1,839 research outputs found
Optimal Nonergodic Sublinear Convergence Rate of Proximal Point Algorithm for Maximal Monotone Inclusion Problems
We establish the optimal nonergodic sublinear convergence rate of the
proximal point algorithm for maximal monotone inclusion problems. First, the
optimal bound is formulated by the performance estimation framework, resulting
in an infinite dimensional nonconvex optimization problem, which is then
equivalently reformulated as a finite dimensional semidefinite programming
(SDP) problem. By constructing a feasible solution to the dual SDP, we obtain
an upper bound on the optimal nonergodic sublinear rate. Finally, an example in
two dimensional space is constructed to provide a lower bound on the optimal
nonergodic sublinear rate. Since the lower bound provided by the example
matches exactly the upper bound obtained by the dual SDP, we have thus
established the worst case nonergodic sublinear convergence rate which is
optimal in terms of both the order as well as the constants involved. Our
result sharpens the understanding of the fundamental proximal point algorithm.Comment: 15 pages, 2 figure
On the optimal linear convergence factor of the relaxed proximal point algorithm for monotone inclusion problems
Finding a zero of a maximal monotone operator is fundamental in convex
optimization and monotone operator theory, and \emph{proximal point algorithm}
(PPA) is a primary method for solving this problem. PPA converges not only
globally under fairly mild conditions but also asymptotically at a fast linear
rate provided that the underlying inverse operator is Lipschitz continuous at
the origin. These nice convergence properties are preserved by a relaxed
variant of PPA. Recently, a linear convergence bound was established in [M.
Tao, and X. M. Yuan, J. Sci. Comput., 74 (2018), pp. 826-850] for the relaxed
PPA, and it was shown that the bound is optimal when the relaxation factor
lies in . However, for other choices of , the bound
obtained by Tao and Yuan is suboptimal. In this paper, we establish tight
linear convergence bounds for any choice of and make the whole
picture about optimal linear convergence bounds clear. These results sharpen
our understandings to the asymptotic behavior of the relaxed PPA.Comment: 9 pages and 1 figur
Applications of gauge duality in robust principal component analysis and semidefinite programming
Gauge duality theory was originated by Freund [Math. Programming,
38(1):47-67, 1987] and was recently further investigated by Friedlander,
Mac{\^e}do and Pong [SIAM J. Optm., 24(4):1999-2022, 2014]. When solving some
matrix optimization problems via gauge dual, one is usually able to avoid full
matrix decompositions such as singular value and/or eigenvalue decompositions.
In such an approach, a gauge dual problem is solved in the first stage, and
then an optimal solution to the primal problem can be recovered from the dual
optimal solution obtained in the first stage. Recently, this theory has been
applied to a class of \emph{semidefinite programming} (SDP) problems with
promising numerical results [Friedlander and Mac{\^e}do, SIAM J. Sci. Comp., to
appear, 2016]. In this paper, we establish some theoretical results on applying
the gauge duality theory to robust \emph{principal component analysis} (PCA)
and general SDP. For each problem, we present its gauge dual problem,
characterize the optimality conditions for the primal-dual gauge pair, and
validate a way to recover a primal optimal solution from a dual one. These
results are extensions of [Friedlander and Mac{\^e}do, SIAM J. Sci. Comp., to
appear, 2016] from nuclear norm regularization to robust PCA and from a special
class of SDP which requires the coefficient matrix in the linear objective to
be positive definite to SDP problems without this restriction. Our results
provide further understanding in the potential advantages and disadvantages of
the gauge duality theory.Comment: Accepted for publication in SCIENCE CHINA Mathematic
Alternating Direction Algorithms for -Problems in Compressive Sensing
In this paper, we propose and study the use of alternating direction
algorithms for several -norm minimization problems arising from sparse
solution recovery in compressive sensing, including the basis pursuit problem,
the basis-pursuit denoising problems of both unconstrained and constrained
forms, as well as others. We present and investigate two classes of algorithms
derived from either the primal or the dual forms of the -problems. The
construction of the algorithms consists of two main steps: (1) to reformulate
an -problem into one having partially separable objective functions by
adding new variables and constraints; and (2) to apply an exact or inexact
alternating direction method to the resulting problem. The derived alternating
direction algorithms can be regarded as first-order primal-dual algorithms
because both primal and dual variables are updated at each and every iteration.
Convergence properties of these algorithms are established or restated when
they already exist. Extensive numerical results in comparison with several
state-of-the-art algorithms are given to demonstrate that the proposed
algorithms are efficient, stable and robust. Moreover, we present numerical
results to emphasize two practically important but perhaps overlooked points.
One point is that algorithm speed should always be evaluated relative to
appropriate solution accuracy; another is that whenever erroneous measurements
possibly exist, the -norm fidelity should be the fidelity of choice in
compressive sensing
Capturing Near Earth Objects
Recently, Near Earth Objects (NEOs) have been attracting great attention, and
thousands of NEOs have been found to date. This paper examines the NEOs'
orbital dynamics using the framework of an accurate solar system model and a
Sun-Earth-NEO three-body system when the NEOs are close to Earth to search for
NEOs with low-energy orbits. It is possible for such an NEO to be temporarily
captured by Earth; its orbit would thereby be changed and it would become an
Earth-orbiting object after a small increase in its velocity. From the point of
view of the Sun-Earth-NEO restricted three-body system, it is possible for an
NEO whose Jacobian constant is slightly lower than C1 and higher than C3 to be
temporarily captured by Earth. When such an NEO approaches Earth, it is
possible to change its orbit energy to close up the zero velocity surface of
the three-body system at point L1 and make the NEO become a small satellite of
the Earth. Some such NEOs were found; the best example only required a 410m/s
increase in velocity.Comment: 22 pages, 6 figures, accepted for publication in Research in
Astronomy and Astrophysics (Chinese Journal of Astronomy and Astrophysics
A general inertial proximal point method for mixed variational inequality problem
In this paper, we first propose a general inertial proximal point method for
the mixed variational inequality (VI) problem. Based on our knowledge, without
stronger assumptions, convergence rate result is not known in the literature
for inertial type proximal point methods. Under certain conditions, we are able
to establish the global convergence and a convergence rate result
(under certain measure) of the proposed general inertial proximal point method.
We then show that the linearized alternating direction method of multipliers
(ADMM) for separable convex optimization with linear constraints is an
application of a general proximal point method, provided that the algorithmic
parameters are properly chosen. As byproducts of this finding, we establish
global convergence and convergence rate results of the linearized ADMM
in both ergodic and nonergodic sense. In particular, by applying the proposed
inertial proximal point method for mixed VI to linearly constrained separable
convex optimization, we obtain an inertial version of the linearized ADMM for
which the global convergence is guaranteed. We also demonstrate the effect of
the inertial extrapolation step via experimental results on the compressive
principal component pursuit problem.Comment: 21 pages, two figures, 4 table
EOS: Automatic In-vivo Evolution of Kernel Policies for Better Performance
Today's monolithic kernels often implement a small, fixed set of policies
such as disk I/O scheduling policies, while exposing many parameters to let
users select a policy or adjust the specific setting of the policy. Ideally,
the parameters exposed should be flexible enough for users to tune for good
performance, but in practice, users lack domain knowledge of the parameters and
are often stuck with bad, default parameter settings.
We present EOS, a system that bridges the knowledge gap between kernel
developers and users by automatically evolving the policies and parameters in
vivo on users' real, production workloads. It provides a simple policy
specification API for kernel developers to programmatically describe how the
policies and parameters should be tuned, a policy cache to make in-vivo tuning
easy and fast by memorizing good parameter settings for past workloads, and a
hierarchical search engine to effectively search the parameter space.
Evaluation of EOS on four main Linux subsystems shows that it is easy to use
and effectively improves each subsystem's performance.Comment: 14 pages, technique repor
Optimal Bidding Algorithms Against Cheating in Multiple-Object Auctions
This paper studies some basic problems in a multiple-object auction model
using methodologies from theoretical computer science. We are especially
concerned with situations where an adversary bidder knows the bidding
algorithms of all the other bidders. In the two-bidder case, we derive an
optimal randomized bidding algorithm, by which the disadvantaged bidder can
procure at least half of the auction objects despite the adversary's a priori
knowledge of his algorithm. In the general -bidder case, if the number of
objects is a multiple of , an optimal randomized bidding algorithm is found.
If the disadvantaged bidders employ that same algorithm, each of them can
obtain at least of the objects regardless of the bidding algorithm the
adversary uses. These two algorithms are based on closed-form solutions to
certain multivariate probability distributions. In situations where a
closed-form solution cannot be obtained, we study a restricted class of bidding
algorithms as an approximation to desired optimal algorithms
Inertial primal-dual algorithms for structured convex optimization
The primal-dual algorithm recently proposed by Chambolle & Pock (abbreviated
as CPA) for structured convex optimization is very efficient and popular. It
was shown by Chambolle & Pock in \cite{CP11} and also by Shefi & Teboulle in
\cite{ST14} that CPA and variants are closely related to preconditioned
versions of the popular alternating direction method of multipliers
(abbreviated as ADM). In this paper, we further clarify this connection and
show that CPAs generate exactly the same sequence of points with the so-called
linearized ADM (abbreviated as LADM) applied to either the primal problem or
its Lagrangian dual, depending on different updating orders of the primal and
the dual variables in CPAs, as long as the initial points for the LADM are
properly chosen. The dependence on initial points for LADM can be relaxed by
focusing on cyclically equivalent forms of the algorithms. Furthermore, by
utilizing the fact that CPAs are applications of a general weighted proximal
point method to the mixed variational inequality formulation of the KKT system,
where the weighting matrix is positive definite under a parameter condition, we
are able to propose and analyze inertial variants of CPAs. Under certain
conditions, global point-convergence, nonasymptotic and asymptotic
convergence rate of the proposed inertial CPAs can be guaranteed,
where denotes the iteration index. Finally, we demonstrate the profits
gained by introducing the inertial extrapolation step via experimental results
on compressive image reconstruction based on total variation minimization.Comment: 23 pages, 4 figures, 3 table
Photolysis of n-butyl nitrite and isoamyl nitrite at 355 nm: A time-resolved Fourier transform infrared emission spectroscopy and ab initio study
We report on the photodissociation dynamics study of n-butyl nitrite
(n-C_4H_9ONO) and isoamyl nitrite ((CH_3)_2C_3H_5ONO) by means of time-resolved
Fourier transform infrared (TR-FTIR) emission spectroscopy. The obtained
TR-FTIR emission spectra of the nascent NO fragments produced in the 355-nm
laser photolysis of the two alkyl nitrite species showed an almost identical
rotational temperature and vibrational distributions of NO. In addition, a
close resemblance between the two species was also found in the measured
temporal profiles of the IR emission of NO and the recorded UV absorption
spectra. The experimental results are consistent with our ab initio
calculations using the time-dependent density functional theory at the
B3LYP/6-311G(d,p) level, which indicate that the substitution of one of the two
{gamma}-H atoms in n-C_4H_9ONO with a methyl group to form (CH_3)_2C_3H_5ONO
has only a minor effect on the photodissociation dynamics of the two molecules.Comment: 25 pages, 7 figures, 3 tables; submitted to JC
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