201,972 research outputs found
Polar Polytopes and Recovery of Sparse Representations
Suppose we have a signal y which we wish to represent using a linear
combination of a number of basis atoms a_i, y=sum_i x_i a_i = Ax. The problem
of finding the minimum L0 norm representation for y is a hard problem. The
Basis Pursuit (BP) approach proposes to find the minimum L1 norm representation
instead, which corresponds to a linear program (LP) that can be solved using
modern LP techniques, and several recent authors have given conditions for the
BP (minimum L1 norm) and sparse (minimum L0 solutions) representations to be
identical. In this paper, we explore this sparse representation problem} using
the geometry of convex polytopes, as recently introduced into the field by
Donoho. By considering the dual LP we find that the so-called polar polytope P
of the centrally-symmetric polytope P whose vertices are the atom pairs +-a_i
is particularly helpful in providing us with geometrical insight into
optimality conditions given by Fuchs and Tropp for non-unit-norm atom sets. In
exploring this geometry we are able to tighten some of these earlier results,
showing for example that the Fuchs condition is both necessary and sufficient
for L1-unique-optimality, and that there are situations where Orthogonal
Matching Pursuit (OMP) can eventually find all L1-unique-optimal solutions with
m nonzeros even if ERC fails for m, if allowed to run for more than m steps
Oriented Spanners
Given a point set in the Euclidean plane and a parameter , we define
an \emph{oriented -spanner} as an oriented subgraph of the complete
bi-directed graph such that for every pair of points, the shortest cycle in
through those points is at most a factor longer than the shortest oriented
cycle in the complete bi-directed graph. We investigate the problem of
computing sparse graphs with small oriented dilation.
As we can show that minimising oriented dilation for a given number of edges
is NP-hard in the plane, we first consider one-dimensional point sets. While
obtaining a -spanner in this setting is straightforward, already for five
points such a spanner has no plane embedding with the leftmost and rightmost
point on the outer face.
This leads to restricting to oriented graphs with a one-page book embedding
on the one-dimensional point set. For this case we present a dynamic program to
compute the graph of minimum oriented dilation that runs in time for
points, and a greedy algorithm that computes a -spanner in
time.
Expanding these results finally gives us a result for two-dimensional point
sets: we prove that for convex point sets the greedy triangulation results in
an oriented -spanner.Comment: conference version: ESA '2
Oriented Spanners
Given a point set P in the Euclidean plane and a parameter t, we define an oriented t-spanner as an oriented subgraph of the complete bi-directed graph such that for every pair of points, the shortest cycle in G through those points is at most a factor t longer than the shortest oriented cycle in the complete bi-directed graph. We investigate the problem of computing sparse graphs with small oriented dilation.
As we can show that minimising oriented dilation for a given number of edges is NP-hard in the plane, we first consider one-dimensional point sets. While obtaining a 1-spanner in this setting is straightforward, already for five points such a spanner has no plane embedding with the leftmost and rightmost point on the outer face. This leads to restricting to oriented graphs with a one-page book embedding on the one-dimensional point set. For this case we present a dynamic program to compute the graph of minimum oriented dilation that runs in ?(n?) time for n points, and a greedy algorithm that computes a 5-spanner in ?(nlog n) time.
Expanding these results finally gives us a result for two-dimensional point sets: we prove that for convex point sets the greedy triangulation results in an oriented ?(1)-spanner
Projection Methods in Sparse and Low Rank Feasibility
In this thesis, we give an analysis of fixed point algorithms involving projections onto closed, not necessarily convex, subsets of finite dimensional vector
spaces.
These methods are used in applications such as imaging science, signal processing, and inverse problems. The tools used in the analysis
place this work at the intersection of optimization and variational analysis. Based on the underlying optimization problems, this work is devided into two main parts. The first one is the compressed sensing problem. Because the problem
is NP-hard, we relax it to a feasibility problem with two sets, namely,
the set of vectors with at most s nonzero entries and, for a linear mapping M
the affine subspace B of vectors satisfying Mx=p for p given.
This problem will be referred to as the sparse-affine-feasibility problem. For the Douglas-Rachford algorithm, we give the proof of linear convergence to a fixed point in the case of a feasibility problem of two affine subspaces.
It allows us to conclude a result of local linear convergence of the Douglas-Rachford algorithm in the sparse affine feasibility problem.
Proceeding, we name sufficient conditions for the alternating projections algorithm to converge to the intersection of an affine subspace with lower level sets
of point symmetric, lower semicontinuous, subadditive functions.
This implies convergence of alternating projections to a solution of the sparse affine feasibility problem.
Together with a result of local linear convergence of the alternating projections algorithm, this allows us to deduce linear convergence after finitely many steps
for any initial point of a sequence of points generated by the alternating projections algorithm. The second part of this dissertation deals with the minimization of the rank of matrices satisfying a set of linear equations.
This problem will be called rank-constrained-affine-feasibility problem.
The motivation for the analysis of the rank minimization problem comes from the physical application of phase retrieval and a reformulation of the same as a
rank minimization problem. We show that, locally, the method of alternating projections must converge at linear rate to a solution of the rank
constrained affine feasibility problem
An Atypical Survey of Typical-Case Heuristic Algorithms
Heuristic approaches often do so well that they seem to pretty much always
give the right answer. How close can heuristic algorithms get to always giving
the right answer, without inducing seismic complexity-theoretic consequences?
This article first discusses how a series of results by Berman, Buhrman,
Hartmanis, Homer, Longpr\'{e}, Ogiwara, Sch\"{o}ening, and Watanabe, from the
early 1970s through the early 1990s, explicitly or implicitly limited how well
heuristic algorithms can do on NP-hard problems. In particular, many desirable
levels of heuristic success cannot be obtained unless severe, highly unlikely
complexity class collapses occur. Second, we survey work initiated by Goldreich
and Wigderson, who showed how under plausible assumptions deterministic
heuristics for randomized computation can achieve a very high frequency of
correctness. Finally, we consider formal ways in which theory can help explain
the effectiveness of heuristics that solve NP-hard problems in practice.Comment: This article is currently scheduled to appear in the December 2012
issue of SIGACT New
Counting Value Sets: Algorithm and Complexity
Let be a prime. Given a polynomial in \F_{p^m}[x] of degree over
the finite field \F_{p^m}, one can view it as a map from \F_{p^m} to
\F_{p^m}, and examine the image of this map, also known as the value set. In
this paper, we present the first non-trivial algorithm and the first complexity
result on computing the cardinality of this value set. We show an elementary
connection between this cardinality and the number of points on a family of
varieties in affine space. We then apply Lauder and Wan's -adic
point-counting algorithm to count these points, resulting in a non-trivial
algorithm for calculating the cardinality of the value set. The running time of
our algorithm is . In particular, this is a polynomial time
algorithm for fixed if is reasonably small. We also show that the
problem is #P-hard when the polynomial is given in a sparse representation,
, and is allowed to vary, or when the polynomial is given as a
straight-line program, and is allowed to vary. Additionally, we prove
that it is NP-hard to decide whether a polynomial represented by a
straight-line program has a root in a prime-order finite field, thus resolving
an open problem proposed by Kaltofen and Koiran in
\cite{Kaltofen03,KaltofenKo05}
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