2,276 research outputs found
Automatic Test Generation for Space
The European Space Agency (ESA) uses an engine to perform tests in the Ground
Segment infrastructure, specially the Operational Simulator. This engine uses
many different tools to ensure the development of regression testing
infrastructure and these tests perform black-box testing to the C++ simulator
implementation. VST (VisionSpace Technologies) is one of the companies that
provides these services to ESA and they need a tool to infer automatically
tests from the existing C++ code, instead of writing manually scripts to
perform tests. With this motivation in mind, this paper explores automatic
testing approaches and tools in order to propose a system that satisfies VST
needs
Updating constraint preconditioners for KKT systems in quadratic programming via low-rank corrections
This work focuses on the iterative solution of sequences of KKT linear
systems arising in interior point methods applied to large convex quadratic
programming problems. This task is the computational core of the interior point
procedure and an efficient preconditioning strategy is crucial for the
efficiency of the overall method. Constraint preconditioners are very effective
in this context; nevertheless, their computation may be very expensive for
large-scale problems, and resorting to approximations of them may be
convenient. Here we propose a procedure for building inexact constraint
preconditioners by updating a "seed" constraint preconditioner computed for a
KKT matrix at a previous interior point iteration. These updates are obtained
through low-rank corrections of the Schur complement of the (1,1) block of the
seed preconditioner. The updated preconditioners are analyzed both
theoretically and computationally. The results obtained show that our updating
procedure, coupled with an adaptive strategy for determining whether to
reinitialize or update the preconditioner, can enhance the performance of
interior point methods on large problems.Comment: 22 page
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Exploration of task mappings plays a crucial role in achieving high
performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms.
The problem of optimally mapping a set of tasks onto a set of given
heterogeneous processors for maximal throughput has been known, in general, to
be NP-complete. The problem is further exacerbated when multiple applications
(i.e., bigger task sets) and the communication between tasks are also
considered. Previous research has shown that Genetic Algorithms (GA) typically
are a good choice to solve this problem when the solution space is relatively
small. However, when the size of the problem space increases, classic genetic
algorithms still suffer from the problem of long evolution times. To address
this problem, this paper proposes a novel bias-elitist genetic algorithm that
is guided by domain-specific heuristics to speed up the evolution process.
Experimental results reveal that our proposed algorithm is able to handle large
scale task mapping problems and produces high-quality mapping solutions in only
a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st
Uniform Uncertainty Principle and signal recovery via Regularized Orthogonal Matching Pursuit
This paper seeks to bridge the two major algorithmic approaches to sparse
signal recovery from an incomplete set of linear measurements --
L_1-minimization methods and iterative methods (Matching Pursuits). We find a
simple regularized version of the Orthogonal Matching Pursuit (ROMP) which has
advantages of both approaches: the speed and transparency of OMP and the strong
uniform guarantees of the L_1-minimization. Our algorithm ROMP reconstructs a
sparse signal in a number of iterations linear in the sparsity (in practice
even logarithmic), and the reconstruction is exact provided the linear
measurements satisfy the Uniform Uncertainty Principle.Comment: This is the final version of the paper, including referee suggestion
Having Fun in Learning Formal Specifications
There are many benefits in providing formal specifications for our software.
However, teaching students to do this is not always easy as courses on formal
methods are often experienced as dry by students. This paper presents a game
called FormalZ that teachers can use to introduce some variation in their
class. Students can have some fun in playing the game and, while doing so, also
learn the basics of writing formal specifications in the form of pre- and
post-conditions. Unlike existing software engineering themed education games
such as Pex and Code Defenders, FormalZ takes the deep gamification approach
where playing gets a more central role in order to generate more engagement.
This short paper presents our work in progress: the first implementation of
FormalZ along with the result of a preliminary users' evaluation. This
implementation is functionally complete and tested, but the polishing of its
user interface is still future work
Submodular Stochastic Probing on Matroids
In a stochastic probing problem we are given a universe , where each
element is active independently with probability , and only a
probe of e can tell us whether it is active or not. On this universe we execute
a process that one by one probes elements --- if a probed element is active,
then we have to include it in the solution, which we gradually construct.
Throughout the process we need to obey inner constraints on the set of elements
taken into the solution, and outer constraints on the set of all probed
elements. This abstract model was presented by Gupta and Nagarajan (IPCO '13),
and provides a unified view of a number of problems. Thus far, all the results
falling under this general framework pertain mainly to the case in which we are
maximizing a linear objective function of the successfully probed elements. In
this paper we generalize the stochastic probing problem by considering a
monotone submodular objective function. We give a -approximation algorithm for the case in which we are given
matroids as inner constraints and matroids as outer constraints.
Additionally, we obtain an improved -approximation
algorithm for linear objective functions
The Euclidean distance degree of an algebraic variety
The nearest point map of a real algebraic variety with respect to Euclidean
distance is an algebraic function. For instance, for varieties of low rank
matrices, the Eckart-Young Theorem states that this map is given by the
singular value decomposition. This article develops a theory of such nearest
point maps from the perspective of computational algebraic geometry. The
Euclidean distance degree of a variety is the number of critical points of the
squared distance to a generic point outside the variety. Focusing on varieties
seen in applications, we present numerous tools for exact computations.Comment: to appear in Foundations of Computational Mathematic
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