266 research outputs found
ETH Tight Algorithms for Geometric Intersection Graphs: Now in Polynomial Space
De Berg et al. in [SICOMP 2020] gave an algorithmic framework for subexponential algorithms on geometric graphs with tight (up to ETH) running times. This framework is based on dynamic programming on graphs of weighted treewidth resulting in algorithms that use super-polynomial space. We introduce the notion of weighted treedepth and use it to refine the framework of de Berg et al. for obtaining polynomial space (with tight running times) on geometric graphs. As a result, we prove that for any fixed dimension d ≥ 2 on intersection graphs of similarly-sized fat objects many well-known graph problems including Independent Set, r-Dominating Set for constant r, Cycle Cover, Hamiltonian Cycle, Hamiltonian Path, Steiner Tree, Connected Vertex Cover, Feedback Vertex Set, and (Connected) Odd Cycle Transversal are solvable in time 2^(n^{1-1/d}) and within polynomial space.publishedVersio
Quantum algorithm for a generalized hidden shift problem
Consider the following generalized hidden shift problem:
given a function f on {0,...,M − 1} × ZN promised to be
injective for fixed b and satisfying f(b, x) = f(b + 1, x + s)
for b = 0, 1,...,M − 2, find the unknown shift s ∈ ZN.
For M = N, this problem is an instance of the abelian
hidden subgroup problem, which can be solved efficiently on
a quantum computer, whereas for M = 2, it is equivalent
to the dihedral hidden subgroup problem, for which no
efficient algorithm is known. For any fixed positive �, we give
an efficient (i.e., poly(logN)) quantum algorithm for this
problem provided M ≥ N^∈. The algorithm is based on the
“pretty good measurement” and uses H. Lenstra’s (classical)
algorithm for integer programming as a subroutine
The power of sum-of-squares for detecting hidden structures
We study planted problems---finding hidden structures in random noisy
inputs---through the lens of the sum-of-squares semidefinite programming
hierarchy (SoS). This family of powerful semidefinite programs has recently
yielded many new algorithms for planted problems, often achieving the best
known polynomial-time guarantees in terms of accuracy of recovered solutions
and robustness to noise. One theme in recent work is the design of spectral
algorithms which match the guarantees of SoS algorithms for planted problems.
Classical spectral algorithms are often unable to accomplish this: the twist in
these new spectral algorithms is the use of spectral structure of matrices
whose entries are low-degree polynomials of the input variables. We prove that
for a wide class of planted problems, including refuting random constraint
satisfaction problems, tensor and sparse PCA, densest-k-subgraph, community
detection in stochastic block models, planted clique, and others, eigenvalues
of degree-d matrix polynomials are as powerful as SoS semidefinite programs of
roughly degree d. For such problems it is therefore always possible to match
the guarantees of SoS without solving a large semidefinite program. Using
related ideas on SoS algorithms and low-degree matrix polynomials (and inspired
by recent work on SoS and the planted clique problem by Barak et al.), we prove
new nearly-tight SoS lower bounds for the tensor and sparse principal component
analysis problems. Our lower bounds for sparse principal component analysis are
the first to suggest that going beyond existing algorithms for this problem may
require sub-exponential time
Efficient Exact Algorithms on Planar Graphs: Exploiting Sphere Cut Decompositions
A divide-and-conquer strategy based on variations of the Lipton-Tarjan planar separator
theorem has been one of the most common approaches for solving planar graph problems for
more than 20 years. We present a new framework for designing fast subexponential exact and parameterized algorithms on planar graphs. Our approach is based on geometric properties of planar branch decompositions obtained by Seymour & Thomas, combined with refined techniques of dynamic programming on planar graphs based on properties of non-crossing partitions. Compared to divide-and-conquer algorithms, the main advantages of our method are a) it is a generic method which allows to attack broad classes of problems; b) the obtained algorithms provide a better worst case analysis. To exemplify our approach we show how to obtain an O(26.903√n) time algorithm solving weighted HAMILTONIAN CYCLE. We observe how our technique can be used to solve PLANAR GRAPH TSP in time O(29.8594√n). Our approach can be used to design parameterized algorithms as well. For example we introduce the first 2O(√ k)nO(1) time algorithm for parameterized Planar k-cycle by showing that for a given k we can decide if a planar graph on n vertices has a cycle of length at least k in time O(213.6√kn + n3)
A low-memory algorithm for finding short product representations in finite groups
We describe a space-efficient algorithm for solving a generalization of the
subset sum problem in a finite group G, using a Pollard-rho approach. Given an
element z and a sequence of elements S, our algorithm attempts to find a
subsequence of S whose product in G is equal to z. For a random sequence S of
length d log_2 n, where n=#G and d >= 2 is a constant, we find that its
expected running time is O(sqrt(n) log n) group operations (we give a rigorous
proof for d > 4), and it only needs to store O(1) group elements. We consider
applications to class groups of imaginary quadratic fields, and to finding
isogenies between elliptic curves over a finite field.Comment: 12 page
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