106 research outputs found
Approximation Resistant Predicates From Pairwise Independence
We study the approximability of predicates on variables from a domain
, and give a new sufficient condition for such predicates to be
approximation resistant under the Unique Games Conjecture. Specifically, we
show that a predicate is approximation resistant if there exists a balanced
pairwise independent distribution over whose support is contained in
the set of satisfying assignments to
Independent Set, Induced Matching, and Pricing: Connections and Tight (Subexponential Time) Approximation Hardnesses
We present a series of almost settled inapproximability results for three
fundamental problems. The first in our series is the subexponential-time
inapproximability of the maximum independent set problem, a question studied in
the area of parameterized complexity. The second is the hardness of
approximating the maximum induced matching problem on bounded-degree bipartite
graphs. The last in our series is the tight hardness of approximating the
k-hypergraph pricing problem, a fundamental problem arising from the area of
algorithmic game theory. In particular, assuming the Exponential Time
Hypothesis, our two main results are:
- For any r larger than some constant, any r-approximation algorithm for the
maximum independent set problem must run in at least
2^{n^{1-\epsilon}/r^{1+\epsilon}} time. This nearly matches the upper bound of
2^{n/r} (Cygan et al., 2008). It also improves some hardness results in the
domain of parameterized complexity (e.g., Escoffier et al., 2012 and Chitnis et
al., 2013)
- For any k larger than some constant, there is no polynomial time min
(k^{1-\epsilon}, n^{1/2-\epsilon})-approximation algorithm for the k-hypergraph
pricing problem, where n is the number of vertices in an input graph. This
almost matches the upper bound of min (O(k), \tilde O(\sqrt{n})) (by Balcan and
Blum, 2007 and an algorithm in this paper).
We note an interesting fact that, in contrast to n^{1/2-\epsilon} hardness
for polynomial-time algorithms, the k-hypergraph pricing problem admits
n^{\delta} approximation for any \delta >0 in quasi-polynomial time. This puts
this problem in a rare approximability class in which approximability
thresholds can be improved significantly by allowing algorithms to run in
quasi-polynomial time.Comment: The full version of FOCS 201
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
Sum of squares lower bounds for refuting any CSP
Let be a nontrivial -ary predicate. Consider a
random instance of the constraint satisfaction problem on
variables with constraints, each being applied to randomly
chosen literals. Provided the constraint density satisfies , such
an instance is unsatisfiable with high probability. The \emph{refutation}
problem is to efficiently find a proof of unsatisfiability.
We show that whenever the predicate supports a -\emph{wise uniform}
probability distribution on its satisfying assignments, the sum of squares
(SOS) algorithm of degree
(which runs in time ) \emph{cannot} refute a random instance of
. In particular, the polynomial-time SOS algorithm requires
constraints to refute random instances of
CSP when supports a -wise uniform distribution on its satisfying
assignments. Together with recent work of Lee et al. [LRS15], our result also
implies that \emph{any} polynomial-size semidefinite programming relaxation for
refutation requires at least constraints.
Our results (which also extend with no change to CSPs over larger alphabets)
subsume all previously known lower bounds for semialgebraic refutation of
random CSPs. For every constraint predicate~, they give a three-way hardness
tradeoff between the density of constraints, the SOS degree (hence running
time), and the strength of the refutation. By recent algorithmic results of
Allen et al. [AOW15] and Raghavendra et al. [RRS16], this full three-way
tradeoff is \emph{tight}, up to lower-order factors.Comment: 39 pages, 1 figur
Hardness of Graph Pricing through Generalized Max-Dicut
The Graph Pricing problem is among the fundamental problems whose
approximability is not well-understood. While there is a simple combinatorial
1/4-approximation algorithm, the best hardness result remains at 1/2 assuming
the Unique Games Conjecture (UGC). We show that it is NP-hard to approximate
within a factor better than 1/4 under the UGC, so that the simple combinatorial
algorithm might be the best possible. We also prove that for any , there exists such that the integrality gap of
-rounds of the Sherali-Adams hierarchy of linear programming for
Graph Pricing is at most 1/2 + .
This work is based on the effort to view the Graph Pricing problem as a
Constraint Satisfaction Problem (CSP) simpler than the standard and complicated
formulation. We propose the problem called Generalized Max-Dicut(), which
has a domain size for every . Generalized Max-Dicut(1) is
well-known Max-Dicut. There is an approximation-preserving reduction from
Generalized Max-Dicut on directed acyclic graphs (DAGs) to Graph Pricing, and
both our results are achieved through this reduction. Besides its connection to
Graph Pricing, the hardness of Generalized Max-Dicut is interesting in its own
right since in most arity two CSPs studied in the literature, SDP-based
algorithms perform better than LP-based or combinatorial algorithms --- for
this arity two CSP, a simple combinatorial algorithm does the best.Comment: 28 page
Реоптимізація проблем про узагальнену виконуваність з предикатами розмірності 2
Припустимо, що виконується унікальна ігрова гіпотеза (UGC). Тоді для реоптимізації Max Cut (при добавленні довільного ребра) існує поліноміальний пороговий (оптимальний) φ(αGW)-наближений алгоритм, де φ(αGW)=1/(2−αGW)≈0,891716, при цьому αGW≈0,878567 (константа Гоеманса–Уільямсона). Для реоптимізації Max 2-Sat (при добавленні довільної диз'юнкції) існує поліноміальний пороговий (оптимальний) φ(α^−LLZ)-наближений алгоритм, де φ(α^−LLZ)≈0,943544, при цьому α^−LLZ≈0,940166 (константа Левіна–Лівната–Звіка).Допустим, что выполняется уникальная игровая гипотеза (UGC). Тогда для реоптимизации Max Cut (при вставке произвольного ребра) существует полиномиальный пороговый (оптимальный) φ(αGW)-приближенный алгоритм, где φ(αGW)=1/(2−αGW)≈0,891716, при этом αGW≈0,878567 (константа Гоеманса–Уильямсона). Для реоптимизации Max 2-Sat (при вставке произвольной дизьюнкции) существует полиномиальный пороговый (оптимальный) φ(α^−LLZ)-приближенный алгоритм, где φ(α^−LLZ)≈0,943544, при этом α^−LLZ≈0,940166 (константа Левина–Ливната–Звика).Assume that the Unique Game Conjecture (UGC) is held. Then, for the reoptimization of Max Cut (if a new edge is inserted), a polynomial threshold (optimal) φ(αGW)-approximation algorithm exists, where φ(αGW)=1/(2−αGW)≈0.891716 and αGW≈0.878567 (the Goemans–Williamson constant). For the reoptimization of Max 2-Sat (if a new disjunction is inserted), a polynomial threshold (optimal) φ(α^−LLZ)-approximation algorithm exists, where φ(α^−LLZ)≈0.943544 and α^−LLZ≈0.940166 (the Levin–Livnat–Zwick constant)
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