225 research outputs found
A PAM method for computing Wasserstein barycenter with unknown supports in D2-clustering
A Wasserstein barycenter is the centroid of a collection of discrete
probability distributions that minimizes the average of the
-Wasserstein distance. This paper concerns with the computation of a
Wasserstein barycenter under the case where the support points are not
pre-specified, which is known to be a severe bottleneck in the D2-clustering
due to the large-scale and nonconvexity. We develop a proximal alternating
minimization (PAM) method for computing an approximate Wasserstein barycenter,
and provide its global convergence analysis. This method can achieve a good
accuracy at a reduced computational cost when the unknown support points of the
barycenter have low cardinality. Numerical comparisons with the existing
representative method on synthetic and real data show that our method can yield
a little better objective values within much less computing time, and the
computed approximate barycenter renders a better role in the D2-clustering
Inexact indefinite proximal ADMMs for 2-block separable convex programs and applications to 4-block DNNSDPs
This paper is concerned with two-block separable convex minimization problems
with linear constraints, for which it is either impossible or too expensive to
obtain the exact solutions of the subproblems involved in the proximal ADMM
(alternating direction method of multipliers). Such structured convex
minimization problems often arise from the two-block regroup settlement of
three or four-block separable convex optimization problems with linear
constraints, or from the constrained total-variation superresolution image
reconstruction problems in image processing. For them, we propose an inexact
indefinite proximal ADMM of step-size with
two easily implementable inexactness criteria to control the solution accuracy
of subproblems, and establish the convergence under a mild assumption on
indefinite proximal terms. We apply the proposed inexact indefinite proximal
ADMMs to the three or four-block separable convex minimization problems with
linear constraints, which are from the duality of the important class of doubly
nonnegative semidefinite programming (DNNSDP) problems with many linear
equality and/or inequality constraints. Numerical results indicate that the
inexact indefinite proximal ADMM with the absolute error criterion has a
comparable performance with the directly extended multi-block ADMM of step-size
without convergence guarantee, whether in terms of the number of
iterations or the computation time.Comment: 34 pages, 3 figure
Locally upper Lipschitz of the perturbed KKT system of Ky Fan -norm matrix conic optimization problems
This note is concerned with the nonlinear Ky Fan -norm matrix conic
optimization problems, which include the nuclear norm regularized minimization
problem as a special case. For this class of nonpolyhedral matrix conic
optimization problems, under the assumption that a stationary solution
satisfies the second-order sufficient condition and the associated Lagrange
multiplier satisfies the strict Robinson's CQ, we show that two classes of
perturbed KKT systems are locally upper Lipschitz at the origin, which implies
a local error bound for the distance from any point in a neighborhood of the
corresponding KKT point to the whole set of KKT points.Comment: twenty-seven page
Linear convergence of the generalized PPA and several splitting methods for the composite inclusion problem
For the inclusion problem involving two maximal monotone operators, under the
metric subregularity of the composite operator, we derive the linear
convergence of the generalized proximal point algorithm and several splitting
algorithms, which include the over-relaxed forward-backward splitting
algorithm, the generalized Douglas-Rachford splitting algorithm and Davis'
three-operator splitting algorithm. To the best of our knowledge, this linear
convergence condition is weaker than the existing ones that almost all require
the strong monotonicity of the composite operator. Withal, we give some
sufficient conditions to ensure the metric subregularity of the composite
operator. At last, the preliminary numerical performances on some toy examples
support the theoretical results
Error bounds for rank constrained optimization problems and applications
This paper is concerned with the rank constrained optimization problem whose
feasible set is the intersection of the rank constraint set
and a
closed convex set . We establish the local (global) Lipschitzian type
error bounds for estimating the distance from any
() to the feasible set and the solution set, respectively,
under the calmness of a multifunction associated to the feasible set at the
origin, which is specially satisfied by three classes of common rank
constrained optimization problems. As an application of the local Lipschitzian
type error bounds, we show that the penalty problem yielded by moving the rank
constraint into the objective is exact in the sense that its global optimal
solution set coincides with that of the original problem when the penalty
parameter is over a certain threshold. This particularly offers an affirmative
answer to the open question whether the penalty problem (32) in (Gao and Sun,
2010) is exact or not. As another application, we derive the error bounds of
the iterates generated by a multi-stage convex relaxation approach to those
three classes of rank constrained problems and show that the bounds are
nonincreasing as the number of stages increases
A corrected semi-proximal ADMM for multi-block convex optimization and its application to DNN-SDPs
In this paper we propose a corrected semi-proximal ADMM (alternating
direction method of multipliers) for the general -block convex
optimization problems with linear constraints, aiming to resolve the dilemma
that almost all the existing modified versions of the directly extended ADMM,
although with convergent guarantee, often perform substantially worse than the
directly extended ADMM itself with no convergent guarantee. Specifically, in
each iteration, we use the multi-block semi-proximal ADMM with step-size at
least as the prediction step to generate a good prediction point, and then
make correction as small as possible for the middle blocks of the
prediction point. Among others, the step-size of the multi-block semi-proximal
ADMM is adaptively determined by the infeasibility ratio made up by the current
semi-proximal ADMM step for the one yielded by the last correction step. For
the proposed corrected semi-proximal ADMM, we establish the global convergence
results under a mild assumption, and apply it to the important class of doubly
nonnegative semidefinite programming (DNN-SDP) problems with many linear
equality and/or inequality constraints. Our extensive numerical tests show that
the corrected semi-proximal ADMM is superior to the directly extended ADMM with
step-size and the multi-block ADMM with Gaussian back substitution
\cite{HTY12,HY13}. It requires the least number of iterations for test
instances within the comparable computing time with that of the directly
extended ADMM, and for about tested problems, its number of iterations
is only that of the multi-block ADMM with Gaussian back substitution
\cite{HTY12,HY13}.Comment: 37 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1404.5378 by other author
Calibrated zero-norm regularized LS estimator for high-dimensional error-in-variables regression
This paper is concerned with high-dimensional error-in-variables regression
that aims at identifying a small number of important interpretable factors for
corrupted data from many applications where measurement errors or missing data
can not be ignored. Motivated by CoCoLasso due to Datta and Zou \cite{Datta16}
and the advantage of the zero-norm regularized LS estimator over Lasso for
clean data, we propose a calibrated zero-norm regularized LS (CaZnRLS)
estimator by constructing a calibrated least squares loss with a positive
definite projection of an unbiased surrogate for the covariance matrix of
covariates, and use the multi-stage convex relaxation approach to compute the
CaZnRLS estimator. Under a restricted eigenvalue condition on the true matrix
of covariates, we derive the -error bound of every iterate and
establish the decreasing of the error bound sequence, and the sign consistency
of the iterates after finite steps. The statistical guarantees are also
provided for the CaZnRLS estimator under two types of measurement errors.
Numerical comparisons with CoCoLasso and NCL (the nonconvex Lasso proposed by
Poh and Wainwright \cite{Loh11}) demonstrate that CaZnRLS not only has the
comparable or even better relative RSME but also has the least number of
incorrect predictors identified
Antitrace maps and light transmission coefficients for a generalized Fibonacci multilayers
By using antitrace map method, we investigate the light transmission for a
generalized Fibonacci multilayers. Analytical results are obtained for
transmission coefficients in some special cases. We find that the transmission
coefficients possess two-cycle property or six-cycle property. The cycle
properties of the trace and antitrace are also obtained.Comment: 8 pages, no figure
Computation of graphical derivatives of normal cone maps to a class of conic constraint sets
This paper concerns with the graphical derivative of the normals to the conic
constraint , where is a twice
continuously differentiable mapping and is a nonempty
closed convex set assumed to be -cone reducible. Such a generalized
derivative plays a crucial role in characterizing isolated calmness of the
solution maps to generalized equations whose multivalued parts are modeled via
the normals to the nonconvex set . The main contribution of
this paper is to provide an exact characterization for the graphical derivative
of the normals to this class of nonconvex conic constraints under an assumption
without requiring the nondegeneracy of the reference point as the papers
\cite{Gfrerer17,Mordu15,Mordu151} do.Comment: 28page
KL property of exponent for zero-norm composite quadratic functions
This paper is concerned with a class of zero-norm regularized and constrained
composite quadratic optimization problems, which has important applications in
the fields such as sparse eigenvalue problems, sparse portfolio problems, and
nonnegative matrix factorizations. For this class of nonconvex and nonsmooth
problems, we establish the KL property of exponent 1/2 of its objective
function under a suitable assumption, and provide some examples to illustrate
that the assumption holds
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