30 research outputs found
The algorithm by Ferson et al. is surprisingly fast: An NP-hard optimization problem solvable in almost linear time with high probability
We start with the algorithm of Ferson et al. (\emph{Reliable computing} {\bf
11}(3), p.~207--233, 2005), designed for solving a certain NP-hard problem
motivated by robust statistics.
First, we propose an efficient implementation of the algorithm and improve
its complexity bound to , where is the
clique number in a certain intersection graph. Then we treat input data as
random variables (as it is usual in statistics) and introduce a natural
probabilistic data generating model. On average, we get and . This results in
average computing time for arbitrarily
small, which may be considered as ``surprisingly good'' average time complexity
for solving an NP-hard problem. Moreover, we prove the following tail bound on
the distribution of computation time: ``hard'' instances, forcing the algorithm
to compute in time , occur rarely, with probability tending to
zero faster than exponentially with
Parameterized complexity of DPLL search procedures
We study the performance of DPLL algorithms on parameterized problems. In particular, we investigate how difficult it is to decide whether small solutions exist for satisfiability and other combinatorial problems. For this purpose we develop a Prover-Delayer game which models the running time of DPLL procedures and we establish an information-theoretic method to obtain lower bounds to the running time of parameterized DPLL procedures. We illustrate this technique by showing lower bounds to the parameterized pigeonhole principle and to the ordering principle. As our main application we study the DPLL procedure for the problem of deciding whether a graph has a small clique. We show that proving the absence of a k-clique requires n steps for a non-trivial distribution of graphs close to the critical threshold. For the restricted case of tree-like Parameterized Resolution, this result answers a question asked in [11] of understanding the Resolution complexity of this family of formulas
On monotone circuits with local oracles and clique lower bounds
We investigate monotone circuits with local oracles [K., 2016], i.e.,
circuits containing additional inputs that can perform
unstructured computations on the input string . Let be
the locality of the circuit, a parameter that bounds the combined strength of
the oracle functions , and
be the set of -cliques and the set of complete -partite graphs,
respectively (similarly to [Razborov, 1985]). Our results can be informally
stated as follows.
1. For an appropriate extension of depth- monotone circuits with local
oracles, we show that the size of the smallest circuits separating
(triangles) and (complete bipartite graphs) undergoes two phase
transitions according to .
2. For , arbitrary depth, and , we
prove that the monotone circuit size complexity of separating the sets
and is , under a certain restrictive
assumption on the local oracle gates.
The second result, which concerns monotone circuits with restricted oracles,
extends and provides a matching upper bound for the exponential lower bounds on
the monotone circuit size complexity of -clique obtained by Alon and Boppana
(1987).Comment: Updated acknowledgements and funding informatio
On the Average-case Complexity of Parameterized Clique
The k-Clique problem is a fundamental combinatorial problem that plays a
prominent role in classical as well as in parameterized complexity theory. It
is among the most well-known NP-complete and W[1]-complete problems. Moreover,
its average-case complexity analysis has created a long thread of research
already since the 1970s. Here, we continue this line of research by studying
the dependence of the average-case complexity of the k-Clique problem on the
parameter k. To this end, we define two natural parameterized analogs of
efficient average-case algorithms. We then show that k-Clique admits both
analogues for Erd\H{o}s-R\'{e}nyi random graphs of arbitrary density. We also
show that k-Clique is unlikely to admit neither of these analogs for some
specific computable input distribution
DNF Sparsification and a Faster Deterministic Counting Algorithm
Given a DNF formula on n variables, the two natural size measures are the
number of terms or size s(f), and the maximum width of a term w(f). It is
folklore that short DNF formulas can be made narrow. We prove a converse,
showing that narrow formulas can be sparsified. More precisely, any width w DNF
irrespective of its size can be -approximated by a width DNF with
at most terms.
We combine our sparsification result with the work of Luby and Velikovic to
give a faster deterministic algorithm for approximately counting the number of
satisfying solutions to a DNF. Given a formula on n variables with poly(n)
terms, we give a deterministic time algorithm
that computes an additive approximation to the fraction of
satisfying assignments of f for \epsilon = 1/\poly(\log n). The previous best
result due to Luby and Velickovic from nearly two decades ago had a run-time of
.Comment: To appear in the IEEE Conference on Computational Complexity, 201
Improved bounds for the sunflower lemma
A sunflower with petals is a collection of sets so that the
intersection of each pair is equal to the intersection of all. Erd\H{o}s and
Rado proved the sunflower lemma: for any fixed , any family of sets of size
, with at least about sets, must contain a sunflower. The famous
sunflower conjecture is that the bound on the number of sets can be improved to
for some constant . In this paper, we improve the bound to about
. In fact, we prove the result for a robust notion of sunflowers,
for which the bound we obtain is tight up to lower order terms.Comment: Revised preprint, added sections on applications and rainbow
sunflower
From DNF Compression to Sunflower Theorems via Regularity
The sunflower conjecture is one of the most well-known open problems in combinatorics. It has several applications in theoretical computer science, one of which is DNF compression, due to Gopalan, Meka and Reingold (Computational Complexity, 2013). In this paper, we show that improved bounds for DNF compression imply improved bounds for the sunflower conjecture, which is the reverse direction of the DNF compression result. The main approach is based on regularity of set systems and a structure-vs-pseudorandomness approach to the sunflower conjecture
Beating Treewidth for Average-Case Subgraph Isomorphism
For any fixed graph G, the subgraph isomorphism problem asks whether an n-vertex input graph has a subgraph isomorphic to G. A well-known algorithm of Alon, Yuster and Zwick (1995) efficiently reduces this to the "colored" version of the problem, denoted G-SUB, and then solves G-SUB in time O(n^{tw(G)+1}) where tw(G) is the treewidth of G. Marx (2010) conjectured that G-SUB requires time Omega(n^{const * tw(G)}) and, assuming the Exponential Time Hypothesis, proved a lower bound of Omega(n^{const * emb(G)}) for a certain graph parameter emb(G) = Omega(tw(G)/log tw(G)). With respect to the size of AC^0 circuits solving G-SUB, Li, Razborov and Rossman (2017) proved an unconditional average-case lower bound of Omega(n^{kappa(G)}) for a different graph parameter kappa(G) = Omega(tw(G)/log tw(G)).
Our contributions are as follows. First, we show that emb(G) is at most O(kappa(G)) for all graphs G. Next, we show that kappa(G) can be asymptotically less than tw(G); for example, if G is a hypercube then kappa(G) is Theta(tw(G)/sqrt{log tw(G)}). Finally, we construct AC^0 circuits of size O(n^{kappa(G)+const}) that solve G-SUB in the average case, on a variety of product distributions. This improves an O(n^{2 kappa(G)+const}) upper bound of Li et al., and shows that the average-case complexity of G-SUB is n^{o(tw(G))} for certain families of graphs G such as hypercubes