24,826 research outputs found
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
The affine rank minimization problem consists of finding a matrix of minimum
rank that satisfies a given system of linear equality constraints. Such
problems have appeared in the literature of a diverse set of fields including
system identification and control, Euclidean embedding, and collaborative
filtering. Although specific instances can often be solved with specialized
algorithms, the general affine rank minimization problem is NP-hard. In this
paper, we show that if a certain restricted isometry property holds for the
linear transformation defining the constraints, the minimum rank solution can
be recovered by solving a convex optimization problem, namely the minimization
of the nuclear norm over the given affine space. We present several random
ensembles of equations where the restricted isometry property holds with
overwhelming probability. The techniques used in our analysis have strong
parallels in the compressed sensing framework. We discuss how affine rank
minimization generalizes this pre-existing concept and outline a dictionary
relating concepts from cardinality minimization to those of rank minimization
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Data compression has the potential to significantly improve the computation
offloading performance in hierarchical fog-cloud systems. However, it remains
unknown how to optimally determine the compression ratio jointly with the
computation offloading decisions and the resource allocation. This joint
optimization problem is studied in the current paper where we aim to minimize
the maximum weighted energy and service delay cost (WEDC) of all users. First,
we consider a scenario where data compression is performed only at the mobile
users. We prove that the optimal offloading decisions have a threshold
structure. Moreover, a novel three-step approach employing convexification
techniques is developed to optimize the compression ratios and the resource
allocation. Then, we address the more general design where data compression is
performed at both the mobile users and the fog server. We propose three
efficient algorithms to overcome the strong coupling between the offloading
decisions and resource allocation. We show that the proposed optimal algorithm
for data compression at only the mobile users can reduce the WEDC by a few
hundred percent compared to computation offloading strategies that do not
leverage data compression or use sub-optimal optimization approaches. Besides,
the proposed algorithms for additional data compression at the fog server can
further reduce the WEDC
Solution of partial differential equations on vector and parallel computers
The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed
Total variation on a tree
We consider the problem of minimizing the continuous valued total variation
subject to different unary terms on trees and propose fast direct algorithms
based on dynamic programming to solve these problems. We treat both the convex
and the non-convex case and derive worst case complexities that are equal or
better than existing methods. We show applications to total variation based 2D
image processing and computer vision problems based on a Lagrangian
decomposition approach. The resulting algorithms are very efficient, offer a
high degree of parallelism and come along with memory requirements which are
only in the order of the number of image pixels.Comment: accepted to SIAM Journal on Imaging Sciences (SIIMS
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
Nonlinear support vector machines through iterative majorization and I-splines
To minimize the primal support vector machine (SVM) problem, wepropose to use iterative majorization. To do so, we propose to use it-erative majorization. To allow for nonlinearity of the predictors, we use(non)monotone spline transformations. An advantage over the usual ker-nel approach in the dual problem is that the variables can be easily inter-preted. We illustrate this with an example from the literature.iterative majorization;support vector machines;I-Splines
- …