425,344 research outputs found
Speeding up SOR Solvers for Constraint-based GUIs with a Warm-Start Strategy
Many computer programs have graphical user interfaces (GUIs), which need good
layout to make efficient use of the available screen real estate. Most GUIs do
not have a fixed layout, but are resizable and able to adapt themselves.
Constraints are a powerful tool for specifying adaptable GUI layouts: they are
used to specify a layout in a general form, and a constraint solver is used to
find a satisfying concrete layout, e.g.\ for a specific GUI size. The
constraint solver has to calculate a new layout every time a GUI is resized or
changed, so it needs to be efficient to ensure a good user experience. One
approach for constraint solvers is based on the Gauss-Seidel algorithm and
successive over-relaxation (SOR).
Our observation is that a solution after resizing or changing is similar in
structure to a previous solution. Thus, our hypothesis is that we can increase
the computational performance of an SOR-based constraint solver if we reuse the
solution of a previous layout to warm-start the solving of a new layout. In
this paper we report on experiments to test this hypothesis experimentally for
three common use cases: big-step resizing, small-step resizing and constraint
change. In our experiments, we measured the solving time for randomly generated
GUI layout specifications of various sizes. For all three cases we found that
the performance is improved if an existing solution is used as a starting
solution for a new layout
A nonmonotone GRASP
A greedy randomized adaptive search procedure (GRASP) is an itera-
tive multistart metaheuristic for difficult combinatorial optimization problems. Each
GRASP iteration consists of two phases: a construction phase, in which a feasible
solution is produced, and a local search phase, in which a local optimum in the
neighborhood of the constructed solution is sought. Repeated applications of the con-
struction procedure yields different starting solutions for the local search and the
best overall solution is kept as the result. The GRASP local search applies iterative
improvement until a locally optimal solution is found. During this phase, starting from
the current solution an improving neighbor solution is accepted and considered as the
new current solution. In this paper, we propose a variant of the GRASP framework that
uses a new ânonmonotoneâ strategy to explore the neighborhood of the current solu-
tion. We formally state the convergence of the nonmonotone local search to a locally
optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP
on three classical hard combinatorial optimization problems: the maximum cut prob-
lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and
the quadratic assignment problem (QAP)
Feedback Generation for Performance Problems in Introductory Programming Assignments
Providing feedback on programming assignments manually is a tedious, error
prone, and time-consuming task. In this paper, we motivate and address the
problem of generating feedback on performance aspects in introductory
programming assignments. We studied a large number of functionally correct
student solutions to introductory programming assignments and observed: (1)
There are different algorithmic strategies, with varying levels of efficiency,
for solving a given problem. These different strategies merit different
feedback. (2) The same algorithmic strategy can be implemented in countless
different ways, which are not relevant for reporting feedback on the student
program.
We propose a light-weight programming language extension that allows a
teacher to define an algorithmic strategy by specifying certain key values that
should occur during the execution of an implementation. We describe a dynamic
analysis based approach to test whether a student's program matches a teacher's
specification. Our experimental results illustrate the effectiveness of both
our specification language and our dynamic analysis. On one of our benchmarks
consisting of 2316 functionally correct implementations to 3 programming
problems, we identified 16 strategies that we were able to describe using our
specification language (in 95 minutes after inspecting 66, i.e., around 3%,
implementations). Our dynamic analysis correctly matched each implementation
with its corresponding specification, thereby automatically producing the
intended feedback.Comment: Tech report/extended version of FSE 2014 pape
Directional clustering through matrix factorization
This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction. This directional distance measure arises in several applications, including document classification and human brain imaging. Using ideas from the field of constrained low-rank matrix factorization and sparse approximation, a novel approach is presented that differs from classical clustering methods, such as seminonnegative matrix factorization, K-EVD, or k-means clustering, yet combines some aspects of all these. As in nonnegative matrix factorization and K-EVD, the matrix decomposition is iteratively refined to optimize a data fidelity term; however, no positivity constraint is enforced directly nor do we need to explicitly compute eigenvectors. As in k-means and K-EVD, each optimization step is followed by a hard cluster assignment. This leads to an efficient algorithm that is shown here to outperform common competitors in terms of clustering performance and/or computation speed. In addition to a detailed theoretical analysis of some of the algorithm's main properties, the approach is empirically evaluated on a range of toy problems, several standard text clustering data sets, and a high-dimensional problem in brain imaging, where functional magnetic resonance imaging data are used to partition the human cerebral cortex into distinct functional regions
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
Macrostate Data Clustering
We develop an effective nonhierarchical data clustering method using an
analogy to the dynamic coarse graining of a stochastic system. Analyzing the
eigensystem of an interitem transition matrix identifies fuzzy clusters
corresponding to the metastable macroscopic states (macrostates) of a diffusive
system. A "minimum uncertainty criterion" determines the linear transformation
from eigenvectors to cluster-defining window functions. Eigenspectrum gap and
cluster certainty conditions identify the proper number of clusters. The
physically motivated fuzzy representation and associated uncertainty analysis
distinguishes macrostate clustering from spectral partitioning methods.
Macrostate data clustering solves a variety of test cases that challenge other
methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral
graph theory, dynamic eigenvectors, machine learning, macrostates,
classificatio
- âŠ