382 research outputs found
On the complexity of probabilistic trials for hidden satisfiability problems
What is the minimum amount of information and time needed to solve 2SAT? When
the instance is known, it can be solved in polynomial time, but is this also
possible without knowing the instance? Bei, Chen and Zhang (STOC '13)
considered a model where the input is accessed by proposing possible
assignments to a special oracle. This oracle, on encountering some constraint
unsatisfied by the proposal, returns only the constraint index. It turns out
that, in this model, even 1SAT cannot be solved in polynomial time unless P=NP.
Hence, we consider a model in which the input is accessed by proposing
probability distributions over assignments to the variables. The oracle then
returns the index of the constraint that is most likely to be violated by this
distribution. We show that the information obtained this way is sufficient to
solve 1SAT in polynomial time, even when the clauses can be repeated. For 2SAT,
as long as there are no repeated clauses, in polynomial time we can even learn
an equivalent formula for the hidden instance and hence also solve it.
Furthermore, we extend these results to the quantum regime. We show that in
this setting 1QSAT can be solved in polynomial time up to constant precision,
and 2QSAT can be learnt in polynomial time up to inverse polynomial precision.Comment: 24 pages, 2 figures. To appear in the 41st International Symposium on
Mathematical Foundations of Computer Scienc
The 2CNF Boolean Formula Satisfiability Problem and the Linear Space Hypothesis
We aim at investigating the solvability/insolvability of nondeterministic
logarithmic-space (NL) decision, search, and optimization problems
parameterized by size parameters using simultaneously polynomial time and
sub-linear space on multi-tape deterministic Turing machines. We are
particularly focused on a special NL-complete problem, 2SAT---the 2CNF Boolean
formula satisfiability problem---parameterized by the number of Boolean
variables. It is shown that 2SAT with variables and clauses can be
solved simultaneously polynomial time and space for an absolute constant . This fact inspires us to
propose a new, practical working hypothesis, called the linear space hypothesis
(LSH), which states that 2SAT---a restricted variant of 2SAT in which each
variable of a given 2CNF formula appears at most 3 times in the form of
literals---cannot be solved simultaneously in polynomial time using strictly
"sub-linear" (i.e., for a certain constant
) space on all instances . An immediate consequence of
this working hypothesis is . Moreover, we use our
hypothesis as a plausible basis to lead to the insolvability of various NL
search problems as well as the nonapproximability of NL optimization problems.
For our investigation, since standard logarithmic-space reductions may no
longer preserve polynomial-time sub-linear-space complexity, we need to
introduce a new, practical notion of "short reduction." It turns out that,
parameterized with the number of variables, is
complete for a syntactically restricted version of NL, called Syntactic
NL, under such short reductions. This fact supports the legitimacy
of our working hypothesis.Comment: (A4, 10pt, 25 pages) This current article extends and corrects its
preliminary report in the Proc. of the 42nd International Symposium on
Mathematical Foundations of Computer Science (MFCS 2017), August 21-25, 2017,
Aalborg, Denmark, Leibniz International Proceedings in Informatics (LIPIcs),
Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik 2017, vol. 83, pp.
62:1-62:14, 201
Average-Case Complexity
We survey the average-case complexity of problems in NP.
We discuss various notions of good-on-average algorithms, and present
completeness results due to Impagliazzo and Levin. Such completeness results
establish the fact that if a certain specific (but somewhat artificial) NP
problem is easy-on-average with respect to the uniform distribution, then all
problems in NP are easy-on-average with respect to all samplable distributions.
Applying the theory to natural distributional problems remain an outstanding
open question. We review some natural distributional problems whose
average-case complexity is of particular interest and that do not yet fit into
this theory.
A major open question whether the existence of hard-on-average problems in NP
can be based on the PNP assumption or on related worst-case assumptions.
We review negative results showing that certain proof techniques cannot prove
such a result. While the relation between worst-case and average-case
complexity for general NP problems remains open, there has been progress in
understanding the relation between different ``degrees'' of average-case
complexity. We discuss some of these ``hardness amplification'' results
Optimizing One Million Variable NK Landscapes by Hybridizing Deterministic Recombination and Local Search
In gray-box optimization, the search algorithms have access to the variable interaction graph (VIG) of the optimization problem. For Mk Landscapes (and NK Landscapes) we can use the VIG to identify an improving solution in the Hamming neighborhood in constant time. In addition, using the VIG, deterministic Partition Crossover is able to explore an exponential number of solutions in a time that is linear in the size of the problem. Both methods have been used in isolation in previous search algorithms. We present two new gray-box algorithms that combine Partition Crossover with highly efficient local search. The best algorithms are able to locate the global optimum on Adjacent NK Landscape instances with one million variables. The algorithms are compared with a state-of-the-art algorithm for pseudo-Boolean optimization: Gray-Box Parameterless Population Pyramid. The results show that the best algorithm is always one combining Partition Crossover and highly efficient local search. But the results also illustrate that the best optimizer differs on Adjacent and Random NK Landscapes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Clustering of solutions in hard satisfiability problems
We study the structure of the solution space and behavior of local search
methods on random 3-SAT problems close to the SAT/UNSAT transition. Using the
overlap measure of similarity between different solutions found on the same
problem instance we show that the solution space is shrinking as a function of
alpha. We consider chains of satisfiability problems, where clauses are added
sequentially. In each such chain, the overlap distribution is first smooth, and
then develops a tiered structure, indicating that the solutions are found in
well separated clusters. On chains of not too large instances, all solutions
are eventually observed to be in only one small cluster before vanishing. This
condensation transition point is estimated to be alpha_c = 4.26. The transition
approximately obeys finite-size scaling with an apparent critical exponent of
about 1.7. We compare the solutions found by a local heuristic, ASAT, and the
Survey Propagation algorithm up to alpha_c.Comment: 8 pages, 9 figure
Ergodic Control and Polyhedral approaches to PageRank Optimization
We study a general class of PageRank optimization problems which consist in
finding an optimal outlink strategy for a web site subject to design
constraints. We consider both a continuous problem, in which one can choose the
intensity of a link, and a discrete one, in which in each page, there are
obligatory links, facultative links and forbidden links. We show that the
continuous problem, as well as its discrete variant when there are no
constraints coupling different pages, can both be modeled by constrained Markov
decision processes with ergodic reward, in which the webmaster determines the
transition probabilities of websurfers. Although the number of actions turns
out to be exponential, we show that an associated polytope of transition
measures has a concise representation, from which we deduce that the continuous
problem is solvable in polynomial time, and that the same is true for the
discrete problem when there are no coupling constraints. We also provide
efficient algorithms, adapted to very large networks. Then, we investigate the
qualitative features of optimal outlink strategies, and identify in particular
assumptions under which there exists a "master" page to which all controlled
pages should point. We report numerical results on fragments of the real web
graph.Comment: 39 page
The 2CNF Boolean Formula Satisfiability Problem and the Linear Space Hypothesis
We aim at investigating the solvability/insolvability of nondeterministic logarithmic-space (NL) decision, search, and optimization problems parameterized by size parameters using simultaneously polynomial time and sub-linear space on multi-tape deterministic Turing machines. We are particularly focused on a special NL-complete problem, 2SAT - the 2CNF Boolean formula satisfiability problem-parameterized by the number of Boolean variables. It is shown that 2SAT with n variables and m clauses can be solved simultaneously polynomial time and (n/2^{c sqrt{log(n)}}) polylog(m+n) space for an absolute constant c>0. This fact inspires us to propose a new, practical working hypothesis, called the linear space hypothesis (LSH), which states that 2SAT_3-a restricted variant of 2SAT in which each variable of a given 2CNF formula appears as literals in at most 3 clauses-cannot be solved simultaneously in polynomial time using strictly "sub-linear" (i.e., n^{epsilon} polylog(n) for a certain constant epsilon in (0,1)) space. An immediate consequence of this working hypothesis is L neq NL. Moreover, we use our hypothesis as a plausible basis to lead to the insolvability of various NL search problems as well as the nonapproximability of NL optimization problems.
For our investigation, since standard logarithmic-space reductions may no longer preserve polynomial-time sub-linear-space complexity, we need to introduce a new, practical notion of "short reduction." It turns out that overline{2SAT}_3 is complete for a restricted version of NL, called Syntactic NL or simply SNL, under such short reductions. This fact supports the legitimacy of our working hypothesis
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