202 research outputs found
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
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
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
A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery
This paper concerns with a noisy structured low-rank matrix recovery problem
which can be modeled as a structured rank minimization problem. We reformulate
this problem as a mathematical program with a generalized complementarity
constraint (MPGCC), and show that its penalty version, yielded by moving the
generalized complementarity constraint to the objective, has the same global
optimal solution set as the MPGCC does whenever the penalty parameter is over a
threshold. Then, by solving the exact penalty problem in an alternating way, we
obtain a multi-stage convex relaxation approach. We provide theoretical
guarantees for our approach under a mild restricted eigenvalue condition, by
quantifying the reduction of the error and approximate rank bounds of the first
stage convex relaxation (which is exactly the nuclear norm relaxation) in the
subsequent stages and establishing the geometric convergence of the error
sequence in a statistical sense. Numerical experiments are conducted for some
structured low-rank matrix recovery examples to confirm our theoretical
findings.Comment: 29 pages, 2 figure
GEP-MSCRA for computing the group zero-norm regularized least squares estimator
This paper concerns with the group zero-norm regularized least squares
estimator which, in terms of the variational characterization of the zero-norm,
can be obtained from a mathematical program with equilibrium constraints
(MPEC). By developing the global exact penalty for the MPEC, this estimator is
shown to arise from an exact penalization problem that not only has a favorable
bilinear structure but also implies a recipe to deliver equivalent DC
estimators such as the SCAD and MCP estimators. We propose a multi-stage convex
relaxation approach (GEP-MSCRA) for computing this estimator, and under a
restricted strong convexity assumption on the design matrix, establish its
theoretical guarantees which include the decreasing of the error bounds for the
iterates to the true coefficient vector and the coincidence of the iterates
after finite steps with the oracle estimator. Finally, we implement the
GEP-MSCRA with the subproblems solved by a semismooth Newton augmented
Lagrangian method (ALM) and compare its performance with that of SLEP and
MALSAR, the solvers for the weighted -norm regularized estimator,
on synthetic group sparse regression problems and real multi-task learning
problems. Numerical comparison indicates that the GEP-MSCRA has significant
advantage in reducing error and achieving better sparsity than the SLEP and the
MALSAR do
Equivalent Lipschitz surrogates for zero-norm and rank optimization problems
This paper proposes a mechanism to produce equivalent Lipschitz surrogates
for zero-norm and rank optimization problems by means of the global exact
penalty for their equivalent mathematical programs with an equilibrium
constraint (MPECs). Specifically, we reformulate these combinatorial problems
as equivalent MPECs by the variational characterization of the zero-norm and
rank function, show that their penalized problems, yielded by moving the
equilibrium constraint into the objective, are the global exact penalization,
and obtain the equivalent Lipschitz surrogates by eliminating the dual variable
in the global exact penalty. These surrogates, including the popular SCAD
function in statistics, are also difference of two convex functions (D.C.) if
the function and constraint set involved in zero-norm and rank optimization
problems are convex. We illustrate an application by designing a multi-stage
convex relaxation approach to the rank plus zero-norm regularized minimization
problem
A proximal MM method for the zero-norm regularized PLQ composite optimization problem
This paper is concerned with a class of zero-norm regularized piecewise
linear-quadratic (PLQ) composite minimization problems, which covers the
zero-norm regularized -loss minimization problem as a special case. For
this class of nonconvex nonsmooth problems, we show that its equivalent MPEC
reformulation is partially calm on the set of global optima and make use of
this property to derive a family of equivalent DC surrogates. Then, we propose
a proximal majorization-minimization (MM) method, a convex relaxation approach
not in the DC algorithm framework, for solving one of the DC surrogates which
is a semiconvex PLQ minimization problem involving three nonsmooth terms. For
this method, we establish its global convergence and linear rate of
convergence, and under suitable conditions show that the limit of the generated
sequence is not only a local optimum but also a good critical point in a
statistical sense. Numerical experiments are conducted with synthetic and real
data for the proximal MM method with the subproblems solved by a dual
semismooth Newton method to confirm our theoretical findings, and numerical
comparisons with a convergent indefinite-proximal ADMM for the partially
smoothed DC surrogate verify its superiority in the quality of solutions and
computing time
Error bound of local minima and KL property of exponent 1/2 for squared F-norm regularized factorization
This paper is concerned with the squared F(robenius)-norm regularized
factorization form for noisy low-rank matrix recovery problems. Under a
suitable assumption on the restricted condition number of the Hessian matrix of
the loss function, we establish an error bound to the true matrix for those
local minima whose ranks are not more than the rank of the true matrix. Then,
for the least squares loss function, we achieve the KL property of exponent 1/2
for the F-norm regularized factorization function over its global minimum set
under a restricted strong convexity assumption. These theoretical findings are
also confirmed by applying an accelerated alternating minimization method to
the F-norm regularized factorization problem
KL property of exponent of -norm and DC regularized factorizations for low-rank matrix recovery
This paper is concerned with the factorization form of the rank regularized
loss minimization problem. To cater for the scenario in which only a coarse
estimation is available for the rank of the true matrix, an -norm
regularized term is added to the factored loss function to reduce the rank
adaptively; and account for the ambiguities in the factorization, a balanced
term is then introduced. For the least squares loss, under a restricted
condition number assumption on the sampling operator, we establish the KL
property of exponent of the nonsmooth factored composite function and its
equivalent DC reformulations in the set of their global minimizers. We also
confirm the theoretical findings by applying a proximal linearized alternating
minimization method to the regularized factorizations.Comment: 29 pages, 3 figure
A proximal dual semismooth Newton method for computing zero-norm penalized QR estimator
This paper is concerned with the computation of the high-dimensional
zero-norm penalized quantile regression estimator, defined as a global
minimizer of the zero-norm penalized check loss function. To seek a desirable
approximation to the estimator, we reformulate this NP-hard problem as an
equivalent augmented Lipschitz optimization problem, and exploit its coupled
structure to propose a multi-stage convex relaxation approach (MSCRA\_PPA),
each step of which solves inexactly a weighted -regularized check loss
minimization problem with a proximal dual semismooth Newton method. Under a
restricted strong convexity condition, we provide the theoretical guarantee for
the MSCRA\_PPA by establishing the error bound of each iterate to the true
estimator and the rate of linear convergence in a statistical sense. Numerical
comparisons on some synthetic and real data show that MSCRA\_PPA not only has
comparable even better estimation performance, but also requires much less CPU
time
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