826 research outputs found
Iteration Complexity Analysis of Block Coordinate Descent Methods
In this paper, we provide a unified iteration complexity analysis for a
family of general block coordinate descent (BCD) methods, covering popular
methods such as the block coordinate gradient descent (BCGD) and the block
coordinate proximal gradient (BCPG), under various different coordinate update
rules. We unify these algorithms under the so-called Block Successive
Upper-bound Minimization (BSUM) framework, and show that for a broad class of
multi-block nonsmooth convex problems, all algorithms covered by the BSUM
framework achieve a global sublinear iteration complexity of , where r
is the iteration index. Moreover, for the case of block coordinate minimization
(BCM) where each block is minimized exactly, we establish the sublinear
convergence rate of without per block strong convexity assumption.
Further, we show that when there are only two blocks of variables, a special
BSUM algorithm with Gauss-Seidel rule can be accelerated to achieve an improved
rate of
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization
Consider the problem of minimizing the sum of a smooth (possibly non-convex)
and a convex (possibly nonsmooth) function involving a large number of
variables. A popular approach to solve this problem is the block coordinate
descent (BCD) method whereby at each iteration only one variable block is
updated while the remaining variables are held fixed. With the recent advances
in the developments of the multi-core parallel processing technology, it is
desirable to parallelize the BCD method by allowing multiple blocks to be
updated simultaneously at each iteration of the algorithm. In this work, we
propose an inexact parallel BCD approach where at each iteration, a subset of
the variables is updated in parallel by minimizing convex approximations of the
original objective function. We investigate the convergence of this parallel
BCD method for both randomized and cyclic variable selection rules. We analyze
the asymptotic and non-asymptotic convergence behavior of the algorithm for
both convex and non-convex objective functions. The numerical experiments
suggest that for a special case of Lasso minimization problem, the cyclic block
selection rule can outperform the randomized rule
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
In this paper, we propose an inexact block coordinate descent algorithm for
large-scale nonsmooth nonconvex optimization problems. At each iteration, a
particular block variable is selected and updated by inexactly solving the
original optimization problem with respect to that block variable. More
precisely, a local approximation of the original optimization problem is
solved. The proposed algorithm has several attractive features, namely, i) high
flexibility, as the approximation function only needs to be strictly convex and
it does not have to be a global upper bound of the original function; ii) fast
convergence, as the approximation function can be designed to exploit the
problem structure at hand and the stepsize is calculated by the line search;
iii) low complexity, as the approximation subproblems are much easier to solve
and the line search scheme is carried out over a properly constructed
differentiable function; iv) guaranteed convergence of a subsequence to a
stationary point, even when the objective function does not have a Lipschitz
continuous gradient. Interestingly, when the approximation subproblem is solved
by a descent algorithm, convergence of a subsequence to a stationary point is
still guaranteed even if the approximation subproblem is solved inexactly by
terminating the descent algorithm after a finite number of iterations. These
features make the proposed algorithm suitable for large-scale problems where
the dimension exceeds the memory and/or the processing capability of the
existing hardware. These features are also illustrated by several applications
in signal processing and machine learning, for instance, network anomaly
detection and phase retrieval
Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations
In this paper, we propose a successive convex approximation framework for
sparse optimization where the nonsmooth regularization function in the
objective function is nonconvex and it can be written as the difference of two
convex functions. The proposed framework is based on a nontrivial combination
of the majorization-minimization framework and the successive convex
approximation framework proposed in literature for a convex regularization
function. The proposed framework has several attractive features, namely, i)
flexibility, as different choices of the approximate function lead to different
type of algorithms; ii) fast convergence, as the problem structure can be
better exploited by a proper choice of the approximate function and the
stepsize is calculated by the line search; iii) low complexity, as the
approximate function is convex and the line search scheme is carried out over a
differentiable function; iv) guaranteed convergence to a stationary point. We
demonstrate these features by two example applications in subspace learning,
namely, the network anomaly detection problem and the sparse subspace
clustering problem. Customizing the proposed framework by adopting the
best-response type approximation, we obtain soft-thresholding with exact line
search algorithms for which all elements of the unknown parameter are updated
in parallel according to closed-form expressions. The attractive features of
the proposed algorithms are illustrated numerically.Comment: submitted to IEEE Journal of Selected Topics in Signal Processing,
special issue in Robust Subspace Learnin
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization
We propose a decomposition framework for the parallel optimization of the sum
of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly
nonseparable), convex one. The latter term is usually employed to enforce
structure in the solution, typically sparsity. The main contribution of this
work is a novel \emph{parallel, hybrid random/deterministic} decomposition
scheme wherein, at each iteration, a subset of (block) variables is updated at
the same time by minimizing local convex approximations of the original
nonconvex function. To tackle with huge-scale problems, the (block) variables
to be updated are chosen according to a \emph{mixed random and deterministic}
procedure, which captures the advantages of both pure deterministic and random
update-based schemes. Almost sure convergence of the proposed scheme is
established. Numerical results show that on huge-scale problems the proposed
hybrid random/deterministic algorithm outperforms both random and deterministic
schemes.Comment: The order of the authors is alphabetica
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