33 research outputs found
Hardness of Finding Independent Sets in 2-Colorable Hypergraphs and of Satisfiable CSPs
This work revisits the PCP Verifiers used in the works of Hastad [Has01],
Guruswami et al.[GHS02], Holmerin[Hol02] and Guruswami[Gur00] for satisfiable
Max-E3-SAT and Max-Ek-Set-Splitting, and independent set in 2-colorable
4-uniform hypergraphs. We provide simpler and more efficient PCP Verifiers to
prove the following improved hardness results: Assuming that NP\not\subseteq
DTIME(N^{O(loglog N)}),
There is no polynomial time algorithm that, given an n-vertex 2-colorable
4-uniform hypergraph, finds an independent set of n/(log n)^c vertices, for
some constant c > 0.
There is no polynomial time algorithm that satisfies 7/8 + 1/(log n)^c
fraction of the clauses of a satisfiable Max-E3-SAT instance of size n, for
some constant c > 0.
For any fixed k >= 4, there is no polynomial time algorithm that finds a
partition splitting (1 - 2^{-k+1}) + 1/(log n)^c fraction of the k-sets of a
satisfiable Max-Ek-Set-Splitting instance of size n, for some constant c > 0.
Our hardness factor for independent set in 2-colorable 4-uniform hypergraphs
is an exponential improvement over the previous results of Guruswami et
al.[GHS02] and Holmerin[Hol02]. Similarly, our inapproximability of (log
n)^{-c} beyond the random assignment threshold for Max-E3-SAT and
Max-Ek-Set-Splitting is an exponential improvement over the previous bounds
proved in [Has01], [Hol02] and [Gur00]. The PCP Verifiers used in our results
avoid the use of a variable bias parameter used in previous works, which leads
to the improved hardness thresholds in addition to simplifying the analysis
substantially. Apart from standard techniques from Fourier Analysis, for the
first mentioned result we use a mixing estimate of Markov Chains based on
uniform reverse hypercontractivity over general product spaces from the work of
Mossel et al.[MOS13].Comment: 23 Page
A PTAS for the Classical Ising Spin Glass Problem on the Chimera Graph Structure
We present a polynomial time approximation scheme (PTAS) for the minimum
value of the classical Ising Hamiltonian with linear terms on the Chimera graph
structure as defined in the recent work of McGeoch and Wang. The result follows
from a direct application of the techniques used by Bansal, Bravyi and Terhal
who gave a PTAS for the same problem on planar and, in particular, grid graphs.
We also show that on Chimera graphs, the trivial lower bound is within a
constant factor of the optimum.Comment: 6 pages, corrected PTAS running tim
On the hardness of learning intersections of two halfspaces
AbstractWe show that unless NP=RP, it is hard to (even) weakly PAC-learn intersection of two halfspaces in Rn using a hypothesis which is a function of up to ℓ halfspaces (linear threshold functions) for any integer ℓ. Specifically, we show that for every integer ℓ and an arbitrarily small constant ε>0, unless NP=RP, no polynomial time algorithm can distinguish whether there is an intersection of two halfspaces that correctly classifies a given set of labeled points in Rn, or whether any function of ℓ halfspaces can correctly classify at most 12+ε fraction of the points
On the hardness of learning sparse parities
This work investigates the hardness of computing sparse solutions to systems
of linear equations over F_2. Consider the k-EvenSet problem: given a
homogeneous system of linear equations over F_2 on n variables, decide if there
exists a nonzero solution of Hamming weight at most k (i.e. a k-sparse
solution). While there is a simple O(n^{k/2})-time algorithm for it,
establishing fixed parameter intractability for k-EvenSet has been a notorious
open problem. Towards this goal, we show that unless k-Clique can be solved in
n^{o(k)} time, k-EvenSet has no poly(n)2^{o(sqrt{k})} time algorithm and no
polynomial time algorithm when k = (log n)^{2+eta} for any eta > 0.
Our work also shows that the non-homogeneous generalization of the problem --
which we call k-VectorSum -- is W[1]-hard on instances where the number of
equations is O(k log n), improving on previous reductions which produced
Omega(n) equations. We also show that for any constant eps > 0, given a system
of O(exp(O(k))log n) linear equations, it is W[1]-hard to decide if there is a
k-sparse linear form satisfying all the equations or if every function on at
most k-variables (k-junta) satisfies at most (1/2 + eps)-fraction of the
equations. In the setting of computational learning, this shows hardness of
approximate non-proper learning of k-parities. In a similar vein, we use the
hardness of k-EvenSet to show that that for any constant d, unless k-Clique can
be solved in n^{o(k)} time there is no poly(m, n)2^{o(sqrt{k}) time algorithm
to decide whether a given set of m points in F_2^n satisfies: (i) there exists
a non-trivial k-sparse homogeneous linear form evaluating to 0 on all the
points, or (ii) any non-trivial degree d polynomial P supported on at most k
variables evaluates to zero on approx. Pr_{F_2^n}[P(z) = 0] fraction of the
points i.e., P is fooled by the set of points
Hardness of Bipartite Expansion
We study the natural problem of estimating the expansion of subsets of vertices on one side of a bipartite graph. More precisely, given a bipartite graph G(U,V,E) and a parameter beta, the goal is to find a subset V\u27 subseteq V containing beta fraction of the vertices of V which minimizes the size of N(V\u27), the neighborhood of V\u27. This problem, which we call Bipartite Expansion, is a special case of submodular minimization subject to a cardinality constraint, and is also related to other problems in graph partitioning and expansion. Previous to this work, there was no hardness of approximation known for Bipartite Expansion.
In this paper we show the following strong inapproximability for Bipartite Expansion: for any constants tau, gamma > 0
there is no algorithm which, given a constant beta > 0 and a bipartite graph G(U,V,E), runs in polynomial time and decides whether
- (YES case) There is a subset S^* subseteq V s.t. |S^*| >= beta*|V| satisfying |N(S^*)| <= gamma |U|, or
- (NO case) Any subset S subseteq V s.t. |S| >= tau*beta*|V| satisfies |N(S)| >= (1 - gamma)|U|, unless
NP subseteq intersect_{epsilon > 0}{DTIME}(2^{n^epsi;on}) i.e. NP has subexponential time algorithms.
We note that our hardness result stated above is a vertex expansion analogue of the Small Set (Edge) Expansion Conjecture of
Raghavendra and Steurer 2010
Hardness of Rainbow Coloring Hypergraphs
A hypergraph is k-rainbow colorable if there exists a vertex coloring using k colors such that each hyperedge has all the k colors. Unlike usual hypergraph coloring, rainbow coloring becomes harder as the number of colors increases. This work studies the rainbow colorability of hypergraphs which are guaranteed to be nearly balanced rainbow colorable. Specifically, we show that for any Q,k >= 2 and ell <= k/2, given a Qk-uniform hypergraph which admits a k-rainbow coloring satisfying:
- in each hyperedge e, for some ell_e <= ell all but 2ell_e colors occur exactly Q times and the rest (Q +/- 1) times,
it is NP-hard to compute an independent set of (1 - (ell+1)/k + eps)-fraction of vertices, for any constant eps > 0. In particular, this implies the hardness of even (k/ell)-rainbow coloring such hypergraphs.
The result is based on a novel long code PCP test that ensures the strong balancedness property desired of the k-rainbow coloring in the completeness case. The soundness analysis relies on a mixing bound based on uniform reverse hypercontractivity due to Mossel, Oleszkiewicz, and Sen, which was also used in earlier proofs of the hardness of omega(1)-coloring 2-colorable 4-uniform hypergraphs due to Saket, and k-rainbow colorable 2k-uniform hypergraphs due to Guruswami and Lee
Approximation Algorithms for Stochastic k-TSP
This paper studies the stochastic variant of the classical k-TSP problem where rewards at the vertices are independent random variables which are instantiated upon the tour\u27s visit. The objective is to minimize the expected length of a tour that collects reward at least k. The solution is a policy describing the tour which may (adaptive) or may not (non-adaptive) depend on the observed rewards.
Our work presents an adaptive O(log k)-approximation algorithm for Stochastic k-TSP, along with a non-adaptive O(log^2 k)-approximation algorithm which also upper bounds the adaptivity gap by O(log^2 k). We also show that the adaptivity gap of Stochastic k-TSP is at least e, even in the special case of stochastic knapsack cover