956 research outputs found
Subsampling Mathematical Relaxations and Average-case Complexity
We initiate a study of when the value of mathematical relaxations such as
linear and semidefinite programs for constraint satisfaction problems (CSPs) is
approximately preserved when restricting the instance to a sub-instance induced
by a small random subsample of the variables. Let be a family of CSPs such
as 3SAT, Max-Cut, etc., and let be a relaxation for , in the sense
that for every instance , is an upper bound the maximum
fraction of satisfiable constraints of . Loosely speaking, we say that
subsampling holds for and if for every sufficiently dense instance and every , if we let be the instance obtained by
restricting to a sufficiently large constant number of variables, then
. We say that weak subsampling holds if the
above guarantee is replaced with whenever
. We show: 1. Subsampling holds for the BasicLP and BasicSDP
programs. BasicSDP is a variant of the relaxation considered by Raghavendra
(2008), who showed it gives an optimal approximation factor for every CSP under
the unique games conjecture. BasicLP is the linear programming analog of
BasicSDP. 2. For tighter versions of BasicSDP obtained by adding additional
constraints from the Lasserre hierarchy, weak subsampling holds for CSPs of
unique games type. 3. There are non-unique CSPs for which even weak subsampling
fails for the above tighter semidefinite programs. Also there are unique CSPs
for which subsampling fails for the Sherali-Adams linear programming hierarchy.
As a corollary of our weak subsampling for strong semidefinite programs, we
obtain a polynomial-time algorithm to certify that random geometric graphs (of
the type considered by Feige and Schechtman, 2002) of max-cut value
have a cut value at most .Comment: Includes several more general results that subsume the previous
version of the paper
A Birthday Repetition Theorem and Complexity of Approximating Dense CSPs
A -birthday repetition of a
two-prover game is a game in which the two provers are sent
random sets of questions from of sizes and respectively.
These two sets are sampled independently uniformly among all sets of questions
of those particular sizes. We prove the following birthday repetition theorem:
when satisfies some mild conditions, decreases exponentially in where is the total number of
questions. Our result positively resolves an open question posted by Aaronson,
Impagliazzo and Moshkovitz (CCC 2014).
As an application of our birthday repetition theorem, we obtain new
fine-grained hardness of approximation results for dense CSPs. Specifically, we
establish a tight trade-off between running time and approximation ratio for
dense CSPs by showing conditional lower bounds, integrality gaps and
approximation algorithms. In particular, for any sufficiently large and for
every , we show the following results:
- We exhibit an -approximation algorithm for dense Max -CSPs
with alphabet size via -level of Sherali-Adams relaxation.
- Through our birthday repetition theorem, we obtain an integrality gap of
for -level Lasserre relaxation for fully-dense Max
-CSP.
- Assuming that there is a constant such that Max 3SAT cannot
be approximated to within of the optimal in sub-exponential
time, our birthday repetition theorem implies that any algorithm that
approximates fully-dense Max -CSP to within a factor takes
time, almost tightly matching the algorithmic
result based on Sherali-Adams relaxation.Comment: 45 page
Global Cardinality Constraints Make Approximating Some Max-2-CSPs Harder
Assuming the Unique Games Conjecture, we show that existing approximation algorithms for some Boolean Max-2-CSPs with cardinality constraints are optimal. In particular, we prove that Max-Cut with cardinality constraints is UG-hard to approximate within ~~0.858, and that Max-2-Sat with cardinality constraints is UG-hard to approximate within ~~0.929. In both cases, the previous best hardness results were the same as the hardness of the corresponding unconstrained Max-2-CSP (~~0.878 for Max-Cut, and ~~0.940 for Max-2-Sat).
The hardness for Max-2-Sat applies to monotone Max-2-Sat instances, meaning that we also obtain tight inapproximability for the Max-k-Vertex-Cover problem
Sketching Cuts in Graphs and Hypergraphs
Sketching and streaming algorithms are in the forefront of current research
directions for cut problems in graphs. In the streaming model, we show that
-approximation for Max-Cut must use space;
moreover, beating -approximation requires polynomial space. For the
sketching model, we show that -uniform hypergraphs admit a
-cut-sparsifier (i.e., a weighted subhypergraph that
approximately preserves all the cuts) with
edges. We also make first steps towards sketching general CSPs (Constraint
Satisfaction Problems)
Approximating Dense Max 2-CSPs
In this paper, we present a polynomial-time algorithm that approximates
sufficiently high-value Max 2-CSPs on sufficiently dense graphs to within
approximation ratio for any constant .
Using this algorithm, we also achieve similar results for free games,
projection games on sufficiently dense random graphs, and the Densest
-Subgraph problem with sufficiently dense optimal solution. Note, however,
that algorithms with similar guarantees to the last algorithm were in fact
discovered prior to our work by Feige et al. and Suzuki and Tokuyama.
In addition, our idea for the above algorithms yields the following
by-product: a quasi-polynomial time approximation scheme (QPTAS) for
satisfiable dense Max 2-CSPs with better running time than the known
algorithms
Optimal Constant-Time Approximation Algorithms and (Unconditional) Inapproximability Results for Every Bounded-Degree CSP
Raghavendra (STOC 2008) gave an elegant and surprising result: if Khot's
Unique Games Conjecture (STOC 2002) is true, then for every constraint
satisfaction problem (CSP), the best approximation ratio is attained by a
certain simple semidefinite programming and a rounding scheme for it. In this
paper, we show that similar results hold for constant-time approximation
algorithms in the bounded-degree model. Specifically, we present the
followings: (i) For every CSP, we construct an oracle that serves an access, in
constant time, to a nearly optimal solution to a basic LP relaxation of the
CSP. (ii) Using the oracle, we give a constant-time rounding scheme that
achieves an approximation ratio coincident with the integrality gap of the
basic LP. (iii) Finally, we give a generic conversion from integrality gaps of
basic LPs to hardness results. All of those results are \textit{unconditional}.
Therefore, for every bounded-degree CSP, we give the best constant-time
approximation algorithm among all. A CSP instance is called -far from
satisfiability if we must remove at least an -fraction of constraints
to make it satisfiable. A CSP is called testable if there is a constant-time
algorithm that distinguishes satisfiable instances from -far
instances with probability at least . Using the results above, we also
derive, under a technical assumption, an equivalent condition under which a CSP
is testable in the bounded-degree model
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