Wenceslas Fernandez de la Vega, Marek Karpinski

We give the first polynomial time approximability characterization

of dense weighted instances of MAX-CUT, and some other dense

weighted NP-hard problems in terms of their empirical weight

distributions. This gives also the first almost sharp

characterization of inapproximability of unweighted 0,1

MAX-BISECTION instances ...
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Piotr Berman, Marek Karpinski

Improved inaproximability results are given, including the

best up to date explicit approximation thresholds for bounded

occurence satisfiability problems, like MAX-2SAT and E2-LIN-2,

and problems in bounded degree graphs, like MIS, Node Cover

and MAX CUT. We prove also for the first time inapproximability

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Uriel Feige, Marek Karpinski, Michael Langberg

We analyze the addition of a simple local improvement step to various known

randomized approximation algorithms.

Let $\alpha \simeq 0.87856$ denote the best approximation ratio currently

known for the Max Cut problem on general graphs~\cite{GW95}.

We consider a semidefinite relaxation of the Max Cut problem,

round it using the ...
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Uriel Feige, Marek Karpinski, Michael Langberg

We design a $0.795$ approximation algorithm for the Max-Bisection problem

restricted to regular graphs. In the case of three regular graphs our

results imply an approximation ratio of $0.834$.

Marek Karpinski, Miroslaw Kowaluk, Andrzej Lingas

The max-bisection problem is to find a partition of the vertices of a

graph into two equal size subsets that maximizes the number of edges with

endpoints in both subsets.

We obtain new improved approximation ratios for the max-bisection problem on

the low degree $k$-regular graphs for ...
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Marek Karpinski

We present some of the recent results on computational complexity

of approximating bounded degree combinatorial optimization problems. In

particular, we present the best up to now known explicit nonapproximability

bounds on the very small degree optimization problems which are of

particular importance on the intermediate stages ...
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Piotr Berman, Marek Karpinski

This paper studies the existence of efficient (small size)

amplifiers for proving explicit inaproximability results for bounded degree

and bounded occurrence combinatorial optimization problems, and gives

an explicit construction for such amplifiers. We use this construction

also later to improve the currently best known approximation lower bounds

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Noga Alon, Wenceslas Fernandez de la Vega, Ravi Kannan, Marek Karpinski

We present a new efficient sampling method for approximating

r-dimensional Maximum Constraint Satisfaction Problems, MAX-rCSP, on

n variables up to an additive error \epsilon n^r.We prove a new

general paradigm in that it suffices, for a given set of constraints,

to pick a small uniformly random ...
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Wenceslas Fernandez de la Vega, Marek Karpinski

We prove that the subdense instances of MAX-CUT of average

degree Omega(n/logn) posses a polynomial time approximation scheme (PTAS).

We extend this result also to show that the instances of general 2-ary

maximum constraint satisfaction problems (MAX-CSP) of the same average

density have PTASs. Our results ...
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Amin Coja-Oghlan, Andreas Goerdt, AndrÃ© Lanka, Frank SchÃ¤dlich

Abstract. It is known that random k-SAT formulas with at least

(2^k*ln2)*n random clauses are unsatisfiable with high probability. This

result is simply obtained by bounding the expected number of satisfy-

ing assignments of a random k-SAT instance by an expression tending

to 0 when n, the number of variables ...
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Ryan Williams

We present a novel method for exactly solving (in fact, counting solutions to) general constraint satisfaction optimization with at most two variables per constraint (e.g. MAX-2-CSP and MIN-2-CSP), which gives the first exponential improvement over the trivial algorithm; more precisely, it is a constant factor improvement in the base of ... more >>>

Guy Kindler, Ryan O'Donnell, Subhash Khot, Elchanan Mossel

In this paper we show a reduction from the Unique Games problem to the problem of approximating MAX-CUT to within a factor of $\GW + \eps$, for all $\eps > 0$; here $\GW \approx .878567$ denotes the approximation ratio achieved by the Goemans-Williamson algorithm~\cite{GW95}. This implies that if the Unique ... more >>>

Wenceslas Fernandez de la Vega, Marek Karpinski

We prove existence of approximation schemes for instances of MAX-CUT with $\Omega(\frac{n^2}{\Delta})$ edges which work in $2^{O^\thicksim(\frac{\Delta}{\varepsilon^2})}n^{O(1)}$ time. This entails in particular existence of quasi-polynomial approximation schemes (QPTASs) for mildly sparse instances of MAX-CUT with $\Omega(\frac{n^2}{\operatorname{polylog} n})$ edges. The result depends on new sampling method for smoothed linear programs that ... more >>>

Wenceslas Fernandez de la Vega, Marek Karpinski

We give a simple proof for the sample complexity bound $O~(1/\epsilon^4)$ of absolute approximation of MAX-CUT. The proof depends on a new analysis method for linear programs (LPs) underlying MAX-CUT which could be also of independent interest.

more >>>Uriel Feige, Guy Kindler, Ryan O'Donnell

Motivated by the study of Parallel Repetition and also by the Unique

Games Conjecture, we investigate the value of the ``Odd Cycle Games''

under parallel repetition. Using tools from discrete harmonic

analysis, we show that after $d$ rounds on the cycle of length $m$,

the value of the game is ...
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Boaz Barak, Moritz Hardt, Thomas Holenstein, David Steurer

We study the question of whether the value of mathematical programs such as

linear and semidefinite programming hierarchies on a graph $G$, is preserved

when taking a small random subgraph $G'$ of $G$. We show that the value of the

Goemans-Williamson (1995) semidefinite program (SDP) for \maxcut of $G'$ is

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Ofer Grossman, Dana Moshkovitz

We present techniques for decreasing the error probability of randomized algorithms and for converting randomized algorithms to deterministic (non-uniform) algorithms. Unlike most existing techniques that involve repetition of the randomized algorithm, and hence a slowdown, our techniques produce algorithms with a similar run-time to the original randomized algorithms.

The ... more >>>