57 research outputs found
Sequential Convex Programming Methods for A Class of Structured Nonlinear Programming
In this paper we study a broad class of structured nonlinear programming
(SNLP) problems. In particular, we first establish the first-order optimality
conditions for them. Then we propose sequential convex programming (SCP)
methods for solving them in which each iteration is obtained by solving a
convex programming problem exactly or inexactly. Under some suitable
assumptions, we establish that any accumulation point of the sequence generated
by the methods is a KKT point of the SNLP problems. In addition, we propose a
variant of the exact SCP method for SNLP in which nonmonotone scheme and
"local" Lipschitz constants of the associated functions are used. And a similar
convergence result as mentioned above is established.Comment: This paper has been withdrawn by the author due to major revision and
correction
Adaptive First-Order Methods for General Sparse Inverse Covariance Selection
In this paper, we consider estimating sparse inverse covariance of a Gaussian
graphical model whose conditional independence is assumed to be partially
known. Similarly as in [5], we formulate it as an -norm penalized maximum
likelihood estimation problem. Further, we propose an algorithm framework, and
develop two first-order methods, that is, the adaptive spectral projected
gradient (ASPG) method and the adaptive Nesterov's smooth (ANS) method, for
solving this estimation problem. Finally, we compare the performance of these
two methods on a set of randomly generated instances. Our computational results
demonstrate that both methods are able to solve problems of size at least a
thousand and number of constraints of nearly a half million within a reasonable
amount of time, and the ASPG method generally outperforms the ANS method.Comment: 19 pages, 1 figur
An Augmented Lagrangian Approach for Sparse Principal Component Analysis
Principal component analysis (PCA) is a widely used technique for data
analysis and dimension reduction with numerous applications in science and
engineering. However, the standard PCA suffers from the fact that the principal
components (PCs) are usually linear combinations of all the original variables,
and it is thus often difficult to interpret the PCs. To alleviate this
drawback, various sparse PCA approaches were proposed in literature [15, 6, 17,
28, 8, 25, 18, 7, 16]. Despite success in achieving sparsity, some important
properties enjoyed by the standard PCA are lost in these methods such as
uncorrelation of PCs and orthogonality of loading vectors. Also, the total
explained variance that they attempt to maximize can be too optimistic. In this
paper we propose a new formulation for sparse PCA, aiming at finding sparse and
nearly uncorrelated PCs with orthogonal loading vectors while explaining as
much of the total variance as possible. We also develop a novel augmented
Lagrangian method for solving a class of nonsmooth constrained optimization
problems, which is well suited for our formulation of sparse PCA. We show that
it converges to a feasible point, and moreover under some regularity
assumptions, it converges to a stationary point. Additionally, we propose two
nonmonotone gradient methods for solving the augmented Lagrangian subproblems,
and establish their global and local convergence. Finally, we compare our
sparse PCA approach with several existing methods on synthetic, random, and
real data, respectively. The computational results demonstrate that the sparse
PCs produced by our approach substantially outperform those by other methods in
terms of total explained variance, correlation of PCs, and orthogonality of
loading vectors.Comment: 42 page
Schatten- Quasi-Norm Regularized Matrix Optimization via Iterative Reweighted Singular Value Minimization
In this paper we study general Schatten- quasi-norm (SPQN) regularized
matrix minimization problems. In particular, we first introduce a class of
first-order stationary points for them, and show that the first-order
stationary points introduced in [11] for an SPQN regularized
minimization problem are equivalent to those of an SPQN regularized
minimization reformulation. We also show that any local minimizer of the SPQN
regularized matrix minimization problems must be a first-order stationary
point. Moreover, we derive lower bounds for nonzero singular values of the
first-order stationary points and hence also of the local minimizers of the
SPQN regularized matrix minimization problems. The iterative reweighted
singular value minimization (IRSVM) methods are then proposed to solve these
problems, whose subproblems are shown to have a closed-form solution. In
contrast to the analogous methods for the SPQN regularized
minimization problems, the convergence analysis of these methods is
significantly more challenging. We develop a novel approach to establishing the
convergence of these methods, which makes use of the expression of a specific
solution of their subproblems and avoids the intricate issue of finding the
explicit expression for the Clarke subdifferential of the objective of their
subproblems. In particular, we show that any accumulation point of the sequence
generated by the IRSVM methods is a first-order stationary point of the
problems. Our computational results demonstrate that the IRSVM methods
generally outperform some recently developed state-of-the-art methods in terms
of solution quality and/or speed.Comment: This paper has been withdrawn by the author due to major revision and
correction
Computing Optimal Experimental Designs via Interior Point Method
In this paper, we study optimal experimental design problems with a broad
class of smooth convex optimality criteria, including the classical A-, D- and
p th mean criterion. In particular, we propose an interior point (IP) method
for them and establish its global convergence. Furthermore, by exploiting the
structure of the Hessian matrix of the aforementioned optimality criteria, we
derive an explicit formula for computing its rank. Using this result, we then
show that the Newton direction arising in the IP method can be computed
efficiently via Sherman-Morrison-Woodbury formula when the size of the moment
matrix is small relative to the sample size. Finally, we compare our IP method
with the widely used multiplicative algorithm introduced by Silvey et al. [29].
The computational results show that the IP method generally outperforms the
multiplicative algorithm both in speed and solution quality
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