well-established AGM postulates. We then review well-known model and formula-based approaches for expansion and contraction for propositional theories and show how they can be adapted to the case of DL-Lite. In particular, we show intrinsic limitations of model-based approaches: besides the fact that some of them do not respect the postulates we have established, they ignore the structural properties of KBs. This leads to undesired properties of evolution results: evolution of DL-Lite KBs cannot be captured in DL-Lite. Moreover, we show that well-known formula-based approaches are also not appropriate for DL-Lite expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in DL-Lite. Thus, we propose a novel formula-based approach that respects our principles and for which evolution is expressible in DL-Lite. For this approach we also propose polynomial time deterministic algorithms to compute evolution of DL-Lite KBs when evolution affects only factual data.

On the Expansion of Group-Based Lifts

A $k$-lift of an $n$-vertex base graph $G$ is a graph $H$ on $n\times k$ vertices, where each vertex $v$ of $G$ is replaced by $k$ vertices $v_1,\ldots,v_k$ and each edge $uv$ in $G$ is replaced by a matching representing a bijection $\pi_{uv}$ so that the edges of $H$ are of the form $(u_i,v_{\pi_{uv}(i)})$. Lifts have been investigated as a means to efficiently construct expanders. In this work, we study lifts obtained from groups and group actions. We derive the spectrum of such lifts via the representation theory principles of the underlying group. Our main results are 1. a uniform random lift by a cyclic group of order $k$ of any $n$-vertex $d$-regular base graph $G$, with the nontrivial eigenvalues of the adjacency matrix of $G$ bounded by $\lambda$ in magnitude, has the new nontrivial eigenvalues bounded by $\lambda+\mathcal{O}(\sqrt{d})$ in magnitude with probability $1-ke^{-\Omega(n/d^2)}$. The probability bounds as well as the dependency on $\lambda$ are almost optimal. As a special case, we obtain that there is a constant $c_1$ such that for every $k\leq 2^{c_1n/d^2}$, there exists a lift $H$ of every Ramanujan graph by a cyclic group of order $k$ such that $H$ is almost Ramanujan (nontrivial eigenvalues of the adjacency matrix at most $O(\sqrt{d})$ in magnitude). This result leads to a quasi-polynomial time deterministic algorithm to construct almost Ramanujan expanders; 2. there is a constant $c_2$ such that for every $k\geq 2^{c_2nd}$, there does not exist an abelian $k$-lift $H$ of any $n$-vertex $d$-regular base graph such that $H$ is almost Ramanujan. This can be viewed as an analogue of the well-known nonexpansion result for constant degree abelian Cayley graphs. Suppose $k_0$ is the order of the largest abelian group that produces expanding lifts. Our two results highlight lower and upper bounds on $k_0$ that are tight up to a factor of $d^3$ in the exponent, thus suggesting a threshold phenomenon.

On Expansion and Contraction of DL-Lite Knowledge Bases

Knowledge bases (KBs) are not static entities: new information constantly appears and some of the previous knowledge becomes obsolete. In order to reflect this evolution of knowledge, KBs should be expanded with the new knowledge and contracted from the obsolete one. This problem is well-studied for propositional but much less for first-order KBs. In this work we investigate knowledge expansion and contraction for KBs expressed in DL-Lite, a family of description logics (DLs) that underlie the tractable fragment OWL2 QL of the Web Ontology Language OWL2. We start with a novel knowledge evolution framework and natural postulates that evolution should respect, and compare our postulates to the well-established AGM postulates. We then review well-known model and formula-based approaches for expansion and contraction for propositional theories and show how they can be adapted to the case of DL-Lite. In particular, we show intrinsic limitations of model-based approaches: besides the fact that some of them do not respect the postulates we have established, they ignore the structural properties of KBs. This leads to undesired properties of evolution results: evolution of DL-Lite KBs cannot be captured in DL-Lite. Moreover, we show that well-known formula-based approaches are also not appropriate for DL-Lite expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in DL-Lite. Thus, we propose a novel formula-based approach that respects our principles and for which evolution is expressible in DL-Lite. For this approach we also propose polynomial time deterministic algorithms to compute evolution of DL-Lite KBs when evolution affects only factual data.

Deterministic Edge Connectivity in Near-Linear Time

We present a deterministic algorithm that computes the edge-connectivity of a graph in near-linear time. This is for a simple undirected unweighted graph G with n vertices and m edges. This is the first o ( mn ) time deterministic algorithm for the problem. Our algorithm is easily extended to find a concrete minimum edge-cut. In fact, we can construct the classic cactus representation of all minimum cuts in near-linear time. The previous fastest deterministic algorithm by Gabow from STOC '91 took Õ( m +λ 2 n ), where λ is the edge connectivity, but λ can be as big as n −1. Karger presented a randomized near-linear time Monte Carlo algorithm for the minimum cut problem at STOC’96, but the returned cut is only minimum with high probability. Our main technical contribution is a near-linear time algorithm that contracts vertex sets of a simple input graph G with minimum degree Δ, producing a multigraph Ḡ with Õ( m /Δ) edges, which preserves all minimum cuts of G with at least two vertices on each side. In our deterministic near-linear time algorithm, we will decompose the problem via low-conductance cuts found using PageRank a la Brin and Page (1998), as analyzed by Andersson, Chung, and Lang at FOCS’06. Normally, such algorithms for low-conductance cuts are randomized Monte Carlo algorithms, because they rely on guessing a good start vertex. However, in our case, we have so much structure that no guessing is needed.

On the Limitations of Greedy Mechanism Design for Truthful Combinatorial Auctions

We study mechanisms for the combinatorial auction (CA) problem, in which m objects are sold to rational agents and the goal is to maximize social welfare. Of particular interest is the special case of s -CAs, where agents are interested in sets of size at most s , for which a simple greedy algorithm obtains an s + 1 approximation, but no polynomial time deterministic truthful mechanism is known to attain an approximation ratio better than O ( m /√log m ). We view this not only as an extreme gap between the power of greedy auctions and truthful greedy auctions, but also as exemplifying the gap between the known power of truthful and non-truthful polynomial time deterministic algorithms. We associate the notion of greediness with a broad class of algorithms, known as priority algorithms, which encapsulate many natural auction methods. This motivates us to ask: how well can a truthful greedy algorithm approximate the optimal social welfare for CA problems? We show that no truthful greedy priority algorithm can obtain an approximation to the CA problem that is sublinear in m , even for s -CAs with s ≥ 2. Our inapproximations are independent of any time constraints on the mechanism and are purely a consequence of the greedy-type restriction. We conclude that any truthful combinatorial auction mechanism with a non-trivial approximation factor must fall outside the scope of many natural auction methods.

Improved Pseudo-polynomial Bound for the Value Problem and Optimal Strategy Synthesis in Mean Payoff Games

In this work we offer an $$O(|V|^2 |E|\, W)$$O(|V|2|E|W) pseudo-polynomial time deterministic algorithm for solving the Value Problem and Optimal Strategy Synthesis in Mean Payoff Games. This improves by a factor $$\log (|V|\, W)$$log(|V|W) the best previously known pseudo-polynomial time upper bound due to Brim et al. The improvement hinges on a suitable characterization of values, and a description of optimal positional strategies, in terms of reweighted Energy Games and Small Energy-Progress Measures.

The Discrete and Semicontinuous Fréchet Distance with Shortcuts via Approximate Distance Counting and Selection

The Fréchet distance is a well-studied similarity measure between curves. The discrete Fréchet distance is an analogous similarity measure, defined for two sequences of m and n points, where the points are usually sampled from input curves. We consider a variant, called the discrete Fréchet distance with shortcuts , which captures the similarity between (sampled) curves in the presence of outliers. When shortcuts are allowed only in one noise-containing curve, we give a randomized algorithm that runs in O (( m + n ) 6/5 + ε ) expected time, for any ε > 0. When shortcuts are allowed in both curves, we give an O (( m 2/3 n 2/3 + m + n )log 3 ( m + n ))-time deterministic algorithm. We also consider the semicontinuous Fréchet distance with one-sided shortcuts, where we have a sequence of m points and a polygonal curve of n edges, and shortcuts are allowed only in the sequence. We show that this problem can be solved in randomized expected time O (( m + n ) 2/3 m 2/3 n 1/3 log ( m + n )). Our techniques are novel and may find further applications. One of the main new technical results is: Given two sets of points A and B in the plane and an interval I , we develop an algorithm that decides whether the number of pairs ( x , y ) ∈ A × B whose distance dist( x , y ) is in I is less than some given threshold L . The running time of this algorithm decreases as L increases. In case there are more than L pairs of points whose distance is in I , we can get a small sample of pairs that contain a pair at approximate median distance (i.e., we can approximately “bisect” I ). We combine this procedure with additional ideas to search, with a small overhead, for the optimal one-sided Fréchet distance with shortcuts, using a very fast decision procedure. We also show how to apply this technique for approximating distance selection (with respect to rank), and a somewhat more involved variant of this technique is used in the solution of the semicontinuous Fréchet distance with one-sided shortcuts. In general, the new technique can be applied to optimization problems for which the decision procedure is very fast but standard techniques like parametric search makes the optimization algorithm substantially slower.

Boolean matrices with prescribed row/column sums and stable homogeneous polynomials: Combinatorial and algorithmic applications

We prove a new efficiently computable lower bound on the coefficients of stable homogeneous polynomials and present its algorithmic and combinatorial applications. Our main application is the first poly-time deterministic algorithm which approximates the partition functions associated with boolean matrices with prescribed row and column sums within simply exponential multiplicative factor. This new algorithm is a particular instance of new polynomial time deterministic algorithms related to the multiple partial differentiation of polynomials given by evaluation oracles.

Faster deterministic Feedback Vertex Set

We present a new deterministic algorithm for the Feedback Vertex Set problem parameterized by the solution size. Our algorithm runs in O⁎((2+ϕ)k) time, where ϕ<1.619 is the golden ratio, surpassing the previously fastest O⁎((1+22)k)-time deterministic algorithm due to Cao et al. (2010) [6]. In our development we follow the approach of Cao et al.; however, thanks to a new reduction rule, we obtain not only better dependency on the parameter in the running time, but also a solution with simple analysis and only a single branching rule.

Fast Approximation Algorithm for Restricted Euclidean Bottleneck Steiner Tree Problem

Bottleneck Steiner tree problem asks to find a Steiner tree for n terminals with at most k Steiner points such that the length of the longest edge in the tree is minimized. The problem has applications in the design of wireless communication networks. In this paper we study a restricted version of the bottleneck Steiner tree problem in the Euclidean plane which requires that only degree-2 Steiner points are possibly adjacent in the optimal solution. We first show that the problem is NP-hard and cannot be approximated within unless P=NP, and provide a fast polynomial time deterministic approximation algorithm with performance ratio .

Derandomizing the HSSW Algorithm for 3-SAT

We present a (full) derandomization of HSSW algorithm for 3-SAT, proposed by Hofmeister, Schoning, Schuler, and Watanabe (in STACS 2002, pp. 192–202, 2002). Thereby, we obtain an O(1.3303 n )-time deterministic algorithm for 3-SAT, which is currently fastest.

Deterministic Identity Testing of Depth-4 Multilinear Circuits with Bounded Top Fan-in

We give the first subexponential time deterministic polynomial identity testing algorithm for depth-4 multilinear circuits with a small top fan-in. More accurately, our algorithm works for depth-4 multilinear circuits with a plus gate at the top (also known as $\Sigma\Pi\Sigma\Pi$ circuits) and has a running time of $\exp(\mathrm{poly}(\log(n),\log(s),k))$ where $n$ is the number of variables, $s$ is the size of the circuit, and $k$ is the fan-in of the top gate. In particular, when the circuit is of polynomial (or quasi-polynomial) size, our algorithm runs in quasi-polynomial time. Prior to this work, sub-exponential time deterministic algorithms were known for depth-$3$ circuits with small top fan-in and for very restricted versions of depth-$4$ circuits. The main ingredient in our proof is a new structural theorem for multilinear $\Sigma\Pi\Sigma\Pi(k)$ circuits. Roughly, this theorem shows that any nonzero multilinear $\Sigma\Pi\Sigma\Pi(k)$ circuit contains an “embedded” nonzero multilinear $\Sigma\Pi\Sigma(k)$ circuit. Using ideas from previous works on identity testing of sums of read-once formulas and of depth-3 multilinear circuits, we are able to exploit this structure and obtain an identity testing algorithm for multilinear $\Sigma\Pi\Sigma\Pi(k)$ circuits.

A Two-Prover One-Round Game with Strong Soundness

We show that for any fixed prime $q \geq 5$ and constant $\zeta > 0$, it is NP-hard to distinguish whether a two-prover one-round game with $q^6$ possible answers has value at least $1-\zeta$ or at most $\frac{4}{q}$. The result is obtained by combining two techniques: (i) An Inner PCP based on the point versus subspace test for linear functions. The test is analyzed Fourier analytically. (ii) The Outer/Inner PCP composition that relies on a certain sub-code covering property for Hadamard codes. This is a new and essentially black-box method to translate a codeword test for Hadamard codes to a consistency test, leading to a full PCP construction. As an application, we show that unless NP has quasi-polynomial time deterministic algorithms, the Quadratic Programming Problem is inapproximable within factor $(\log n)^{1/6 - o(1)}$. A preliminary version of this paper appeared in the Proceedings of the 52nd IEEE FOCS, 2011.